\r\n\tThis book aims to explore the issues around the rheology of polymers, with an emphasis on biopolymers as well as the modification of polymers using reactive extrusion.
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\n
1. Introduction
\n
Seismologists try to help human beings across the globe by taking different strategies and techniques to be aware of the most dangerous disaster, earthquake (EQ). The first usage of seismology dated back to 100 years ago as a recorder of EQ. Earthquake is the result of the faults mutation and their failure process. For failure of the faults, a massive energy has to be collected to the level where friction is broken down. Recently, seismologists have realized the source of energy. Activities of the flexible plates located at the earth surface contribute in coolness of the globe by heat transfer. Physically, the degree which is recognized an occurrence is frequently evaluated by how truly it can be predicted. Consequently, the request is not how EQ can be detected; it is preferably how accurate they can be predicted. No scientific estimation is feasible without precise description of the predicted phenomenon and the instructions, which specify evidently before it; whether the anticipation is affirmed or not [1]. Perchance, the simplest adaptive formulation in report of future incidences was carried out by the eminent Panel on Earthquake Prediction [2] in 1976. Based on it, for prediction of an EQ the geographical zone, the time period in which it would occur with sufficient meticulousness and the predictable magnitude range must be taken into consideration so that the final failure or success of the EQ prediction can willingly be judged. Furthermore, scientists should correspondingly allot a level of confidence for each prediction.
\n
In this matter, researchers have studied and examined various techniques and approaches, such as numerical and mathematical models [3–11] to predict EQs in a desired range. A good example of finite meshing of a complex earthquake was evaluated by Landry and Barbot [8], in which a new numerical approach was presented as a numerical solution to the elastostatic equations with embedded discontinuities. The method was performed in a new earth modeling code, Gamra. In particular, recommendation of recent studies done encourage the use of several seismicity indicators or features comprising geophysical information corresponding to EQ incidence in order to predict EQ [12–14]. The relationship of such indicators using the binary class showed that some indicators displayed information fetch up nine to zero [15]. However, some investigators [16] tried to work out one step forward since all of those indicators were used within a baseline arrangement and only used the standard values, neglecting the fact that, changes in arrangements or configurations might lead to opposed results or in some conditions to better consequences.
\n
Review of the abovementioned studies as representatives of investigations in the field of EQ prediction shows serious and heavy works, but according to studies done, to predict a potent motion, the estimation of pertaining parameters is vital for the aim of seismic analysis, seismic design, and seismic retrofitting. This study attempts to present new current methods for prediction of EQ in seismic regions. However, the EQ prediction, as mentioned, is a very difficult task which has been broadly addressed by means of various techniques, but it still sounds to achieve precise results, there is a long way to go. Therefore, to have an overview of utilized approaches, a category of these has been carried out. Herein, an effort was made to figure out those strategies used such as short‐term (SHT hereinafter), intermediate‐term (IMT hereinafter) and long‐term (LT hereinafter) prediction in order to familiarize readers in this filed. Moreover, this chapter will go briefly through artificial intelligence branches that are being comprehensively implemented by scientists across the globe. Figure 1 demonstrates a general process of the chapter.
Figure 1.
The general process of EQ prediction used by researchers is investigated in this chapter to clarify the current progress in the relevant field.
