List of Barker codes.
\r\n\tNearly 25% - 30% of the world population is affected by neurological diseases exerting a hard financial strain on the healthcare system. The costs are estimated at around $800 billion annualy, expected to exponentially increase as the elders, at high risk of debilitating neurological diseases, will double by 2050. A varied spectrum of neuroprotective strategies has been suggested, including combined antioxidative-anti-inflammatory treatments, ozone autohemotherapy, hypothermia, cell therapy, the administration of neurotrophic factors, hemofiltration, and others. Distressingly, none of the currently available neuroprotective approaches has so far proven to prolong either life span or the cardinal symptoms of the patients suffering from brain injury. Last but not least, translational studies are still lacking.
\r\n\r\n\tThe book aims to revisit, discuss, and compile some promising current approaches in neuroprotection along with the current goals and prospects.
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De Robertis, School of Medicine (UBA), Argentina. Then he moved abroad to perform his postdoctoral studies at the University of California San Diego (UCSD-NCMIR) and the Karolinska Institute, Department of Neuroscience. Over an eight-year period, his research focused on synaptic organization, combining electron tomography, 3-D reconstruction, and correlative light and electron microscopy techniques. Upon his return to Argentina in 2006, he devoted to study the mechanisms involved in the pathophysiology of the perinatal asphyxia supported by his broad experience in electron microscopy. He has published 101 papers in recognized journals and has been invited assisting in a speaker capacity to several international conferences.",institutionString:"University of Buenos Aires",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Buenos Aires",institutionURL:null,country:{name:"Argentina"}}},coeditorTwo:{id:"168475",title:"Dr.",name:"Santiago Perez",middleName:null,surname:"Lloret",slug:"santiago-perez-lloret",fullName:"Santiago Perez Lloret",profilePictureURL:"https://mts.intechopen.com/storage/users/168475/images/system/168475.jpeg",biography:"Dr. Santiago Perez Lloret is a leading expert in neurophysiology and neuropharmacology with more 90 papers published in international medical journals (H-index= 29). He has been recently awarded Top 1% reviewer in neurosciences (Publons.com). After obtaining his MD and PhD, he pursued master courses in pharmacoepidemiology, clinical pharmacology and biostatistics at the Universities of Bordeaux and Paris. Dr. Perez Lloret is Assistant professor of Neurophysiology at the Medicine School of the Buenos Aires University and Associate Researcher at the Cardiology Research Institute, University of Buenos Aires, National Research Council. He is member of the International Parkinson’s Disease and Movement Disorder Society (MDS), where he is Co-editor of the Webpage and collaborates in several committees, including the Educational and the Evidence-based Medicine Committees.",institutionString:"University of Buenos Aires",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Buenos Aires",institutionURL:null,country:{name:"Argentina"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"69122",title:"Lifestyle Factors, Mitochondrial Dynamics, and Neuroprotection",slug:"lifestyle-factors-mitochondrial-dynamics-and-neuroprotection",totalDownloads:83,totalCrossrefCites:0,authors:[null]},{id:"69463",title:"Polyphenols as Potential Therapeutic Drugs in Neurodegeneration",slug:"polyphenols-as-potential-therapeutic-drugs-in-neurodegeneration",totalDownloads:36,totalCrossrefCites:0,authors:[null]},{id:"69376",title:"Trends in Neuroprotective Strategies after Spinal Cord Injury: State of the Art",slug:"trends-in-neuroprotective-strategies-after-spinal-cord-injury-state-of-the-art",totalDownloads:35,totalCrossrefCites:0,authors:[null]},{id:"70228",title:"Aptamers and Possible Effects on Neurodegeneration",slug:"aptamers-and-possible-effects-on-neurodegeneration",totalDownloads:10,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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Chan and Manoj Kumar Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/3794.jpg",editedByType:"Edited by",editors:[{id:"252210",title:"Dr.",name:"Felix",surname:"Chan",slug:"felix-chan",fullName:"Felix Chan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"58526",title:"Adaptive Coding, Modulation and Filtering of Radar Signals",doi:"10.5772/intechopen.71542",slug:"adaptive-coding-modulation-and-filtering-of-radar-signals",body:'Radar signal processing is essential in a radar receiver as it is required to enhance and detect received echo signals that are immersed in noise and clutter. The waveform of the transmitted signal plays a distinctive role in the performance of the radar receiver to distinguish valid echo signals from interfering random or hostile received signals. In addition to enhancing the signal-to-noise ratio (SNR) of received signals, receiver needs to enhance the signal-to-clutter ratio (SCR) so as to increase the radar detectability of targets inside the sever clutter. The performance of the radar is highly affected by the level of the interfering signals that share with the radar signal the same channel. It is affected by the unintentional interference from adjacent channels, environmental and industrial noise sources and the system noise that is generated within the elements of radar system itself.
In military applications, radar is affected as well as by intentional interference or jamming caused by others to deteriorate the ability of the radar to detect hostile targets. Increase in noise and interference lowers the signal-to-noise ratio and thus allows the false alarms rate (FAR) at the receiver output to increase. Wideband jamming signals which have already enough power may find the chance to totally saturate the receiver channel to the extent that the radar might become useless. One of the drawbacks of pulse radar is its relatively large receiver bandwidth, which adds more noise and interference. Such a large bandwidth is required to pass the narrow radar pulses with satisfactory distortion. However, filtering that reduces receiver bandwidth broadens the pulse and causes distortion, which, as well as noise and interference, deteriorate the range resolution and accuracy of the radar. This disadvantage of pulse signals makes continuous wave (CW) radar signals, which require a smaller bandwidth, more preferable within heavy jammed environments. A narrowband interfering signal affects both CW and pulse radars and does not need a large bandwidth to penetrate through the receiver; however, it needs higher power to contest with the desired radar signal.
In addition to noise and interference, the radar suffers from other types of signals which are known as clutter signals. Clutter is any unwanted object that reflects radar signals and will be shown on the radar display, together with the real targets. In air traffic control radar, both fixed and very slow moving objects are considered as clutter and need to be filtered out, so as to not reach the receiver output. There are many radar signal processing techniques that are used to reduce the clutter, such as constant false alarm rate techniques that serve in the reduction of both interference and clutter problems. Doppler processing techniques, such as the long-established moving target indication (MTI) and moving target detection (MTD), are also used to process cluttered radar signals.
Signal filtering means the processing of signals by altering their behaviour in the frequency domain, mostly to reject lower and higher frequency interfering signals. In general, in order to use filters to enhance SNR, bandpass filters (BPFs) process passband signals, while low-pass filters (LPFs) are used to process baseband signals, namely video signals. On the other hand, high-pass filters are used in Doppler processing to enhance demodulated video signals due to reflections that come back from moving objects. Figure 1 demonstrates the frequency response of a typical BPF.
Frequency response of a BPF.
From the spectral analysis of pulse radar signal, it could be noticed that most of the pulse energy is concentrated on the main lobe centred about the carrier frequency. North [1] suggests the reciprocal of the pulse width as the optimum value for the bandwidth of the LPF used to process radar signals in the presence of white noise. The signal-to-noise ratio at the input of the filter can be expressed as the signal power to the average thermal noise power:
where k is the Boltzmann’s constant that equals 1.38 × 10−23 JK−1, T the temperature in Kelvin and B the bandwidth of the system in Hz. By substituting B with the optimum bandwidth suggested by North, we get
where E is the pulse energy and No is the single-sided noise density in W/Hz. This signal-to-noise ratio which is directly proportional to the energy of the signal is sometimes known as the detectability factor [2], which is defined as the minimum SNR at the output of a filter that is matched to the signal. Generally, microwave circuits and transmission lines are still being used to realize filters in the radio frequency (RF) stages, while passive LC and crystal filters were used within the intermediate-frequency (IF) stages. Other passive elements, such as surface acoustic wave (SAW) devices, have been employed in many radar receivers as bandpass filters and for other purposes [3]. However, digital signal processing (DSP) is often applied in IF and baseband stages of the radar in order to accomplish the processing of IF and video signals. Nevertheless, DSP is gradually moving towards RF.
Due to the rapid growth in the fabrication of digital processors and analogue-to-digital converters (ADCs), digital filters today are widely utilized in the IF stages for bandpass filtering and other radar signal processing schemes. Both finite impulse response (FIR) and infinite impulse response (IIR) filters are used to filter IF and video radar signals [4]. For the FIR filter shown in Figure 2, the current sample of the output signal y(n) can be computed as the sum of the present sample of the input signal x(n) and the samples prior to it, multiplied by the coefficients of the filter bk as
where N is the number of taps or filter coefficients. N−1 is the filter order, which equals the number of delays marked as Z−1. FIR filters are stable for all values of coefficients, which makes them more suitable to be adaptive. Also, they can be designed to have a linear phase response, unlike IIR filters that have a nonlinear phase response and might be instable.
Structure of the FIR filter.
The design of an FIR filter involves finding the coefficients bk which are the samples of the filter impulse response h(n), which results in the frequency response that suits the required filter behaviour. An ideal LPF has an impulse response (kernel) that is proportional to the Sinc function, which extends to infinity. The convolution of any input signal with that impulse response shall produce perfect low-pass filtering. Nevertheless, the Sinc function has to be truncated in order to have an impulse response of a finite length. Because of truncation, the frequency response of the real filter will involve ripples as well as slower transition band, that occur because of the discontinuity that arises at the end of the truncated Sinc function. An increase of samples of h(n) will not eliminate these deficiencies because discontinuity is of a key effect. A number of windows are formulated in order to reduce the side lobes that occur because of rectangular windowing [5]. For instance, for a filter kernel of length N, the Hamming window is
The design of a windowed FIR filter is based on two parameters: the bandwidth, or cut-off frequency, and the length of the kernel, N. The bandwidth is set as a fraction of the sampling frequency fs and has a value from 0 to 0.5 (i.e. the folding frequency). Kernel length N sets the roll-off using the estimate N = 4/BW, where BW is the transition bandwidth, measured from where the curve just departs from 1 to where it is about 0. The transition bandwidth is also set as a fraction of the sampling frequency, in the range 0–0.5.
We discussed the role of filters in shaping the frequency response of the receiver channel and the reflection of that on the reduction of the white noise signals. Such filtering is less effective with interfering signals that share the same band with the radar signal, that is, the passband of the filter. A filter that is matched to a signal is the one whose impulse response is a time-inverted delayed replica of that signal [6]. Thus, for a transmitted signal s(t), the impulse response of the filter matched to the transmitted signal will be a delayed time-inverted replica of s(t), that is,
where to is the optimum sampling moment. In its discrete form, for a matched FIR digital filter, its impulse response for a discrete signal of length N is expressed as
If we consider the response of the matched filter (MF) in the frequency domain, we will find that the transfer function of that filter is the complex conjugate of a delayed replica of the signal or a scaled value as
The abovementioned equation indicates that the filter will be matched to waveform of the signal represented by its spectrum S(ƒ) rather than its amplitude or time of its arrival. When we decide on using matched filtering for the processing of signals, we must carry with us that the signal will be distorted. The idea is to maximize the signal-to-noise ratio at the output of the filter prior to detection rather than preserving the original waveform of the signal. In most of the cases, it will be difficult or impossible to realize the filter of a specific transfer function that is proportional to the signal spectrum conjugate. Thus, special waveforms are to be designed to accomplish certain functions, such that the corresponding matched filters are realizable and practical. For further reading, the subject of radar signal waveform design is explained in detail in [7].
From [1, 4], it is derived that a matched filter can be realized with the aid of a correlator. This is because the output signal of a filter matched to the transmitted signal is proportional to the autocorrelation function (ACF) of the transmitted signal or a delayed replica of it, as follows:
This conclusion states that if the received signal was free of noise, the signal at the MF output would be the autocorrelation function of the transmitted signal. If the received signal was affected by the presence of any form of noise, the output signal will be the cross-correlation function of both signals. Due to the lack of correlation between the transmitted signal and noise, the effect of noise will be dramatically reduced in the output. Figure 3 demonstrates the use of a correlator in the processing of radar signals. The output signal at the output of a digital MF could be either expressed as convolution between the received signal and the impulse response of the MF given in Eq. (6) or as correlation between the received signal and a delayed version of the transmitted signal like that shown in Figure 3, according to:
Correlator as MF.
Doppler filters are applied in the processing of radar signals in order to remove fixed targets and raise the signal-to-clutter ratio. There are several processing techniques to enhance the SCR in pulse radar, such as moving target indication used in the short pulse radar and moving target detection that is used in the pulse Doppler radar [2]. CFAR that will be discussed in detail is another signal processing technique that improves detection of targets inside clutter. If the wavelength of radar signal is λ, the Doppler frequency shift in the signal received from a target moving with a relative velocity vr (to or from the radar) is given as
MTI filters are easier to be implemented and cost less compared to MTD processors. For a video signal, the MTI filter is a high-pass filter used to filter out signals of fixed targets, which is of zero Doppler frequency, and weakens signals reflected by slow targets. The MTI filter is a discrete analogue or digital filter. Therefore, it has a periodic frequency response that repeats each multiple of the sampling frequency, that is, the pulse repetition frequency (PRF) of the pulse radar. Hence as seen in Figure 4, an MTI filter will reject more than one moving target of the Doppler frequency that is not actually zero. Those rejected targets are of frequencies that are multiples of the PRF, which are due to targets having blind speeds that are not sensed by the radar, that is, it will be blind and will not detect those targets. Substituting PRF in Eq. (11), the first of those blind speeds is related to the PRF and the wavelength as
Frequency response of the MTI filter.
The MTI filter will act as a proper HPF only within the Doppler frequency range
MTI filters are frequently designed as FIR filters. Two-pulse canceller is a first-order FIR filter of filter coefficients b = {1, −1}, which is the simplest MTI filter. Higher order filters, such as the three-pulse canceller of b = {1, −2, 1} and four-pulse canceller of b = {1, −3, 3, −1}, are mostly used since they provide more flat amplitude frequency response, both in the passband and stopband.
FIR filter shown in Figure 2 is stable by definition, and hence it can easily become adaptive whatever its coefficients are. Its kernel could be modified to adapt, either to the required radar signal or to the unwanted signal due to noise or clutter [8]. For all FIR filters, the coefficients bk are the elements of its kernel, that is, samples of its impulse response. For example, an LPF may be modified in order to reshape its frequency response according to variations of the received signal. A matched filter can be adapted to diverse waveforms that are transmitted by the radar to enhance the diversity of detection and/or to avoid some jamming signals. The MTI filter may alter the location of its notch so as to compensate for a moving radar platform, for example, an airborne MTI (AMTI) radar [2].
Figure 5 illustrates the basic concept of the adaptive filter. The idea is to filter the input signal x(n) using an adaptive filter, such that it excels to match that signal to a desired signal d(n). The filtered signal y(n) is subtracted from the desired signal in order to generate an error signal. That error signal e(n) drives some adaptive algorithm, which in order generates the filter coefficients bk in a manner that minimizes the error signal according to that adaptive algorithm. Filter coefficients are known also as tap weights w(k) of the FIR filter. Mean square error (MSE) algorithm is one of the most popular known algorithms, which in turn includes the least mean square (LMS) and the recursive least squares (RLS) algorithms [9].
Adaptive FIR filter.
In Figure 5, the estimation error signal at the time index n is e(n). It is fed back to the adaptive algorithm to minimize some function of the error, known in the literature as the cost function [10]. In case of the radar, the optimal output signal of the applied adaptive filter is identical to the desired echo signal, that is, it looks like the transmitted signal. If the output signal of the filter is identical to the desired signal, the error signal becomes zero.
All MSE algorithms intend to minimize the cost function, which is equal to the expectation of the square of the difference between the current output signal of the adaptive filter y(n) and the desired signal, as follows:
LMS algorithm is generally used in adaptive filtering because of the simplicity of the required computations. It is also known as stochastic gradient-based algorithm, which makes use of the gradient vector of the filter tap weights in order to converge on the optimal Wiener solution [9]. That relative simplicity made it the benchmark versus which all other adaptive filtering algorithms would be judged. At each of the iterations of the LMS algorithm, the filter tap weights of the filter will be updated in accordance to:
In Eq. (14), the parameter μ is the step size, which will be a small positive constant. The step size manages the influence of the updating factor. The selection of a suitable value for μ is crucial to the performance of the LMS algorithm. If that value is so small, the time taken by the adaptive filter to converge on the optimal solution is going to be very long. In contrast, if μ is so large, then the adaptive filter might become unstable and thus its output signal would diverge away from the desired signal [9].
A short pulse waveform of a single pulse is modulated only in amplitude. For the small pulse width, the range resolution is excellent and is directly proportional to that width, namely δR = c τ/2. However, it will not be enough to resolve scattering targets in Doppler because Doppler resolution is proportional to the bandwidth. Bandwidth is equal approximately to the reciprocal of the pulse width and thus the time bandwidth product is about unity. Such waveforms are used in the MTI radar that uses Doppler processing just to filter out non-moving targets from its output, which does not require much better Doppler resolution.
Long pulse waveforms consist of a single pulse that is modulated only in amplitude, but now the duration will be long enough for the scattering targets to be resolved in Doppler. CW radar signals belong to this class of waveforms, with very long pulse duration. On the other hand, in a practical situation, it will not be suitable for resolving targets in range, of course unless we modulate the angle of the signal. Noise-type waveforms consist of pulses that are modulated with amplitude or phase modulation function that is irregular or noise like and has a relatively larger time-bandwidth product. This class of waveforms includes those used in pulse compression of radar signals like biphase codes (such as Barker and pseudorandom codes), polyphase codes (such as Frank codes), nonlinear frequency modulation (NLFM) pulses, pulse trains with staggered PRF pulses, trains with frequency shift coding and long pulses with irregular amplitude modulation [7].
The radar signal is frequently coded with the purpose of pulse compression. Nevertheless, the frequency and phase coding of the radar signal are employed to accomplish other requirements rather than the compression of echo signals. Thus, the conventional pulse or CW radar signals with fixed frequencies and phase angles are not always the favourable waveforms. Nevertheless, the environment in the region of the radar includes ground clutter, multipath, refraction, weather and interference. The optimum radar signal waveform for this application must contain sufficient energy to achieve detection on the smallest aircraft at the longest range. It must also have sufficient bandwidth to provide the necessary range accuracy and resolution and must have a duration long enough to permit velocity discrimination of targets relative to ground clutter.
Pulse compression [2], also known as pulse coding, is a signal processing practice that is intended to maximize the sensitivity and resolution of radar systems. In a pulse radar system, there are several factors that affect each of the radar functional parameters. One of these factors is the effect of the pulse width in the determination of the range resolution and accuracy. The radar range equation [1] indicates that the range is affected by the energy of the transmitted pulse, that is, its power and pulse width. Generally speaking, the ability of any radar to detect far objects depends primarily on the transmitted signal energy.
For pulse radar, energy is indicated by the average transmitted signal power, which is expressed as the peak power multiplied by the duty cycle of the transmitter. Even though the peak transmitter power could be as high as hundreds of kilowatts or even some megawatts, as pulse radars transmit very short pulses (typically in order of microseconds), the average transmitted signal power perhaps is much less than 1% of that value. Obviously, this would not be the efficient use of the available transmitter power.
Transmission of longer pulses improves the detectability of the radar as it increases the average transmitted signal power. On the other hand, just lengthening the radar pulse has the result of degrading its range resolution, because the RF pulse would be spread over a larger distance. Thus, some technique is needed to increase the average power without degradation in the range resolution and accuracy of measurement. In order to solve this dilemma, we have to understand that the range resolution of the pulse radar does not essentially depend on transmitted pulse duration but in fact it depends on the bandwidth of the transmitted pulse. In old radars, a simple rectangular RF pulse is transmitted in each repetition interval. Its bandwidth is just 1/τ, where τ is the duration. If the carrier signal within the pulse is altered using frequency or phase modulation, the bandwidth will be increased. Accordingly, the radar resolution is changed independent of the average transmitted signal power. This kind of modulation of the transmitted radar pulse is generally known as radar signal coding.
In radar signal processing, pulse compression is one of the techniques that makes use of coding to increase the bandwidth of the radar signal. At the radar receiver, reflected coded pulses are compressed in the time domain, which produces a pulse of a finer range resolution than that of an uncoded pulse. Decoding or compression involves correlating the received signal with a replica of the transmitted signal. Several methods were developed to realize that, such as binary phase and polyphase coding, frequency modulation and frequency hopping. Applying linear frequency modulation (LFM) to the transmitted pulse is often referred to as chirp coding. The main drawback of pulse compression is the appearance of range side lobes around the main signal peak after decoding. It may produce echoes that spread out from neighbouring targets and bring in range ambiguities.
A common signal coding method used in the pulse radar is binary phase shift keying (BPSK), which involves repeatedly flipping the phase of the RF signal within the duration of the pulse, according to a code known as the spreading code, which should yield minimum side-lobe levels after decoding. Barker codes are efficient binary codes, which have lengths of up to 13 bits [2] only. Side lobes can be minimized by the use of complementary codes, which are carefully selected pairs of codes whose range side lobes cancel out under ideal conditions. Generally, the efficiency of a particular code is judged by the time-bandwidth product, BT, where B is the pulse bandwidth and T is the entire transmitted pulse width. To realize a higher compression ratio, more subpulses of τ width are used per one long pulse of T duration. For an uncoded pulse, BT = 1, as B = 1/T only.
For example, a 13-bit Barker code has a time-bandwidth product of 13, which means that each radar pulse contains 13 times the energy of an uncoded pulse of the same resolution. Range resolution becomes 13 times finer than that for an uncoded pulse of the same width. A pulse Doppler radar compromises between range and velocity resolution by applying relatively longer pulses, while some radar systems focus on the issue of range resolution more than other parameters. Precise target tracking, range finders, target recognition and radar imagery applications are examples of those areas of interest. Synthetic aperture radar (SAR) is widely used for radar imagery and mapping and for space and aerial reconnaissance, which requires superior range resolution [3].
Pulse compression is the solution that solves almost all those problems mentioned. It makes use of specific radar signal processing techniques to provide most of the advantages of very narrow pulses yet retaining long range with less power. Matched filters, similar to those discussed in Section 3, are used to perform pulse compression of the received signals. Compression is needed because radar signals are usually expanded in time before transmission and must be compressed to obtain the desired resolution or sharp focusing. This will enhance extensively the received signal SNR and thus improve the radar detectability of far and small targets immersed inside noise and clutter. We can briefly state the merits of the pulse compression mentioned above in addition to others as follows [7]:
Large compression ratio offers higher resolution and accuracy in range and reduction in the radar minimum range.
The reduction of the required receiver bandwidth and thus reduction of interference.
The improvement of the SNR of the received signal due to matched filtering or correlation of signals.
Better anti-jamming capabilities, low probability of intercept (LPI), low probability of exploitation (LPE) and low power that makes the radar system difficult to be detected or positioned.
Long transmitted pulses cause low interference to other radar and communication equipment and thus provide better electromagnetic compatibility (EMC).
Ease of using solid-state transmitters and small power supplies like batteries that leads to small and light systems.
The Doppler information in the long pulse received back from a target will be richer than what is gained from a short pulse.
Pulse compression realizes radar imagery and mapping in addition to the possibility for target recognition and classification, using a high-range resolution (HRR) radar.
There are many techniques used in pulse compression that are generally divided into passive and active techniques, which include frequency modulation and phase modulation techniques.
In this technique of pulse compression, the phase angle of the transmitted radar pulse is switched between two or more values, while the long pulse is modulated with the radar PRF as usual. The relatively long transmitted pulse is divided into an integer number of subpulses, of equal widths and amplitudes but with different phase angles.
A digital signal or a code sequence is used to determine the phase of each subpulse. It is more common to use binary or biphase coding [11] to provide the modulated pulse rather than the ternary, quaternary or higher coding of signal. A signal multiplier may be used to obtain the binary phase shift keying signal that has a phase angle that swings between 0 and 180°, according to the modulating digital signal. In Figure 6, such a BPSK modulated pulse is illustrated. Due to the modulation of the carrier of the transmitted signal with the digital signal, the bandwidth of the transmitted signal is increased. The spreading of the digital signal in most cases is a pseudorandom signal that has special desirable correlation features. Such signals look like white noise, which has an infinite bandwidth and an infinitesimal autocorrelation function centred about the zero axis. Those pseudo-noise (PN) binary codes are used in spread spectrum communication systems, in a similar manner, in order to spread data signals along the frequency domain. They can be easily generated with the aid of shift registers (D-type flip flops) and logic circuits.
Phase-coded BPSK signal.
After the phase-coded signal is amplified and emitted into the radar zone of operation, the echo signal will be reflected by targets inside that zone. The received echo pulse is compressed by a matched filter or a correlator. The peak value of the compressed signal, for any code, is equal to the number of subpulses N times the pulse amplitude. The pulse width of the pulse is equal to that of the subpulse used, τ. The pulse compression ratio (PCR) is equal to the code length, that is,
Side lobes are generated within the output of the matched filter, before and after the main lobe. Those side lobes may be reduced by the selection of the proper code for the modulating signal. Many measures exist to differentiate between the available codes and decide which code is more desirable for a certain application. The features of a specific code are extracted from the ACF of the sequence that represents that code. The relations between the peak and the side lobes are of big importance. The peak-to-side-lobe level (PSL) is defined as the ratio of the largest square of side-lobe levels xi to the square of the peak of the compressed pulse xo, which is computed in decibels by [11].
Barker codes are one class of the famous classes of codes used in the biphase coding of the radar signal with the purpose of pulse compression. Those codes are characterized with very small PSL values and particular autocorrelation functions that have side lobes with equal amplitudes and unity absolute value, similar to that shown in Figure 7.
ACF of 13-bit Barker code.
Barker codes exist for only certain code lengths that are limited to a maximum code length of 13 bits. Table 1 shows all the available Barker codes, where a plus sign represents +1 and the minus represents −1 bit. We notice that for any of these codes, there exist four allomorphic codes that are formed by the inversion of the bits and/or the reversal of their order. All these allomorphic codes will have the same autocorrelation function, and thus we consider only one of them. The autocorrelation function of a discrete bipolar signal, that is, of ‘+1’ or ‘−1’ values, is approximated using the discrete temporal aperiodic ACF given as
where the integer index m steps over the domain −(N − 1) ≤ m ≤ (N − 1) and x = 0 for all indices k < 0 and k > N + 1.
Code length | Code sequence | PSL, dB |
---|---|---|
1 | + | - |
2 | + −, + + | −6 |
3 | + + − | −9.5 |
4 | + + − +, + + + − | −12 |
5 | + + + − + | −14 |
7 | + + + − − + − | −16.9 |
11 | + + + − − − + − − + − | −20.8 |
13 | + + + + + − − + + − + − + | −22.3 |
List of Barker codes.
Barker codes are a subset of other binary codes known as the minimum peak side lobe (MPS) codes that attain the lowest PSL for a given code length. Thus, we find that many approaches to finding binary sequences with good side lobes have been undertaken. By means of an extensive computer search, binary codes of long lengths can be found. According to [11], Delong has discovered a considerable number of these codes as long as 99 bits with a peak side lobe amplitude of 7. And according to [7], Kerdock later has built a dedicated computer and has applied it successively to search for good codes. However, neither of them has made any attempt to ascertain that the discovered codes are MPS nor have they compiled a list of all codes of a given length with a given PSL, although long codes with excellent PSL values have been discovered this way.