\n
\n
\n
2. Earthquake prediction strategies
\n
Prediction of earthquakes is routinely categorized into three divisions: SHT, IMT, and LT prediction. They may differ based on the used methods, purpose, and accuracy. The SHT prediction requests precursors. Even though some encouraging precursors are informed, the dominant pieces of evidence in Japan and other places are excessively pessimistic. The cynicism basically roots as a fact is that, the SHT precursor is commonly nonseismic and the developed tools in the seismology field are not planned to find them. Regrettably, SHT predictions that ran to practical activities have rarely been accomplished and doubtful views are prevalent in seismological society. Both IMT and LT predictions are in nature predictions of the actuarial possibility of earthquakes. LT prediction contracts with the possibility of EQ incidence on a time‐scale between 10 and 100 years and mainly is based on the geological investigations of faults and epochal seismicity records. IMT prediction, unlike to LT prediction is mainly in a time‐scale of 1–10 years, utilizes more of current instrumental seismology and geodesy data. Despite the negative opinions such as that of Geller et al. [17], substantial development has been created in the study of precursory model changes of seismicity like [18–20] and in case of the IMT estimation of strong EQs around the globe is already proven in the statistically stage [21]. More lately, even the attempts to simplify the main time to the SHT range are being carried out [1, 22]. Therefore, this chapter attempts to present aforesaid strategies in the EQ prediction field to update and familiarize readers with current progress of the area.
\n
\n
2.1. Short‐term (SHT) prediction
\n
Historically, a broad variety of methods have been practiced to predict EQs. Nowadays, in addition to attempts at IMT and LT prediction, SHT forecasting is on progress too. This would postwarnings at the very early signs of a momentous EQ or tsunami. EQ prediction has to determine the epicenter, EQ size and domain, and time with high precision. Amongst the short‐, mid, and long‐term predictions, SHT prediction may be significant, but as mentioned before, unfortunately, it has not achieved yet. Some precursors are thoroughly required for SHT detection. Several types of EQ precursors like Uyeda et al. [23] were determined. Seismological events such as fore‐shocks can be considered as precursors. Nevertheless, most of precursors cannot be considered as seismological. In order to predict EQ, the national scheme (launched in Japan, 1965) has no success even a unique achievement. The explanation of this failure was that it failed to understand those precursors as explained by Uyeda [24]. In 1995, the Kobe EQ with M7.3 occurred with no prediction (Figure 2), in the seventh 5‐year plan. With no prediction, the national project was violently criticized. After long deliberations at different points and stages, they resulted to disuse SHT forecast since precursors were highly knotty to cope with (see Swinbanks [25]) and struggles must have focused on the fundamental study that really was seismology [26]. Taking this exercise, the mission not only outlived the criticism, but investing was augmented too. After 1995, the no SHT prediction strategy was stepped up even to “make decision” that, precursors are not existent and their investigation is not science. A few years later, namely in 2011, the Tohoku‐Oki EQ with M9.0 hit Japan (see Figure 3) and produced a destructive tsunami, which caused huge explosions, smelt at Fukushima Nuclear Plant No.1 [27] and over 20,000 people were killed.
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Such an EQ was described by the severity model, however, none of them even visualize that an EQ with M9.0 occur. With this broad disappointment, researchers now even converse about eliminating a well‐disciplined working group (Seismological Society of Japan) in the field of EQ forecast/prediction. The current authorized attitude of the Headquarters for EQ Research Promotion of MEXT (Ministry of Education, Culture, Sports, Science, and Technology) of Japan declares that, their mission would be long‐term statistical forecast of seismicity. Even, it is claimed that they do not emphasize on SHT forecast. These are weird positions for accountable authorities, once the people instantly need all conceivable information on next ground motions, for example, the Ryukyu trench as shown in Figure 4 with high potential, particularly after the ruinous Tohoku EQ, 2011 with M9.0. This is inoperative. Recently, even the national project title has been altered to “Promotion of EQ and Volcano Observation Research Project to Contribute to Disaster Mitigation”, eliminating “prediction” term. Prediction of such ground excitations are not any more a simple monetary source, but the ship has shifted the helm to “catastrophe prevention”.
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Figure 2.
A damaged massive bridge and railways by Kobe EQ, 1995, January 17, M7.3.
Figure 3.
Uprising the Sekii River and burning the port of Sendai during the Tohoku‐Oki tsunami, 2011, March 3 and 11, M9.0.
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Herein, the SHT prediction has been described as an untrusted strategy compared to other prediction techniques. Therefore, an attempt is made to present an example of this strategy in a brief manner just in order to acquaintance.
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Figure 4.