There exist other approaches to achieve longer codes providing higher pulse compression ratios but look different somewhat from Delong and Kerdock approaches. One of these is the process of code concatenation or combination, in which available codes are utilized to code the transmitted pulse at more than one level; thus, each segment of the code is coded again with another phase code. When utilized with Barker codes it has been called Barker-squared or combined Barker coding. Referring to [11], Hollis combined a Barker code of length 4 with the code of length 13 in two ways, each of different ACFs.
A random code is to be obtained when the binary bipolar sequence is determined by a random process, with an equal probability of 0.5 for both positive and negative values. A pseudorandom, or pseudo-noise code, is obtained when the binary sequence has approximately the same number of positive and negative values with a probability ≈0.5.
An important class of pseudorandom codes is the maximal-length sequences or shortly the m-sequences. These sequences are known also in the literature as Galois and PN codes [7]. While Barker codes are finite, the m-sequences are not finite and can realize higher compression ratios. Periodic m-sequences are easily generated with the aid of linear shift registers and XOR gates. The register is fed back with a number of its different stages of outputs, after they are XORed or modulo-2 summed. However, certain feedback connections of the register stages outputs to the input and not all of them will produce codes of maximal length, that is, m-sequences. The maximal length of the sequence is the longest possible period for an n-stage register, which is given in terms of the number of register stages (flip-flops) as
A cycle of the periodic autocorrelation function of an m-sequence can be computed by Eq. (17). It is shown in Figure 8 where it looks somehow different from the ACF of a Barker code. The ACF function might be preferred to be normalized to N, which is the peak value at m = 0. As the code length N tends to infinity and the subpulse width τ tends to zero, the ACF of the m-sequence tends to that of white noise, which is an impulse that is centred at a zero time shift. Hence, the name PN is significant. It may be worth to remember that the power spectrum density of white noise is constant with frequency. A well-known example of white noise is the thermal noise that has a power density of kTo, where k is the Boltzman’s constant and To is the absolute temperature. In the absence of Doppler shift, the circular autocorrelation function has two levels. Its amplitude at the origin (i.e. for m = 0) is equal to the length of period N. For all offsets other than multiples of one period, the magnitude of the function is unity. This may be written as
ACF of a 15-bit M-sequence.
There are 2N−1 samples in the autocorrelation function and this function is symmetrical about the origin. PN codes are often chosen over randomly coded words for application because their generation and decoding can be easily mechanized, their side lobe levels easily predicted and they provide a rich source for good codes of arbitrary length. Pseudorandom sequences have some properties that they share with random sequences and other properties that are unique for them. The number of segments of ones in each period of the sequence is within one, that is, ±1, of the number of zeros (minuses), which is known as the balance property. In every period, half the runs have length 1, one-fourth have length 2, one-eighth have length 3 and so on (the run property).
PN codes can be generated deterministically through the implementation of shift registers with feedback connections. The initial condition in the shift register determines the starting point of the code. The condition of all zeros is forbidden. The last stage in the shift register must be connected to the feedback circuit and there must be an even number of feedback taps [4]. For maximal-length PN codes, each period has a length of 2n−1 pulses. There are 2n−1 different maximal-length codes that can be generated from each n-bit shift register, which are simply shifts of each other derived by changing the seed utilized. The number of different n-bit shift registers that yield maximal-length codes, that is, the number of codes (including mirror images), is given by
where Ф(x) here is the Euler phi function [11]. If 2n−1 is a prime number, the number of codes will be (2n−2)/n, whereas if 2n−1 is factorable into prime numbers denoted as Pi then
Each prime Pi is used in the foregoing computation only once even if it appears in the factorization more often. Algebraic sum of all the ACFs for all the starting points of a given code is (2n−1)2 at the origin and [(N−k)/N] (2n−1) for each segment, k, away from the origin, in either direction. The ACFs of this waveform is symmetrical about the time delay axis, as shown in Figure 8.
Diversity is a key solution for many radar and telecommunications problems. Using diverse radar elements of different parameters rather than depending on a single element enhances the radar capability and functionality. For instance, frequency diversity increases the detectability of the radar to detect different targets of different resonant sizes. Similarly, using diverse PRF reduces the problem of blind speeds in the Doppler processing of signals [7]. Earlier, most radar pulse coding was done for the sole purpose of achieving the benefits provided by pulse compression. Exceptions included the use of frequency modulation for ranging applications, for example, continuous wave and high PRF radar and the use of coded pulse trains to increase Doppler resolution. Nowadays, radar signal coding is used for other purposes such as security and waveform diversity. Waveform diversity takes many forms, including PRI diversity, frequency diversity, amplitude diversity and phase diversity.
Optimal coding is based on a certain criterion such as measurement errors, detection performance and false alarm probability. These criteria have traditionally placed certain restrictions on code selection. For instance, the repeated use of a single pulse code meets most optimality requirements for traditional applications, and identically coded pulse trains can be used to demonstrate greatly enhanced Doppler processing [12]. On the other hand, diverse pulse coding techniques are required to ensure that target responses from individual pulses are distinguishable from one another. This inherently requires the generation of code families that possess good autocorrelation properties and cross-correlation properties as well, which are often mutually exclusive properties.
Phase-coded radar signals offer a remarkable flavour of diversity that simply depends on the change of the code of the modulation sequence. Such signals are easily generated compared to other diverse radar signals that require changing the carrier frequency. Frequency hopping signals may be considered as diverse code signals, in which its pattern of changing codes is optimized for diversity to avoid jamming, rather than to enhance the range resolution of radar. For a radar that uses a pseudorandom waveform, a comprehensive search of possible waveforms could be attempted. The choice of waveform may be reduced upon many rules, such as search of maximal-length sequences that do not include binary codes of all ‘1s or all ‘0s, which definitely do not accomplish the required resolution. Several codes are satisfactorily close that only one of a set might need to be tried. However, the number of combinations is still big, and extensive search of high time-bandwidth codes could not be practical using available computers. Nevertheless, regarding the current improvement in computer speeds, the power to do this seems to be achievable.
For example, if a correlator can correlate a waveform with 1 ms integration length in real time, it could search 10,000 waveforms in only 10 s. Therefore, if all the possible low probabilities of exploitation techniques were employed as efficiently as possible, it becomes so difficult to exploit the transmitted signal within tactical timescales. A great deal of research is being carried on to investigate waveform that designed and the related signal processing for the high-resolution pulse Doppler imaging, both in radar and in sonar.
The uncertainty association of Fourier transformation states the primary limitation on the ability of any individual waveform to simultaneously resolve two or more targets closely spaced in both time delay and Doppler shift [13]. Transmitting successive signals of adequately diverse waveforms and processing them properly could make it possible to resolve those targets and generate a high-resolution delay-Doppler image. It is somehow similar to the situation of generating a high-resolution optical image from several low-resolution optical images with somehow different imaging apertures. A selection of optimal sets of coded waveforms and designing associated processing algorithms has already been considered [13], for example, in order to generate pulse-echo delay-Doppler images of a substantially higher resolution than that is possible using a single waveform with comparable time-bandwidth product. In an adaptive diverse system, the instantaneous waveform is selected to improve the performance according to changes in clutter and noise variations [8].
The importance of this technique is based on the fact that it realizes the higher discrimination of radar or sonar targets compared to those that are possible using only one waveform. It results in higher resolution images in pulse-echo imaging systems, in addition to improved capability in resolving targets in tracking systems. There are many applications that can benefit from this superior delay-Doppler resolution, such as SAR, planetary and ground-based astronomy radar, ionospheric radio sounding, meteorological radar, aircraft surveillance and tracking radar and in active sonar systems.
For better reduction of interference of several radar signals, one may use sort of orthogonal codes, such as Walsh codes, in same manner as in the code division multiple access (CDMA) technique used in mobile networks. However, though those codes have good cross-correlation properties that make them exceptional in multiuser environments, and they have poor PSL due to their undesirable ACF properties. On the other hand, MPS and PN codes are not orthogonal but they have very considerable PSL that realize high SNR at the output of matched filter and high-pulse compression ratio, with the minimum range ambiguity [7].
A major problem that encountered the performance of radar is clutter, which is defined as the unwanted targets that may compete with the desired targets in the radar receiver. There are many efforts exerted to model, analyse and mitigate clutter in radar systems. Many techniques appeared in the literature deal with modelling and filtering of clutter. Clutter is a random phenomenon and it is well described in the context of a probabilistic framework.
In this section, analysis of well-known CFAR detectors is going to be accomplished. It also includes a brief overview of topics related to clutter modelling and mitigation in the monostatic radar. Finally, the schemes of CFAR detectors under different clutter scenarios will be covered.
Sometimes the clutter residue, after the moving target indicator, is enough to saturate the radar display. To overcome such shortcomings, a clutter estimation circuitry is provided after MTI to reduce the effect of false alarms. There are many schemes devised in the literature to keep the level of false alarms constant. Here in this section, an analysis of some well-matured schemes will be given. It is well known that when the radar returns come from a background with homogeneous clutter, the cell average (CA) CFAR is adequate to control the false alarms. In some cases, there will be strong returns from some targets that may mask other weaker targets. In these cases, it is better to use a clutter estimation scheme based on choosing the Smallest-Of (SO) sample of returned clutter power to represent estimation to the background clutter, which is known as SO-CFAR. In cases of clutter power transition, it is better to use a scheme based on choosing the Greatest-Of (GO) sample in the reference window as an estimation to clutter background. This scheme is known as GO-CFAR sample, as a representative to clutter.
In other situations, there may be spiky samples of the clutter in one side of the reference window, which is known as a clutter edge. Order statistic is another clutter estimation scheme that may be used to alleviate both the problem of multiple targets and clutter power transition in the reference window. In some situations, a radar receiver is encountered by a fixed position clutter during several scans. Such types of clutter can be stored and subtracted in the course of the following scans. Such clutter is called a clutter map or area clutter. In the following paragraphs, we will give a short review to the concept of each of these schemes that is used to reduce the effect of false alarms. All these schemes are capable of setting adaptively a threshold to represent the local clutter background in the chosen reference window. The threshold in these schemes of CFAR is set on a cell-by-cell basis by processing a group of cells of a reference window sited on either side of a cell under test (CUT). Figure 9 gives a general structure of the CA-, SO- and GO-CFAR.
Mean level CFAR processors.
In this scheme, a reference window of length N cells is chosen to represent the local clutter power background. The CA-CFAR will maximize the probability of detection if the clutter background is homogeneous and of independent and identically distributed (IDD) observations. As the size of the window increases, the detection probability approaches that of the optimum detector based on a fixed threshold.
The CA-CFAR makes its estimate of clutter power by summing samples of background before and after a CUT, then taking the average of them, assuming that the underlying noise distribution is homogeneous (i.e. exponentially and independent and identically distributed samples). Of course the processor will encounter severe performance degradation if the above assumption is violated. These violations may occur in terms of interfering targets or a clutter power transition (clutter edge), and in both cases a result is the unnecessary increase in the threshold and reduction in probability of detection.
In order to analyse the detection performance of a CA-CFAR scheme with the assumption that the clutter background is homogeneous, assume that the square-law detected output for any range cell is exponentially distributed with the probability density function (PDF) [14]
Under the null hypothesis (H0) of no target in a range cell and homogeneous background,
The cells surrounding CUT is always given by
where yb denotes the optimum fixed threshold. Similarly, the probability of detection in the case of optimum detector is given as [15]
Eqs (24) and (25) can be combined to give
A code written in Matlab is used to present the performance of the optimum receiver in terms of SNR versus Pd with fixed Pfa and varying reference window length, using Eq. (26), as shown in Figure 10.
Performance of CA-CFAR with varying window length and fixed Pfa = 1e-4 detector.
In the problem of setting adaptive threshold for the radar receiver to cope with the varying clutter power levels, the CFAR scheme has to follow the clutter localities. The estimate of clutter power,
where M(.) is the moment generating function (MGF). Similarly, the probability of detection can be given by [14]
For CFAR action to hold, M(.) must be independent of
In the CA-CFAR scheme, the noise estimate is obtained by summing the power content in the reference cells before and after the CUT. This may be the adequate estimate when the background noise is exponentially distributed. Therefore, the noise estimate for this scheme can be written as
where Xis are range cells surrounding CUT. The exponential density is a special case of the gamma density with α = 1 in the PDF given by [16]
The cumulative distribution function (CDF) corresponding to this PDF is denoted by G(α,β). The moment generating function to G(α, β) is [16]
The probability detection Pd for the CA-CFAR processor is obtained by substituting Eq. (31) into Eq. (27) with
When setting S = 0 in Eq. (32), the scale factor
It is clear that from Eqs. (32) and (33), that both the Pd and Pfa expressions are independent of
Performance of CA-CFAR.
Performance of CA-CFAR compared to that of optimum detector.
The performance of CA-CFAR in a non-homogeneous background taken from a reference window can be analysed in similar way to the above discussion. The probability of detection obtained under CA-CFAR for this case is given as
where r is the number of interfering targets. Figure 13 shows the performance of CA-CFAR in a non-homogeneous environment compared to that in a homogeneous one. Also from Figure 14, it illustrates the performance of this processor when the number of interfering targets is two and probability of false alarm is 10−4 with the reference window length as a varying parameter.
Performance of CA-CFAR processor in a homogeneous environment versus performance in a non-homogeneous environment (two interfering targets).
Performance of CA-CFAR at a non-homogeneous background (two interfering targets) with varying reference window and fixed Pfa.
Excessive number of false alarms in the CA-CFAR processor at clutter edges and degradation of detection probability in multiple target environments are prime motivations for exploring other CFAR schemes that discriminate between interference and primary targets. Candidate schemes are SO- and GO-CFAR to deal with the two problems, respectively. Here in this section, analysis of the two schemes will be given. The two schemes are modifications to the CA-CFAR scheme, and each of them overcomes one of the two mentioned problems, with the addition of loss of power when operating in a homogeneous background. As abovementioned, the GO-CFAR maintains the false alarms constant when the clutter edge is encountered in the reference window, while the SO-CFAR resolves the interfering targets.
A modified scheme proposed in [16] known as GO-CFAR is specifically aimed at reducing false alarms at clutter edges. The estimated total noise power is obtained from the larger of two separate sums calculated for leading and lagging reference cells to the CUT. For this scheme, we have (see Figure 9)
where
with n=N/2, where N is the number of tested cells. In general, the PDF of Z in (35) is given in [15] as
where f(Z) and F(Z) are the PDF and CDF, respectively. For a homogeneous background, Fi = G(n,2
where
Substituting Eqs. (38)–(41) into Eq. (37) and after some algebraic manipulations, the following expression is obtained:
Exploiting the assumption that both sides of the window are of homogeneous clutter, we can see in Eqs. (42) and (43)
The probability of detection for GO-CFAR is simply obtained by setting
where
Figure 15 shows the performance of GO-CFAR compared to that of CA-CFAR when the clutter background is homogeneous. It is clear that CA-CFAR outperforms GO-CFAR when both algorithms operate in homogeneous background.
Performance of GO-CFAR compared to CA-CFAR when clutter background is homogeneous.
The SO-CFAR is a modification of the CA-CFAR scheme so as to alleviate the problem of the presence of close spaced targets in the reference window. In the SO-CFAR scheme, the estimated power noise represents the smaller value of two sums Y1 and Y2. That is [16]
In this case, the PDF of Z is given as [Weiss]
The probability of SO-CFAR can be obtained by the MGF or
The probability of detection for this scheme can be given by the setting
Figure 16 shows a performance comparison between CA-, GO- and SO-CFARs when the background is homogeneous. When the background of clutter is not homogeneous, that is, the reference window contains clutter edges or more than one target found in the reference window, this situation increases the false alarms when the processor is CFAR.
Performance comparison of the three processors (CA-, GO- and SO-CFARs) in the homogeneous background (Pfa = 1e-4).
Even the Arab alchemists, the first chemists of the seventeenth and eighteenth centuries, talked about the relationship between color change and changes in the physical properties of various substances [1]. For example, the temperature of substances and their chemical transformations were estimated approximately by color. Here, new data on color phenomenon that we have discovered in recent years for complex and simple substances is provided. For example, these are the effects of the relationship of physical and chemical properties and color characteristics of compounds (“color properties principle”) [2, 3, 4, 5, 6, 7, 8, 9, 10]. Color characteristics were measured by standard methods in colorimetric systems RGB or XYZ [11, 12, 13]. In particular, the effects of the relationship between the vertical ionization potentials and the electron affinity of light-absorbing molecules in the visible region were found [14, 15, 16, 17, 18]. The results indicate the practical use of these effects in chemical technology and nanophysics. We assume that the cause of these phenomena is quantum entanglement and strong correlation of electron states [19]. We established new physical effects between spectral densities (integral absorption, reflection, and transmission characteristics) with ionization potential and electron affinity [2, 3, 4, 15, 18, 19, 20, 21]. We propose to use these effects in determining the energies of electronic states. Methods for determination of IP and EA for molecules and organic semiconductors have been developed. We propose to use these effects in determining the energies of electronic states. In addition, the color characteristics of biological fluids were investigated. In addition, we have determined the averaged color characteristics of the electromagnetic spectrum for aqueous solutions of hemolyzed blood, plasma, and serum from 100 donors and 95 patients with different diagnoses and different severities of their conditions. From the averaged absorption spectra, we calculated the color characteristics of the hemolyzed blood, plasma, and serum from the donors and patients by the standard CIE procedure. The blood is considered as a single, indivisible light-absorbing system in studying complex biological specimens. We studied the relationship between the color characteristics of human blood in normal and pathological conditions [22, 23, 24, 25, 26]. Let us consider these aspects in more detail.
\nWe have discovered new optical effects of the relationship between the physicochemical properties and color characteristics for very complex chemical systems [2, 3, 4]. In particular, the dependencies between the properties and color characteristics of multicomponent hydrocarbon systems are investigated. Dependencies between color coordinates (luminosity) and various physical and chemical characteristics of these substances are established. All results are confirmed by statistical data processing. The dependence of the properties on the CCs is linear (the law “color-properties”):
\nwhere Z is one of the physical or chemical properties, q is the one of the color characteristics of the substance (e.g., color coordinates Xj\n, Yj\n, Zj\n in the XYZ system or Rj\n, Gj\n, Bj\n in the RGB system; or chromaticity coordinates xj\n, yj\n, zj\n in the XYZ system or trichromatic coordinates rj, gj, bj in the RGB system; j, standard light source A, B, C, or D), and B\n0\n, B\n1 are the empirical constants dependent on the type of the source and the class of researched substances and dimensional properties.
\nColor coordinates of (X, Y, Z), coordinates of chromaticity (x, y, z), hue (λ), and luminosity (L) have been taken as color characteristics [11, 12, 13] . CCs of multicomponent hydrocarbonic systems have been determined by the technique of the International Committee on Illumination (Commission Internationale de l’Eclairage, CIE) [11] for four standard sources (illuminants) A, B, C, and D65. The technique, corrected for optically transparent [13] medium, has been used. Electron absorption spectra of multicomponent hydrocarbon systems have been determined in toluene solutions in the range of 380–780 nm with the use of automatic spectrophotometer.
\nThe CCs were defined on the methods CIE [12, 13] in the revised version to optical transparent solutions via the transparent coefficients—τ(λ). The color properties were calculated from formulas (2)–(7):
\nwhere X, Y, and Z are the tristimulus values for system CIE; Е(λ) is the spectral power distribution for the spectrum of emission source; \n
The transparent coefficient is defined on known relationships:
\nk (λ)—absorption coefficient.
\nwhere D is the optical density of solutions, c is the concentration, and L is the thickness of absorption layer.
\nFor more hydrocarbon systems, the solutions were prepared with concentration near 0.002 g/l; similarly, as in this case, we got the result with minimal errors.
\nThe relations for X, Y, Z are presented in matrix form [12]:
\nwhere Ф\n\n
The chromaticity coordinates were calculated on formulas in the system CIE (12).
\nwhere x, y, and z are chromaticity coordinates.
\nIn \nTable 1\n the defined CCs of multicomponent petrochemical systems are given [3, 4]. As it can be seen from the results of the calculations, CCs at the identical radiation source are close among themselves despite their different nature. Obviously, the reason of similarity of color properties is the similarity of the absorption spectra of the systems researched. Also the research has shown that multicomponent petrochemical systems do not have color isomerism, i.e., their CCs change depending on the radiation sources.
\nHydrocarbon systems | \nCIE standard source | \nLuminosity | \nCoordinates of chromaticity | \n||
---|---|---|---|---|---|
x | \ny | \nz | \n|||
Separator oils of the Russian Federation (Bashkortostan, West Siberia, Tatarstan) | \nA | \n20.56–70.59 | \n0.51–0.63 | \n0.36–0.42 | \n0.00–0.08 | \n
B | \n17.24–66.60 | \n0.39–0.51 | \n0.42–0.49 | \n0.00–0.19 | \n|
C | \n15.73–65.91 | \n0.38–0.54 | \n0.38–0.47 | \n0.01–0.24 | \n|
D65\n | \n17.17–67.42 | \n0.37–0.51 | \n0.39–0.50 | \n0.01–0.23 | \n|
Blown, residual, road, and structural petroleum | \nA | \n9.80–64.97 | \n0.51–0.67 | \n0.32–0.42 | \n0.00–0.07 | \n
B | \n7.76–61.44 | \n0.40–0.55 | \n0.44–0.46 | \n0.01–0.16 | \n|
C | \n6.60–60.88 | \n0.38–0.61 | \n0.38–0.43 | \n0.02–0.22 | \n|
D65\n | \n7.48–62.79 | \n0.38–0.57 | \n0.41–0.45 | \n0.01–0.21 | \n|
Organic fractions of oligomers | \nA | \n68.23–87.09 | \n0.50–0.52 | \n0.39–0.40 | \n0.08–0.11 | \n
B | \n64.75–84.41 | \n0.36–0.38 | \n0.39–0.41 | \n0.20–0.25 | \n|
C | \n64.40–84.71 | \n0.34–0.37 | \n0.35–0.37 | \n0.26–0.32 | \n|
D65\n | \n65.66–85.51 | \n0.34–0.37 | \n0.36–0.39 | \n0.25–0.30 | \n|
Residual high-boiling hydrocarbonic fraction of vacuum oil refining | \nA | \n15.26–86.19 | \n0.51–0.66 | \n0.33–0.40 | \n0.00–0.09 | \n
B | \n12.15–82.06 | \n0.38–0.57 | \n0.42–0.47 | \n0.01–0.20 | \n|
C | \n10.41–81.90 | \n0.36–0.62 | \n0.36–0.44 | \n0.01–0.26 | \n|
D65\n | \n11.80–83.30 | \n0.36–0.58 | \n0.39–0.46 | \n0.01–0.25 | \n|
Hydrocarbonic fractions with average boiling temperature Тboil 180–360°C | \nA | \n21.20–99.83 | \n0.48–0.63 | \n0.36–0.41 | \n0.01–0.14 | \n
B | \n17.70–98.97 | \n0.33–0.52 | \n0.36–0.48 | \n0.03–0.30 | \n|
C | \n15.98–99.96 | \n0.31–0.55 | \n0.32–0.45 | \n0.04–0.37 | \n|
D65\n | \n17.57–99.90 | \n0.31–0.52 | \n0.33–0.47 | \n0.04–0.36 | \n|
Asphaltenes and tars | \nA | \n0.10–98.67 | \n0.29–0.86 | \n0.14–0.41 | \n0.00–0.33 | \n
B | \n0.09–97.66 | \n0.04–0.62 | \n0.32–0.64 | \n0.00–0.32 | \n|
\n | C | \n0.08–98.67 | \n0.04–0.71 | \n0.24–0.59 | \n0.00–0.37 | \n
The coefficients B\n0 and B\n1 Eq. (1) have been calculated by the method of least squares. As the criterion of adequacy, the correlation coefficient R and the mean-square deviation have been taken. Some results of the calculations are given in \nTable 2\n. The received results show that for all the researched petrochemical systems, there is correlation dependence PCP from CCs [2, 3, 4].
\nMulticomponent hydrocarbon system | \nPCP | \nCC | \nCoefficients of Eq. (1)\n | \nCorrelation coefficient | \nVariation coefficient (%) | \nFisher’s ratio test for sample volume F | \n|
---|---|---|---|---|---|---|---|
В0\n | \nВ1\n | \n||||||
Raw oils | \n\np\n | \n\nyD\n\n | \n0.793 | \n0.349 | \n0.98 | \n0.05 | \n887.80 | \n
\ngA\n\n | \n0.758 | \n0.310 | \n0.98 | \n0.05 | \n889.46 | \n||
\nM\n | \n\nXA\n\n | \n846.429 | \n−4.563 | \n0.99 | \n0.48 | \n1931.49 | \n|
\nRA\n\n | \n897.646 | \n−2.683 | \n0.99 | \n0.46 | \n2084.20 | \n||
\ng\n | \n\nYB\n\n | \n17.063 | \n−0.150 | \n0.96 | \n3.23 | \n453.22 | \n|
\nRC\n\n | \n17.984 | \n−0.098 | \n0.96 | \n3.20 | \n460.43 | \n||
\nЕа\n | \n\nYB\n\n | \n37.701 | \n−0.376 | \n0.97 | \n5.17 | \n536.05 | \n|
\nGA\n\n | \n37.463 | \n−0.140 | \n0.97 | \n5.16 | \n536.51 | \n||
Petroleum residues | \n\nр\n | \n\nxC\n\n | \n0.757 | \n0.462 | \n0.99 | \n0.27 | \n577.35 | \n
\nrC\n\n | \n0.874 | \n0.240 | \n0.99 | \n0.20 | \n1019.05 | \n||
\nM\n | \n\nYB\n\n | \n877.611 | \n−6.183 | \n0.95 | \n4.50 | \n146.34 | \n|
\nrA\n\n | \n−121.96 | \n1157.340 | \n0.96 | \n4.31 | \n161.29 | \n||
\ng\n | \n\nxD\n\n | \n−34.205 | \n106.697 | \n0.98 | \n6.80 | \n341.48 | \n|
\nrC\n\n | \n−6.530 | \n53.960 | \n0.98 | \n5.92 | \n455.11 | \n||
\nЕа\n | \n\nxD\n\n | \n−76.698 | \n228.968 | \n0.98 | \n8.31 | \n308.21 | \n|
\nrB\n\n | \n−22.755 | \n110.594 | \n0.98 | \n7.53 | \n378.34 | \n||
Bitumens and bituminous materials | \n\nр\n | \n\nxD\n\n | \n0.612 | \n0.676 | \n0.98 | \n0.26 | \n260.20 | \n
\nrC\n\n | \n0.876 | \n0.209 | \n0.99 | \n0.22 | \n384.50 | \n||
\nM\n | \n\nXA\n\n | \n−11.918 | \n1308.245 | \n0.99 | \n1.61 | \n797.32 | \n|
\nRA\n\n | \n1341.792 | \n−6.249 | \n0.99 | \n1.47 | \n958.95 | \n||
\ng\n | \n\nyA\n\n | \n−570.815 | \n255.283 | \n0.98 | \n3.62 | \n252.80 | \n|
\ngA\n\n | \n241.685 | \n−379.399 | \n0.98 | \n3.64 | \n249.38 | \n||
\nЕа\n | \n\nYA\n\n | \n−0.878 | \n68.000 | \n0.98 | \n3.32 | \n294.90 | \n|
\nGA\n\n | \n64.355 | \n−0.341 | \n0.98 | \n3.36 | \n287.60 | \n
Coefficients of Eq. (10) for physicochemical property estimation of oils and petroleum residues in colorimetric systems XYZ and RGB [3, 4, 5, 6, 7, 8, 9, 10].
ρ = relative density; M = number-average molecular weight, moles; g = Conradson carbon residue, wt.%; Ea = activation energy for viscous flow, kJ/mol.
In many processes, it is necessary to take express control of the PCPs. Therefore, the dynamic form of Eq. (8) has been investigated in the author’s very last investigation of more than 300 of multicomponent hydrocarbon systems:
\nwhere ΔZ is the change of the physicochemical property and Δq is the change of CCs. Eq. (8) means that a change of properties is proportional to the change of color for any colored substances.
\nThe received results show that for all the researched petrochemical systems, there is correlation dependence PCP from CCs. The correlation coefficient R and the standard deviation were used as the criterion of adequacy. Some results of calculations are given in \nTable 2\n. Properties such as relative density (ρ); number-average molecular weight (M in Dalton); Conradson carbon residue (g in weight.%); activation energy for viscous flow (Ea in kJ/mol). The results show that for all studied petrochemical systems, there is a clear dependence of PCP on CCs [2, 3, 4]. These correlations allow the determination of PCP substances using CCs. Such dependencies are necessary for quality control of oil distillates and oil products. In addition, there is an opportunity for remote control methods of environmental pollution by oil and oil products.
\nFor example, it is possible to determine in a few minutes such properties of formation oils as molecular mass, viscosity, density, the index of thermal stability, the index of reactivity of fractions in coking, thermal cracking processes, etc.
\nThe method of electronic phenomenological spectroscopy (EPS) was first proposed by Mikhail Dolomatov [2, 3]. In recent years, this science direction has been intensively developed by the Dolomatov group at the Oil Technical State University and Bashkir State University (Ufa) in Russia. There are the following approaches and physical phenomena in the basis of EPS:
\nUnlike conventional spectroscopic methods, the EPS studies substances as a comprehensive quantum quasicontinuum without separating the spectrum of the substance into characteristic spectral bands by certain resonance frequencies or wavelengths of individual functional groups or components. The spectrum is studied as a single system (broadband signal) from a set of electronic states. Therefore, at this system integral level, there are new physical effects, not previously known. For example, the effects of the relationship of integral optical characteristics with different macroscopic and quantum properties of the substance as a whole by quantum quasicontinuum “spectrum-properties” and “color-properties” are observed. Qualitatively new physical phenomena appear when considering systems interacting with radiation in a wide optical spectrum. According to these laws, changes in the physical and chemical properties of substances cause a change in the integral characteristics of absorbed, reflected, or emitted radiation in the ultraviolet (UV), visible, and near-infrared (IR) regions of the electromagnetic spectrum. This allows the use of EPS methods for the study of individual and complex multicomponent substances.
\nFor example, there may be a relationship between the integral force of the oscillator and some physical and chemical properties Z:
\n\nHere θ is the integral absorption—integral oscillator force (IOF), which has a simple physical explanation, namely, the area under the radiation absorption curve for the visible and UV regions of the spectrum, l nm mole−1 cm−1;
\nc is the constant depending on the method of measuring the spectrum, the nature of the substance, and individual for each property.
\nTherefore, it can be assumed that there is a relationship between any integral optical characteristic of a wide-spectrum signal (\nFigure 4\n) and properties having the form.
\nHere, P is the integral spectral parameter, for example, integrated oscillator power, color characteristics, integral autocorrelation function, or relative imperial parameter and others into \nFigure 1\n.
\nIntegral phenomenological characteristics of electronic spectra.
Obviously, a special case of Eq. (10) is the effect of “color-properties” (1) (we found with the coauthors O. Kydyrgychova, L. Dolomatova and V. Kartasheva in 1999 [5]). Phenomenological spectroscopy methods have been developed for identification and simultaneous determination of a set of different physical and chemical properties of natural and technical multicomponent organic systems, as well as properties of individual substances. For example, in a few minutes, it is possible to determine such properties of formation oils as the average molecular weight, viscosity, density, thermal stability index, index of reactivity of fractions in the processes of coking and thermal cracking, etc. EPS methods were adopted in the oil and petrochemical industry [2, 3, 4], environmental monitoring [3], biophysics and medicine [24, 25], nanotechnology and molecular electronics [15, 16, 17, 18], and space exploration.
\nFor science and technology, of interest are laws of the relationship of the integral characteristics of the spectrum and the electronic properties of matter. The knowledge of the electron structure of the molecular substances and materials has the fundamental importance for solving real problems in many fields of science and technology (physic of solid state, chemistry, electronics, electrical engineering). Despite progress in the experimental and quantum methods in some cases, there are significant discrepancies between the predicted values and experimental results of electron structure determination of complex materials and compounds. Many compounds and some materials for nanotechnology are characterized by complex structure and chemical and phase instabilities. Therefore, it is necessary to create new methods for assessing electronic structures, for example, ionization potentials, affinities to electron, and some other properties.
\nHence the difficulty of determining the first ionization potentials (IP), the affinity of electrons (EA) and other characteristics of the energies of electronic states for such systems.
\nAs known, ionization energy is the energy required to remove an electron from an atom or molecule. The unit of measurement of this physical quantity is the amount of energy required to remove one electron from one atom or molecule, expressed in electronic volts. The ionization potential (IP) is the electrical potential at which an electron leaves an atom or molecule, overcoming the forces of attraction. This process forms a positive ion [27, 28].
\nIf during the electronic transition the geometry of the molecule changes minimally, it is said about the vertical IP. Next, we will consider the vertical potential only. According to the theorem of Koopmans, the first vertical ionization energy of a molecular system is equal to the negative of the orbital energy of the highest occupied molecular orbital (HOMO).
\nThe electron affinity (EA) of an atom or molecule is defined as the amount of energy released or spent when an electron is added to a neutral atom or molecule with the formation of a negative ion [28].
\nIn the chemistry IP and EA are the characteristics for ability of molecules to donor-acceptor properties [27]. These physical values may be used for the determination of the indexes of reactivity of molecules (a characteristic of its chemical activity).
\nIn previous works [2, 14, 20, 21], we established new physical effects between spectral densities (integral absorption, reflection, and transmission characteristics) with IP and EA. We propose to use these effects in determining the energies of electronic states. Methods for determination of IP and EA for molecules and organic semiconductors have been developed. We propose to use these effects in determining the energies of electronic states.
\nThe IP and the EA of materials were estimated from the empirical dependencies linking these characteristics with the integral parameter of UV and/or vis spectrum:
\nwhere Е is effective ionization potential or effective electron affinity, eV; α1 and α2 are empirically determined coefficients, and P is the integral spectral parameter. For example, integrated oscillator force (IOF), color characteristics, integral autocorrelation function or relative empirical parameter, and others (\nFigure 1\n).
\nThe first experiments in the detection of the phenomenon (2) were carried out in 1988–1992 together with the Dr. G. Mukaeva [14]. The dependence of IP and EA on the integral oscillator force (IOF) was established by the results of the study of about 200 optical spectra of atoms and organic molecules:
\nIntegral spectral characteristic can be any physical value of general absorption or emission of electromagnetic radiation, such as integral oscillator force (IOF):
\nwhere \n
Let us consider the method, which was proved in our previous works [14, 15]. The IP and EA are estimated according to empirical dependencies which link these characteristics with logarithmic integral index of absorption (1).
\nHere Е is the ionization potential or an electron affinity, eV; α1 and α2 are empirically determined coefficients, eV and eV ·nm−1, respectively.
\nwhere ε(λ) is the molar extinction coefficient, l mol−1 cm−1; θ\n1g\n is the integral logarithmic index of absorption (logarithmic IOS), ·nm; λ\n1 and λ\n2 are borders of the spectrum in UV and (or) visible region, nm; and λ1 and λ2 are the borders of wavelength of the spectrum in UV and (or) visible region.
\n\n\nTable 3\n shows the corresponding coefficients for the dependencies (16) in different classes of organic molecules.
\nDependence | \n\n\n | \n|||||
---|---|---|---|---|---|---|
Homologous series | \nIP or EA | \nCoefficient of correlation equations | \nStatistic characteristics | \n|||
α1, eV | \nα2, 10−7 eV nm−1\n | \nCorrelation coefficient | \nMean-square deviation, eV | \nVariation coefficient, % | \n||
Polycyclic aromatic compounds | \nIP | \n8.074 | \n−0.0010256 | \n0.76 | \n0.22 | \n3.07 | \n
Polycyclic aromatic compounds | \nEA | \n0.290 | \n0.00064502 | \n0.71 | \n0.16 | \n2.22 | \n
Nitrogen-containing compounds [35] | \nIP | \n10.11 | \n−0.00250000 | \n0.88 | \n0.26 | \n2.46 | \n
Oxygen-containing compounds [35] | \nIP | \n11.03 | \n−0.00347000 | \n0.82 | \n0.32 | \n2.54 | \n
Coefficients of dependence (16) for homologous series.
Breakthrough research in this area was done in collaboration with Dr. D. Shulyakovskii, Dr. E. Kovaleva, Dr. G. Yarmuhamedova, N. Paimurzina, and K. Latypov [20, 21]. We established the following regularities, which connected the integral parameters of the spectrum with IP and EA (18)–(21).
\nIP is the effective ionization potential; EA is the effective electron affinity; Acv is integral autocorrelation function of the electron spectrum (IAFS) (23); μ is the relative empirical autocorrelation parameter (μ, parameter) (24); ε (v) is the density distribution function of the radiation absorption; v is the spectral frequency; \n
Group of organic semiconductor | \nConstants by (18) and (19)eV | \nConstants by (18) and (19), 10−17 eV s | \nDetermination coefficient, R2\n | \n|||
---|---|---|---|---|---|---|
γ1\n | \nχ1\n | \nγ2\n | \nχ2\n | \nIP | \nEA | \n|
Complex oxy-compounds | \n9.35 | \n0.08 | \n−1.96 | \n1.24 | \n0.90 | \n0.88 | \n
Ketones and aldehydes | \n10.65 | \n−0.02 | \n−2.98 | \n1.76 | \n0.85 | \n0.81 | \n
Constants by (20) and (21),eV | \nDetermination coefficient, R2\n | \n|||||
Polycyclic aromatic hydrocarbons | \n\nφ\n\n1\n\n | \n\nφ\n\n2\n\n | \n\nη\n\n1\n\n | \n\nη\n\n2\n\n | \n\nIP\n | \n\nEA\n | \n
5.43 | \n1.68 | \n1.88 | \n−1.36 | \n0.88 | \n0.87 | \n
Constants and determination coefficients for dependencies (14–16).
In the calculation of integral parameters using the autocorrelation function of the signal, we have used the techniques adopted in statistical physics and spectroscopy [29]. We presented the energy spectrum of the molecule in the form of the integral of the autocorrelation function (IACF), frequency-dependent transitions. The integral autocorrelation function (ACF) is defined by the following formula:
\nwhere \n
In [20] we proposed numerical parameter from IACP in the optical spectra was determined with the logarithmic function. The parameters of the ACF are because numbers are calculated using definite integral.
\nwhere \n
where the numerator of fraction is the integral autocorrelation function (IACF) in the UV spectral region; the denominator is IACF in the UV-vis spectral region; ν\n
The dependencies of IP and EA on the μ-factor for polycyclic aromatic hydrocarbons (PAH) of various classes (\nFigures 2\n and \n3\n) are established [20]. In addition, the dependencies of IP and EA on IACP for oxygen-containing compounds (alcohols, aldehydes, ketones) (\nFigures 4\n and \n5\n) are established [21].
\nRelationship of IP with the relative empirical autocorrelation parameter μ for PAH.
Relationship of EA with the relative empirical autocorrelation parameter μ for PAH.
Relationship of IP from IACF of organic oxygen groups containing molecules.
Relationship of EA from IACF of organic oxygen groups containing molecules.
IP and EA of organic molecules and PAH of different origin are presented in \nTables 5\n and \n6\n.
\nMolecules | \nμ-parameter | \nIP method DFT, eV | \nIP \nEq. (4), eV | \nEA method DFT, eV | \nEA \nEq. (5), eV | \n
---|---|---|---|---|---|
Hexahelicene | \n0.900 | \n6.97 | \n6.94 | \n0.64 | \n0.66 | \n
1.2,3.4,7.8-Tribenztetracene | \n0.820 | \n6.82 | \n6.81 | \n0.78 | \n0.77 | \n
Heptaphene | \n0.745 | \n6.60 | \n6.68 | \n0.88 | \n0.87 | \n
Pentacene | \n0.404 | \n6.07 | \n6.10 | \n1.30 | \n1.33 | \n
1,2-Benzpentacene | \n0.503 | \n6.18 | \n6.27 | \n1.23 | \n1.20 | \n
1,2-3,4-8,9-10,11-Tetrabenzpentacene | \n0.600 | \n6.44 | \n6.43 | \n1.10 | \n1.07 | \n
Naphtho-(2′.3′:3.4)-pyrene | \n0.647 | \n6.70 | \n6.52 | \n1.06 | \n1.00 | \n
3,4-Benznaphtho(2″,3″:8,9)-pyrene | \n0.472 | \n6.12 | \n6.71 | \n1.30 | \n0.84 | \n
3,4-Benznaphtho(2″,3″:9,10)-pyrene | \n0.531 | \n6.41 | \n6.20 | \n1.05 | \n1.26 | \n
1,14-4,5-Dibenzpentacene | \n0.765 | \n6.66 | \n6.69 | \n0.86 | \n0.86 | \n
1,2-Benzphenanthrene-(9′,10′:6,7)-pyrene | \n0.775 | \n6.73 | \n6.61 | \n0.78 | \n0.92 | \n
1,16-4,5-Dibenzhexacene | \n0.624 | \n6.28 | \n6.58 | \n1.20 | \n0.95 | \n
1,2-11,12-Dibenzperylene | \n0.378 | \n5.98 | \n6.06 | \n1.41 | \n1.37 | \n
1,12-2,3-Dibenzperylene | \n0.807 | \n6.81 | \n6.78 | \n0.76 | \n0.79 | \n
1,2-5,6-Dibenzcoronene | \n0.760 | \n6.76 | \n6.70 | \n0.84 | \n0.85 | \n
Calculated values of IP and EA.
Molecules | \nACF, 1015 Hz | \nIP method HF, eV | \nIP \nEq. (2), eV | \nEA method HF, eV | \nEA \nEq. (3), eV | \n
---|---|---|---|---|---|
1-Phenylacetylbutadiene | \n55.46 | \n9.01 | \n9.00 | \n0.99 | \n0.96 | \n
2-Furylpolyenoic acids C4H3O▬(CH〓CH)2COOH | \n61.49 | \n8.92 | \n8.95 | \n1.13 | \n1.06 | \n
Polyenoic acid | \n64.39 | \n8.77 | \n8.87 | \n1.11 | \n1.10 | \n
9-Oxoacridine | \n41.32 | \n8.38 | \n8.69 | \n1.15 | \n1.01 | \n
Calculated values of IP and EA.
EA and IP of organic molecules and semiconductors of different origins are presented in \nTables 2\n and \n3\n.
\nThus it is established that IP and EA of PAH, calculated by RHF 6-31G** and DFT methods, have the IACF dependence and μ-factor. These dependencies allow simplification of the estimations of IP and EA of organic molecules and PAH of different origin (\nTables 5\n and \n6\n).
\nThus, new methods for determining characteristics of electronic structure of different molecules and organic semiconductors are developed.
\nSubsequently, dependence (14) was confirmed by the study of IP and EA for various classes of sulfur and nitrogen organic compounds, organic dyes, amino acids, and biological fluids [24].
\nIn studies [2, 3, 4] for very complex multicomponent systems, the problem of determining the electronic structure and, consequently, chemical activity was solved.
\nThe characteristics of the chemical activity can be determined from the electron absorption spectra simplification. The authors introduced new values: effective IP and effective electron affinity [30]. The effective IP and EA are the averaged potentials of ionization and the electron affinity of the radiation-absorbing components.
\nThey allow to estimate the electron states of multicomponent and high-molecular substances, such as heavy residual resins of oil processing, high-molecular mixtures, and others.
\nDetermining the electronic structure of materials and nanomaterials is an important problem of molecular electronics. For this, EPS was used. This application of EPS to determine the electronic structure of high-molecular compounds of petroleum (petroleum asphaltenes) was proposed in our previous works (Dolomatov et al.) [2, 3, 4, 30].
\nThe asphaltenes are complex substances that can be found in crude oil, bitumen, and high-boiling hydrocarbon distillates. The asphaltenes are composed mainly of polyaromatic and heterocyclic compounds with traces of vanadium and nickel, which are in porphyrin structures. The electronic structure of asphaltenes has not been researched enough. The aim of research was to define the electronic structure of various asphaltenes. We have used the EPS methods. Some of the results are shown in \nTable 7\n.
\nAsphaltenes | \nEIP, eV | \nEEA, eV | \nBand gap energy, eV | \nQuasi Fermi level, eV | \n
---|---|---|---|---|
Asphaltenes of Radevski oil | \n5.70 | \n1.85 | \n3.85 | \n1.92 | \n
Asphaltenes of Surgut oil | \n5.20–5.70 | \n2.10–2.50 | \n3.10–3.20 | \n1.55–1.60 | \n
Asphaltenes of distillate fraction | \n4.37–5.27 | \n2.44–2.50 | \n1.93–2.77 | \n0.96–1.38 | \n
Hydrogenation asphaltenes of West Siberia oil | \n6.41 | \n2.66 | \n3.75 | \n1.85 | \n
Asphaltenes and resins of Surgut oil | \n5.34 | \n1.82 | \n3.52 | \n1.76 | \n
Asphaltenes of Kushkul oil | \n5.2 | \n1.90 | \n3.30 | \n1.65 | \n
The characteristics of the electronic structure of asphaltenes by EPS method.
Thus for asphaltenes, IP is in the interval from 4.37 up to 6.41 eV and EA differs from 1.82 to 2.66 eV. The size of energy band gap from 1.93 to 3.85 eV indicates that oil asphaltenes belong to amorphous, compensated, wideband semiconductors. The experiments for band gap estimation of the asphaltene molecules were confirmed by electronic structure computing with ab initio methods. The main deduction from this research is that oil asphaltenes can be used as organic semiconductors.
\nThe research [7] (co-author Dr. Shulyakovskaya D. and Dr. Yarmuhametova G.) established the phenomenon of the relationship between the energy of the molecular orbital, which characterizes the IP and EA, and color properties.
\nwhere Е is energy of the boundary molecular orbital (IP or EA), eV; α1 and α2 are empirically determined coefficients eV; q is one of the color characteristics (CCs) for standard light source A, B, C, or D; and CCs can be represented in one of the international color measurement systems (e.g., color coordinates or chromaticity coordinates in XYZ or RGB systems). The color coordinates of polycyclic aromatic hydrocarbons in the XYZ system are shown (\nFigure 6\n). These coordinates are calculated in the visible region of the transmission spectra of hydrocarbon solutions according to the formulas (2)–(7).
\nColor characteristics (chromaticity coordinates x and y) of the individual aromatic oil components in XYZ colorimetric system: (1) perylene, (2) tetrabenzpentacene, (3) dibenzpyrene, (4) hexabenzcoronene, (5) 1,2-benzphenantrenopyrene, (6) 2,3-benzperylene, (7) dibenzpentacene, (8) phenantrenopyrene, (9) ovalen, (10)–(12) dibenzperylenes, (13) dibenzpyrene, (14) dinaphtpyrene, (15) tetrabenzheptacene, (16) benzanathtpyrene, (17) dibenzanthanthrene, (18) bisantene, (19) benzanathtpyrene, (20) benzbisantene, (21) dinathteptacene, (22) 1, 2-benzanaphtpyrene, (23) dibenzperylene, (24) dibenzanthanthrene, (25) benzperylene, (26) dinaphtpyrene, and (27) tetrabenzheptacen.
Several classes of compounds, including PAH, were studied by dependence (25). The corresponding coefficients for IP and EA are presented in \nTables 8\n and \n9\n. As can be seen from the tables, the accuracy of the assessment of ionization potentials and electron affinity is satisfactory. Thus, the effect of the relationship between IP and EA on the color characteristics can be used to simultaneously measure these physical quantities.
\nOrganic semiconductor class | \nCCs | \nCoefficients for IP | \nCorrelation coefficients | \nVariation coefficients (%) | \nStandard deviation (eV) | \n|
---|---|---|---|---|---|---|
А1 (eV) | \nА0 (eV) | \n|||||
Semiconductors containing three and five linear annelated benzene rings and semiconductors of perylene series | \n\nZC\n\n | \n−0.0120 | \n8.2188 | \n0.90 | \n3.10 | \n0.23 | \n
\nZD\n\n | \n−0.0129 | \n8.2035 | \n0.89 | \n3.13 | \n0.23 | \n|
\nBC\n\n | \n−0.0023 | \n8.2256 | \n0.89 | \n3.17 | \n0.24 | \n|
\nBD\n\n | \n−0.0024 | \n8.2110 | \n0.89 | \n3.20 | \n0.24 | \n|
Semiconductor of bisantene series and anthanthrene | \n\nzC\n\n | \n4.7985 | \n3.7638 | \n0.94 | \n4.44 | \n0.31 | \n
\nzD\n\n | \n4.5947 | \n3.9328 | \n0.94 | \n4.47 | \n0.31 | \n|
\nbB\n\n | \n4.2833 | \n3.4563 | \n0.94 | \n4.71 | \n0.32 | \n|
\nbC\n\n | \n5.3597 | \n2.4476 | \n0.94 | \n4.52 | \n0.31 | \n|
Semiconductors of pyrene series | \n\nxC\n\n | \n−4.2636 | \n7.8232 | \n0.87 | \n2.85 | \n0.20 | \n
\nxD\n\n | \n−4.2503 | \n7.8231 | \n0.86 | \n2.88 | \n0.20 | \n|
\n | \n\nRA\n\n | \n−0.0110 | \n7.2866 | \n0.87 | \n2.86 | \n0.20 | \n
\nRB\n\n | \n−0.0148 | \n7.3764 | \n0.87 | \n2.80 | \n0.20 | \n|
Heterocyclic semiconductors | \n\nyC\n\n | \n−1.9421 | \n7.7117 | \n0.94 | \n1.92 | \n0.14 | \n
\nyD\n\n | \n−1.8822 | \n7.7100 | \n0.93 | \n1.96 | \n0.14 | \n|
\ngA\n\n | \n−1.1612 | \n7.5854 | \n0.91 | \n2.32 | \n0.17 | \n|
\ngB\n\n | \n−1.2637 | \n7.5599 | \n0.90 | \n2.41 | \n0.18 | \n
Organic semiconductor class | \nCCs | \nCoefficients (25) for EA | \nCorrelation coefficients | \nVariation coefficients (%) | \nStandard deviation (eV) | \nSample volume (pcs) | \n|
---|---|---|---|---|---|---|---|
B1 (eV) | \nB0 (eV) | \n||||||
Semiconductors containing three and five linear annelated benzene rings and semiconductors of perylene series | \n\nZC\n\n | \n0.0049 | \n0.6344 | \n0.90 | \n9.92 | \n0.09 | \n29 | \n
\nZD\n\n | \n0.0053 | \n0.6407 | \n0.89 | \n10.01 | \n0.10 | \n||
\nBC\n\n | \n0.0009 | \n0.6316 | \n0.89 | \n10.77 | \n0.10 | \n||
\nBD\n\n | \n0.0010 | \n0.6376 | \n0.89 | \n10.88 | \n0.10 | \n||
Semiconductor of bisantene series and anthanthrene | \n\nzC\n\n | \n−1.9716 | \n2.4650 | \n0.94 | \n10.65 | \n0.13 | \n11 | \n
\nzD\n\n | \n−1.8879 | \n2.3955 | \n0.94 | \n10.71 | \n0.13 | \n||
\nbB\n\n | \n−1.7597 | \n2.5912 | \n0.94 | \n11.29 | \n0.13 | \n||
\nbC\n\n | \n−2.2019 | \n3.0056 | \n0.94 | \n10.85 | \n0.13 | \n||
Semiconductors of pyrene series | \n\nxC\n\n | \n1.7519 | \n0.7970 | \n0.87 | \n7.62 | \n0.08 | \n20 | \n
\nxD\n\n | \n1.7464 | \n0.7970 | \n0.86 | \n7.68 | \n0.08 | \n||
\nRA\n\n | \n0.0045 | \n1.0175 | \n0.87 | \n7.64 | \n0.08 | \n||
\nRB\n\n | \n0.0061 | \n0.9806 | \n0.87 | \n7.49 | \n0.08 | \n||
Heterocyclic semiconductors | \n\nyC\n\n | \n0.7978 | \n0.8430 | \n0.94 | \n5.71 | \n0.06 | \n15 | \n
\nyD\n\n | \n0.7732 | \n0.8437 | \n0.93 | \n5.82 | \n0.06 | \n||
\ngA\n\n | \n0.4769 | \n0.8949 | \n0.91 | \n6.89 | \n0.07 | \n||
\n | \n\ngB\n\n | \n0.5190 | \n0.9054 | \n0.90 | \n7.17 | \n0.07 | \n\n | \n
The dependence of the IP on the chromatic coordinate-Z in the XYZ system for PAH based on three and five linear annular benzene rings and from the perilene series is shown in \nFigure 7\n.
\nThe correlation of the first PI and the color characteristic for the compounds with three and five linear annulary benzene rings and from the perilene series. (1) 2,3-benzpizene, (2) 1,12-2,3-8,9-tribenzperylene, (3) 1,12-2,3-dibenzperylene; (4) anthracene [2′,1′:1,2] anthracene; (5) coronene; (6) 2,3–8, 9-dibenzpizene; (7) 3,4-benzpentaphene; (8) pentaphene; (9) perilene; (10) naphtha[2′, 3′:3, 4]pentaphene; (11) 1,12–0-phenylenperilene; (12) 1,2-benzcoronene; (13) 1,2-3,4-5,6-10,11-tetrabenzanthracene; (14) 2,3-8,9-dibenzpizene; (15) 1,2-7,8-dibenzcoronene; (16) 1,12–0-phenyl-2,3-10,11-dibenzperilene; (17) naphtha [2′, 3′:1, 2] coronene; (18) 2,3–10, 11-dibenzperylene; (19) 2,3-benzperylene; (20) 1,2-3,4-5,6-tribenzcoronene; (21) anthracene [2′, 1′,1, 2]tetraphene; (22) 1,2-benzperylene; (23) 1,2-10,11-dibenzperylene; (24) 1,2-3,4-8,9-10,11-tetrabenzpentazene; (25) 1,2-11,12-dibenzperylene; (26) 1,2-8,9-dibenzpentazene; (27) 1,2-benzpentazene; (28) pentazene; and (29) 1,2-7,8-dibenzpizene.
The IP and EA values for various organic molecules obtained by the dependence (11) are confirmed by various modifications of quantum DFT and ab initio methods. In addition, the values of IP were estimated by photoelectron spectroscopy. The results are shown in \nTables 10\n and \n11\n.
\nOrganic semiconductor class | \nSemiconductor name | \nElectron affinity, eV | \nAbs. accuracy, eV\n | \nRel. accuracy, % | \n|
---|---|---|---|---|---|
Regular methods | \nAcc. to CCs | \n||||
Semiconductors containing three and five linear annelated benzene rings and semiconductors of perylene series | \nPentaphene | \n0.85 | \n0.78 | \n0.07 | \n8.24 | \n
Anthraceno[2′,1′:1,2]anthracene | \n0.73 | \n0.77 | \n0.04 | \n5.75 | \n|
2,3-Benzpicene | \n0.62 | \n0.70 | \n0.08 | \n12.81 | \n|
Anthraceno[2′,1′:1,2]tetraphene | \n1.05 | \n1.12 | \n0.06 | \n6.05 | \n|
Pentacene | \n1.19 | \n1.23 | \n0.04 | \n3.36 | \n|
1,2-Benzpentacene | \n1.13 | \n1.22 | \n0.09 | \n7.71 | \n|
1,2-3,4-8,9-10,11-Tetrabenzpentacene | \n1.16 | \n1.20 | \n0.05 | \n4.23 | \n|
1,2-Benzperylene | \n1.19 | \n1.15 | \n0.04 | \n3.55 | \n|
1.2–10.11-Dibenzperylene | \n1.09 | \n1.16 | \n0.07 | \n6.36 | \n|
1.2–11.12-Dibenzperylene | \n1.19 | \n1.22 | \n0.03 | \n2.59 | \n|
1,12-2,3-Dibenzperylene | \n0.66 | \n0.72 | \n0.06 | \n9.75 | \n|
1,12-2,3-8,9-Tribenzperylene | \n0.73 | \n0.72 | \n0.01 | \n0.92 | \n|
1,2-3,4-5,6-Tribenzcoronene | \n1.12 | \n1.11 | \n0.01 | \n1.13 | \n|
Semiconductor of bisantene series and anthanthrene | \nBisantene | \n1.69 | \n1.71 | \n0.02 | \n1.33 | \n
1,14-Benzbisantene | \n1.11 | \n1.18 | \n0.07 | \n6.32 | \n|
3,4-11,12-Dibenzbisantene | \n0.88 | \n0.93 | \n0.05 | \n5.44 | \n|
3,4–10.11-Dibenzbisantene | \n0.99 | \n0.95 | \n0.04 | \n4.27 | \n|
1,2-3,4-8,9-10,11-Tetrabenzbisantene | \n0.89 | \n0.97 | \n0.08 | \n9.03 | \n|
Anthanthrene | \n1.04 | \n0.92 | \n0.12 | \n11.64 | \n|
1,2-7,8-Dibenzanthanthrene | \n0.95 | \n0.95 | \n0.00 | \n0.02 | \n|
Semiconductors of pyrene series | \n3,4–8.9-Dibenzpyrene | \n1.08 | \n1.03 | \n0.04 | \n3.91 | \n
3,4-9,10-Dibenzpyrene | \n1.01 | \n1.02 | \n0.02 | \n1.59 | \n|
3,4-Benzanaft[2″,3″:8.9]pyrene | \n1.02 | \n1.02 | \n0.00 | \n0.36 | \n|
3,4-Benzanaft[2″,3″:9,10]pyrene | \n0.97 | \n1.03 | \n0.06 | \n5.93 | \n|
Dinaft[2′,3′:3,4]-[2″,3″:9,10]pyrene | \n1.00 | \n1.01 | \n0.01 | \n0.97 | \n|
1,14-4,5-Dibenzpetacene | \n1.10 | \n1.04 | \n0.07 | \n6.02 | \n|
\n | \n5,6-15,16-Dibenzhexacene | \n1.06 | \n1.04 | \n0.03 | \n2.65 | \n
Naft[1′,7′:2,16]hexacene | \n1.40 | \n1.40 | \n0.00 | \n0.16 | \n|
1,18-4,5-9,10-13,14-Tetrabenzheptacene | \n1.02 | \n1.02 | \n0.00 | \n0.15 | \n|
Heterocyclic semiconductors | \n9-Anthracentiol | \n0.91 | \n0.93 | \n0.02 | \n2.38 | \n
2,2′;5′,2”-Tertienil | \n0.83 | \n0.87 | \n0.05 | \n5.46 | \n|
2-Tiapyranthion | \n1.26 | \n1.30 | \n0.04 | \n3.22 | \n|
1,3-Ditiolene-2-thione | \n0.83 | \n0.90 | \n0.07 | \n8.78 | \n|
4,5-Cyclohexeceno-1.3-ditiolene-2-thione | \n0.85 | \n0.91 | \n0.06 | \n7.26 | \n|
4-Phenyl-1,3-ditiolene-2-thione | \n0.94 | \n0.90 | \n0.04 | \n4.23 | \n|
Nafto[1,2-b]-1,3-ditiolene-2-thione | \n0.97 | \n0.90 | \n0.07 | \n7.48 | \n|
4,5-Cyclopenteno-1.2-ditiolene-3-thione | \n1.18 | \n1.12 | \n0.06 | \n5.12 | \n|
\n | \n4,5-Cyclohexeceno-1,2-ditiolene-3-thione | \n1.12 | \n1.12 | \n0.00 | \n0.21 | \n
4,5-Cyclohepteno-1,2-ditiolene-3-thione | \n1.18 | \n1.13 | \n0.06 | \n4.75 | \n|
\n | \nThiolane-3,4-dithion | \n1.01 | \n1.00 | \n0.01 | \n1.47 | \n
Results of determining electron affinity of some organic semiconductors [7].
Organic semiconductor class | \nSemiconductor name | \nIonization potential, eV | \nAbs. accuracy, eV | \nRel. accuracy, % | \n|
---|---|---|---|---|---|
Regular methods | \nAcc. to CCs | \n||||
Semiconductors containing three and five linear annelated benzene rings and semiconductors of perylene series | \nPentaphene | \n7.70 | \n7.87 | \n0.17 | \n2.22 | \n
Anthraceno[2′,1′:1,2]anthracene | \n7.99 | \n7.88 | \n0.10 | \n1.26 | \n|
2,3-Benzpicene | \n8.26 | \n8.07 | \n0.19 | \n2.34 | \n|
Anthraceno[2′,1′:1,2]tetraphene | \n7.20 | \n7.04 | \n0.15 | \n2.14 | \n|
Pentacene | \n6.87 | \n6.77 | \n0.10 | \n1.41 | \n|
\n | \n1,2-Benzpentacene | \n7.01 | \n6.80 | \n0.21 | \n3.01 | \n
1,2-3,4-8,9-10,11-Tetrabenzpentacene | \n6.95 | \n6.83 | \n0.12 | \n1.72 | \n|
1,2-Benzperylene | \n6.87 | \n6.97 | \n0.10 | \n1.48 | \n|
1.2–10,11-Dibenzperylene | \n7.12 | \n6.95 | \n0.17 | \n2.38 | \n|
1,2-11,12-Dibenzperylene | \n6.87 | \n6.79 | \n0.08 | \n1.10 | \n|
1,12-2,3-Dibenzperylene | \n8.16 | \n8.00 | \n0.16 | \n1.92 | \n|
1,12-2,3-8,9-Tribenzperylene | \n7.99 | \n8.00 | \n0.02 | \n0.20 | \n|
1,2-3,4-5,6-Tribenzcoronene | \n7.03 | \n7.06 | \n0.03 | \n0.42 | \n|
Semiconductor of bisantene series and anthanthrene | \nBisantene | \n5.66 | \n5.60 | \n0.05 | \n0.96 | \n
1,14-benzbisantene | \n7.06 | \n6.89 | \n0.17 | \n2.42 | \n|
3,4-11,12-Dibenzbisantene | \n7.61 | \n7.50 | \n0.12 | \n1.53 | \n|
3,4–10.11-Dibenzbisantene | \n7.35 | \n7.46 | \n0.10 | \n1.40 | \n|
\n | \n1,2-3,4-8,9-10,11-Tetrabenzbisantene | \n7.60 | \n7.41 | \n0.19 | \n2.56 | \n
Anthanthrene | \n7.24 | \n7.53 | \n0.29 | \n4.06 | \n|
1,2-7,8-Dibenzanthanthrene | \n7.46 | \n7.46 | \n0.00 | \n0.02 | \n|
Semiconductors of pyrene series | \n3,4-8,9-Dibenzpyrene | \n7.14 | \n7.25 | \n0.10 | \n1.44 | \n
Semiconductors of pyrene series | \n3,4-8,9-Dibenzpyrene | \n7.14 | \n7.25 | \n0.10 | \n1.44 | \n
3,4-9,10-Dibenzpyrene | \n7.32 | \n7.28 | \n0.04 | \n0.54 | \n|
3,4-Benzanaft[2″,3″:8,9]pyrene | \n7.29 | \n7.28 | \n0.01 | \n0.13 | \n|
3,4-Benzanaft[2″,3″:9,10]pyrene | \n7.40 | \n7.25 | \n0.14 | \n1.90 | \n|
Dinaft[2′,3′:3,4]-[2″,3″:9,10]pyrene | \n7.33 | \n7.31 | \n0.02 | \n0.32 | \n|
\n | \n1,14-4,5-Dibenzpetacene | \n7.08 | \n7.24 | \n0.16 | \n2.28 | \n
5,6-15,16-Dibenzhexacene | \n7.17 | \n7.24 | \n0.07 | \n0.98 | \n|
Naft[1′,7′:2,16]hexacene | \n6.35 | \n6.36 | \n0.01 | \n0.10 | \n|
1,18-4,5-9,10-13,14-Tetrabenzheptacene | \n7.29 | \n7.28 | \n0.00 | \n0.07 | \n|
Heterocyclic semiconductors | \n9-Anthracentiol | \n7.54 | \n7.49 | \n0.05 | \n0.71 | \n
2,2′;5′,2”-Tertienil | \n7.75 | \n7.64 | \n0.11 | \n1.40 | \n|
2-Tiapyranthion | \n6.69 | \n6.60 | \n0.10 | \n1.46 | \n|
1,3-Ditiolene-2-thione | \n7.76 | \n7.58 | \n0.18 | \n2.27 | \n|
4,5-Cyclohexeceno-1,3-ditiolene-2-thione | \n7.69 | \n7.54 | \n0.15 | \n1.95 | \n|
4-Phenyl-1,3-ditiolene-2-thione | \n7.48 | \n7.58 | \n0.10 | \n1.29 | \n
Results of determining of the first ionization potentials of some organic semiconductors [7].
From the received results, it follows that the equation is distributed to substances with IP < 9.8 eV, i.e., it covers the majority of organic substances.
\nThus, it can be concluded that the effects (21)–(25) discovered by us allow us to estimate the energy levels of quantum systems with sufficient accuracy. This is important for the study of multi-electron systems in molecular electronics and nanotechnology and chemistry such as single molecules, atomic clusters, and high-molecular systems. From here it follows that electronic spectra and color characteristics can be applied to the definition of various characteristics of substances.
\nThis cycle of works is described [22, 23, 24, 25, 26] and executed together with Dr. N. Kalashchenko and Dr. S. Dezortsev. The experiments were conducted at the Ufa Medical University and the Republican Clinic named after Kuvatov (Ufa, Russia).
\nColorimetric studies of blood are actively used in medicine [31], criminal law [32], and the food industry [33, 34]. In medical practice, colorimetric methods are used to determine the hemoglobin concentration in the blood of a patient (the color index) [35]. Today a rather exact (±1%) cyanomethemoglobin photometric method is used everywhere, in which cyanomethemoglobin is determined at a wavelength of 540 nm after preparation of a working solution of the blood in Drabkin reagent. Various modifications of this method do not change its essential physical nature [6]. Furthermore, spectral analysis in the visible region has been used to determine oxyhemoglobin and other hemoglobin-containing compounds from the absorption spectra of blood and its solutions [36]. Despite this, the quantitative colorimetric characteristics of blood have not been studied before.
\nThe aim of this work was to study the color characteristics of hemolyzed blood, plasma, and serum from donors in the visible range of the absorption spectra by standard CIE methods (International Commission on Illumination, 1964).
\nThe basic color characteristics (lightness and chromaticity coordinates) determine the position of the color of the specimen in an arbitrary color space and are found by the CIE method [11, 12].
\nThe familiar spectrophotometric method for color measurements involves measuring the spectral power distribution of the radiation followed by calculation of the color coordinates by multiplying the determined spectral power distribution function times the three color-matching functions and then integrating the products. For the spectral power distribution function of the source E(λ), the spectral transmittance function τ(λ), and x(λ), y(λ), and z(λ) (the color-matching functions) and the color coordinates X, Y, and Z are determined by integration over the wavelength range for visible radiation 380–760 nm. In practice, integration is replaced by summation over the interval dλ (from 5 to 10 nm), since the spectral functions under the integral sign are usually not easily integrated:
\nThe spectral power distribution and the spectral transmittance curve are measured by separating light into a spectrum, such as in a spectrophotometer or monochromator. The color-matching curves are specified as tables of values of the specific coordinates in 10 nm steps. There are also tables of E(λ)x(λ) values for standard CIE light sources A, B, C, and D, characterizing the most typical natural (B, C, D) and artificial (A) illumination conditions.
\nThe chromaticity coordinates are calculated using the formulas.
\nThe coordinate Y characterizes the lightness (luminance) of the specimens.
\nThe quantitative colorimetric characteristics of hemolyzed blood, plasma, and serum described by formulas (26) and (27) in the standard CIE method are connected with the transmittance or reflectance spectra and are integrated parameters determined over the entire visible region of the electromagnetic spectrum. So it is assumed that they carry information about the condition of the entire body. In our approach, blood and its components are considered as a single, indivisible light-absorbing system.
\nThe experiment. The objects of investigation were solutions of hemolyzed blood and solutions of plasma and serum (prepared from that blood) of the same concentration from 100 male and female donors (in different blood groups and age groups) and from 95 patients who were assigned to three arbitrary groups: (I) 41 patients with purulent diseases (osteomyelitis, purulent fistulas, gonitis), (II) 41 resuscitated patients (acute myocardial infarction, acute cerebral circulatory collapse, chronic cardiac insufficiency), and (III) 13 patients with cirrhosis of the liver. We determined the color characteristics of the “average” donor (without separating the donors according to blood, sex, and age groups) and compared them with the analogous characteristics of patients from the different groups. The blood for the studies was drawn at a blood donation center and in clinical departments by standard procedures [37].
\nThe spectra of the solutions of hemolyzed blood, plasma, and serum of concentration 2.5 vol.% (1.40) were taken in quartz cuvets with thickness of the working layer of liquid equal to 10 mm at room temperature, on an SF-2000 spectrophotometer in the range 200–1000 nm in 20 nm steps. As the solvent and the reference solution, we used distilled water for injection, which is optically neutral under the experimental conditions and is the natural physiological solvent in the human body. The hemolyzed blood was prepared using a standard heparin solution, and the plasma was prepared using the preservative Glyugitsir.
\nIn addition to the averaged values of the color coordinates and the lightness value, we calculated the standard deviation, the confidence interval for significance level α = 95%, and the coefficient of variation.
\n\n\nFigure 8\n shows the averaged spectra for the hemolyzed blood, plasma, and serum from the patients in all three examined groups compared with the corresponding averaged spectra of the donors. There are clear differences between the different groups of patients.
\nSpectra of hemolyzed donor blood (1), plasma (2), and serum (3) in the UV and visible regions (averaged over 100 donors).
For the plasma and serum, over the entire studied region, the group spectra for the patients lie higher than the averaged spectra of the donors, and their positional order is consistent: the averaged spectrum for patients with purulent diseases lies above the averaged spectrum for the donors, the averaged spectrum for the resuscitation patients lies above that spectrum, and the averaged spectrum for patients with cirrhosis of the liver lies even higher. Probably such positioning of the spectra reflects the severity of the general condition of the patients, if we assume that cirrhosis is the most severe condition for the patients with the least likelihood of recovery. We do not observe such a dependence for the hemolyzed blood: the averaged spectrum for the patients with purulent diseases lies below the averaged spectrum for the donors (\nFigure 9\n).
\nAveraged spectra of hemolyzed blood (a), plasma (b), and serum (c) of examined groups of patients (▲ = donors, ¨ = I, ¤ = II, × = III) compared with averaged spectrum of hemolyzed blood, plasma, and serum, respectively, from donors [25].
\n\nTable 12\n gives the averaged color coordinates and lightness for the donors and each group of examined patients, calculated for the solutions of blood, plasma, and serum as a single light-absorbing system according to the standard CIE method. For the donors, the chromaticity coordinate x varies from 0.320 ± 0.001 (for serum and plasma) to 0.630 ± 0.008 (for hemolyzed blood). The coefficients of variation in this case also decrease from 4.7 for blood down to 1.3 for serum. The chromaticity coordinate y for the donors has similar values: 0.320 ± 0.002 for plasma and serum and 0.340 ± 0.003 for blood. The coefficients of variation for y steadily decrease from 3.2 for blood down to 2.0 for serum. The parameter z for the donors is higher for serum and plasma (0.360 ± 0.003) than for blood (0.030 ± 0.005). The coefficient of variation for this parameter is maximum for blood (67.6) and minimum for serum (2.5). The lightness, as expected, has the maximum value (84.88–1.54) for serum and the minimum value (11.55–0.67) for hemolyzed blood. For plasma, this parameter is close to the value typical of serum.
\nParameter | \nHemolyzed blood | \nPlasma | \nSerum | \n|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Donors | \nI | \nII | \nIII | \nDonors | \nI | \nII | \nIII | \nDonors | \nI | \nII | \nIII | \n|
Chromaticity coordinate x\n | \n||||||||||||
Mean value | \n0.63 | \n0.59 | \n0.64 | \n0.34 | \n0.32 | \n0.335 | \n0.33 | \n0.35 | \n0.32 | \n0.32 | \n0.32 | \n0.35 | \n
Standard deviation | \n0.029 | \n0.041 | \n0.032 | \n0.048 | \n0.005 | \n0.009 | \n0.013 | \n0.02 | \n0.004 | \n0.009 | \n0.016 | \n0.019 | \n
Confidence interval, α = 0.95 | \n0.01 | \n0.01 | \n0.01 | \n0.02 | \n0.001 | \n0.002 | \n0.004 | \n0.01 | \n0.001 | \n0.002 | \n0.005 | \n0.009 | \n
Coefficient of variation | \n4.7 | \n6.9 | \n5.46 | \n8.11 | \n1.6 | \n2.65 | \n4.13 | \n5.82 | \n1.3 | \n2.9 | \n4.93 | \n5.5 | \n
Chromaticity coordinate y\n | \n||||||||||||
Mean value | \n0.34 | \n0.35 | \n0.35 | \n0.36 | \n0.32 | \n0.33 | \n0.34 | \n0.36 | \n0.32 | \n0.33 | \n0.34 | \n0.37 | \n
Standard deviation | \n0.011 | \n0.013 | \n0.011 | \n0.014 | \n0.008 | \n0.011 | \n0.011 | \n0.019 | \n0.006 | \n0.011 | \n0.012 | \n0.019 | \n
Confidence interval, α = 0.95 | \n0.003 | \n0.003 | \n0.003 | \n0.007 | \n0.002 | \n0.003 | \n0.004 | \n0.009 | \n0.002 | \n0.003 | \n0.004 | \n0.009 | \n
Coefficient of variation | \n3.2 | \n3.67 | \n3.07 | \n3.98 | \n2.6 | \n3.36 | \n3.37 | \n5.27 | \n2.0 | \n3.55 | \n3.61 | \n5.22 | \n
Chromaticity coordinate z\n | \n||||||||||||
Mean value | \n0.03 | \n0.005 | \n0.05 | \n0.06 | \n0.36 | \n0.34 | \n0.33 | \n0.29 | \n0.36 | \n0.35 | \n0.34 | \n0.29 | \n
Standard deviation | \n0.02 | \n0.028 | \n0.024 | \n0.034 | \n0.012 | \n0.017 | \n0.024 | \n0.039 | \n0.009 | \n0.019 | \n0.026 | \n0.038 | \n
Confidence interval, α = 0.95 | \n0.005 | \n0.007 | \n0.008 | \n0.017 | \n0.003 | \n0.004 | \n0.008 | \n0.019 | \n0.003 | \n0.005 | \n0.008 | \n0.019 | \n
Coefficient of variation | \n67.6 | \n53.77 | \n53.16 | \n61.58 | \n3.3 | \n4.98 | \n7.29 | \n13.46 | \n2.5 | \n5.53 | \n7.76 | \n13.26 | \n
Lightness L, % | \n||||||||||||
Mean value | \n11.55 | \n14.82 | \n12.99 | \n13.46 | \n78.94 | \n67.82 | \n63.04 | \n36.66 | \n84.88 | \n79.34 | \n72.42 | \n48.08 | \n
Standard deviation | \n2.46 | \n3.5 | \n2.72 | \n5.8 | \n8.17 | \n12.42 | \n14.79 | \n19.65 | \n5.58 | \n10.21 | \n16.46 | \n22.77 | \n
Confidence interval, α = 0.95 | \n0.67 | \n0.9 | \n0.85 | \n2.84 | \n2.19 | \n3.11 | \n4.62 | \n9.63 | \n1.54 | \n2.62 | \n5.14 | \n11.15 | \n
Coefficient of variation | \n21.3 | \n23.86 | \n21.38 | \n43.07 | \n10.3 | \n18.11 | \n23.92 | \n53.6 | \n6.6 | \n13.03 | \n23.2 | \n47.35 | \n
In determining the color range (see \nFigure 9a\n) for the dilute solutions (1.40) of hemolyzed blood, plasma, and serum from the donors, the color range of blood falls within the red region of the spectrum; the range for plasma and serum falls within the yellow region with lower saturation, which supports the correctness of our experiments and calculations. The corresponding regions for the color range for the patients cover a larger area than for the donors (\nFigure 9b\n). In order to better visualize the results obtained, we calculated the color coordinates for the studied specimens with correction for concentration. All the points for the donors lie within the yellow-orange region with saturation of 30–50%, which corresponds to the visual observations.
\nThe average values of the color coordinates for all the patient groups (see \nTable 12\n) are virtually no different from the averages for the donors except for patients with cirrhosis of the liver, for which the bilirubin blood concentration sharply increases. As a result of this, the plasma and serum take on a saturated orange color, which is reflected in the spectra and accordingly in their color characteristics. For the same reason, the lightness of the plasma and serum for these patients is much lower (by almost a factor of two) than for the rest. However, we do not observe sharp differences in the spectra of the hemolyzed blood.
\nThe lightness of the plasma and serum depends on the condition of the patients. Thus the average values of this parameter for the plasma decrease as the severity of the diseases increases: from 78.94 for the donors up to 67.82 for septic patients, 63.04 for resuscitated patients, and 36.66 for patients with cirrhosis of the liver.
\nIn the future, we performed new experiments in which the possibility of diagnosis of liver cirrhosis by color characteristics of blood was considered [23].
\nThe color characteristics of the samples of blood plasma were calculated after processing spectra. The selective figures of the chromaticity coordinates of patients with liver cirrhosis and healthy people are shown in \nTable 13\n.
\nSamples of blood plasma | \nPatients with liver cirrhosis | \nHealthy people | \n||
---|---|---|---|---|
x | \ny | \nx | \ny | \n|
1 | \n0.348 | \n0.350 | \n0.322 | \n0.324 | \n
2 | \n0.345 | \n0.348 | \n0.324 | \n0.326 | \n
3 | \n0.341 | \n0.343 | \n0.318 | \n0.315 | \n
4 | \n0.383 | \n0.373 | \n0.331 | \n0.325 | \n
5 | \n0.348 | \n0.357 | \n0.321 | \n0.315 | \n
6 | \n0.339 | \n0.348 | \n0.323 | \n0.317 | \n
7 | \n0.360 | \n0.368 | \n0.315 | \n0.316 | \n
8 | \n0.350 | \n0.363 | \n0.322 | \n0.326 | \n
9 | \n0.372 | \n0.375 | \n0.33 | \n0.331 | \n
10 | \n0.342 | \n0.347 | \n0.309 | \n0.31 | \n
11 | \n0.344 | \n0.346 | \n0.313 | \n0.311 | \n
12 | \n0.359 | \n0.356 | \n0.326 | \n0.325 | \n
13 | \n0.345 | \n0.347 | \n0.317 | \n0.318 | \n
14 | \n0.357 | \n0.359 | \n0.314 | \n0.317 | \n
15 | \n0.353 | \n0.360 | \n0.319 | \n0.325 | \n
The chromaticity coordinates (x and y) of patients with liver cirrhosis and healthy people [25].
Then the statistical analysis of the data was made. The basic statistics for all the investigated samples are shown in \nTable 14\n.
\nStatistical indicators | \nPatients with liver cirrhosis | \nHealthy people | \n||
---|---|---|---|---|
x | \ny | \nx | \ny | \n|
Mean | \n0.352 | \n0.356 | \n0.320 | \n0.320 | \n
Confidence interval | \n0.006 | \n0.005 | \n0.001 | \n0.002 | \n
Dispersion, σ | \n0.02 | \n0.02 | \n0.005 | \n0.008 | \n
Error of mean | \n0.003 | \n0.002 | \n0.001 | \n0.001 | \n
Variation coefficient | \n5.62 | \n4.80 | \n1.6 | \n2.6 | \n
The statistical characteristics of chromaticity coordinates for blood plasma of healthy individuals and patients with liver cirrhosis in the XYZ system [25].
Totalities of samples have a distribution close to normal and similar values of dispersion; therefore the t-test can be used to assess the reliability of the results. T of t-test for the chromaticity coordinate x was 10.57, for y—12.9. The critical value of t for confidence probability p = 0.999 is 3.5, which is much smaller than the obtained results. Consequently, the differences between chromaticity coordinates of groups of patients and donors were statistically significant.
\nThe differences between the color characteristics of blood plasma of patients with liver cirrhosis and healthy subjects are shown in \nFigure 10\n.
\nBlood plasma of patients with liver cirrhosis and healthy people on the chromaticity diagram of system XYZ (Source C) [24, 25].
Colorimetric method established that a healthy person is characterized by the following indicators of chromaticity coordinates:
\nPatients with liver cirrhosis are characterized by the following color characteristics: x = x = 0.352 ± 0.006 y = 0.356 ± 0.005.
\nHaving made the statistical processing, the data revealed that color characteristics of blood plasma of patients with liver cirrhosis differ from color characteristics of blood plasma of healthy people with a high degree of reliability.
\nInvestigation of human blood plasma by colorimetric methods can be used to express diagnosis of liver cirrhosis. A healthy person is characterized by the following indicators of chromaticity coordinates: x = 0.32 ± 0.001, a = 0.32 ± 0.002. Patients with cirrhosis of the liver are characterized by the following color characteristics: x = 0.352 ± 0.006 a = 0.356 ± 0.005.
\nHence based on the integrated absorption spectra according to the standard CIE system, using the absorption coefficient for radiation in the visible wavelength range, we quantitatively determined the normal and pathological average color characteristics of human blood and its components (plasma and serum). The condition of the body is most adequately described using the lightness parameter for the aqueous solutions of plasma and serum. The method can be used in medical practice for rapid health assessment.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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