An EQ probability along the Ryukyu trench, Japan.
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In this regard, the Chiayi zone (southwestern Taiwan) is selected with a significant deformation rate which has been hit by several devastating EQs, e.g., Chiayi EQ, 1792 [28]. This EQ can be due to the Meishan fault rupture. Moreover, the Meishan EQ sequence (during 1904 and 1906) caused wide fatalities and numerous buildings collapsed. Nevertheless, a SHT probabilistic seismic hazards assessment was applied, as shown in Figure 5, to the Chiayi zone that involved the Meishan EQ sequence. Figure 6 and Figure 7 show the Touliu, 1904, Meishan, 1906, and Yangshuigang EQ, 1906 which is used as the source events in order calculate ΔCFS. Here, the rate and state friction model [29] is considered for assessing SHT seismicity‐rate evolution. The Coulomb failure stress changes, ΔCFS, by EQ are computed for the model application. Based on the constant apparent friction model, the general state for ΔCFS can be written as follows:\n
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Figure 5.
Propagation of three EQs with M ≥ 6.0 for the Meishan EQ sequence between 1904 and 1906. Three Xingang, Chiayi, and Meishan cities considered for seismic hazards assessments and are depicted as black squares. Stars display the EQs epicenters. The location of the illustrated region is given in Figure 7.
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Figure 6.
The flowchart for an approach of SHT prediction.
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Figure 7.
The repartition of declustered EQs with M ≥ 5.0 during 1940 and 2010. Note that, some interface actions in northeastern Taiwan occurred at the longitude 122° east are not revealed in this figure.
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ΔCFS=Δτ+μ′ΔσnE1
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in which Δτ is the changes of shear stress calculated along the receiver fault slip. μ′ is the coefficient of apparent friction; and Δσn is the changes of normal stress which is perpendicular to receiver fault. Former studies, e.g., [30] have discovered that a positive change of stress boosts following events. In contrast, a negative change of stress debars the future seismicity events. Additionally, the focal mechanisms or receiver fault mechanism should be used as an information key for calculation of the ΔCFS. According to those previous investigations [30, 31], a temporally fixed fault is assumed and procedure is made. The ΔCFS should be estimated on each grid (e. g., 0.01° × 0.01°) and solved partial changing of receiver faults by means of an appropriate program such as Coulomb 3.3.
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The ΔCFSs divulged by the Meishan EQ sequence have been assessed in the Chiayi zone (Figure 8). The results showed notable intensifications in the neighborhood of each event. The M6.9 Meishan EQ (Figure 8(b)) produced a stress, that increased with a vaster range and a severer magnitude. In contrary, the M6.1 Touliu EQ (Figure 8(a)) generated less considerable stress disturbance. Such an inconsistency can be ascribed to the difference of magnitude between EQs. In short, the outcomes demonstrated that, a large EQ close to the epicenter zone of the Yanshuigang EQ could be predicted. It is noteworthy mentioning that, some areas with the highest increases of Coulomb stress did not take place subsequent to devastating EQs (Figure 8). Such consequences can be attributed to the low rate of cumulative stress (away from next coming rupture, or to relevant tectonics not found with seismogenic).
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Figure 8.
The Coulomb stress changes divulged by (a) the Touliu, 1904, (b) the Meishan, 1906, and (c) the Yanshuigang EQ, 1906. Changes of stress nearby the epicenters of subsequent EQs that presented as dash stars are illustrated. (a) The Touliu EQ, 1904. (b) The Meishan EQ, 1906. (c) The Yaunshuigang EQ, 1906. Solved ΔCFS on partial changing receiver fault, maximum ΔCFS among the seismogenic layer for 0–30 km depth.
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2.2. Intermediate‐term (IMT) prediction
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In the last few decades the serious EQ model, that is according to considerations pertaining to accelerating seismic transformation and dynamic concepts of the critical point, has been suggested by several seismologists as an advantageous tool in order to predict IMT EQ. IMT EQ prediction is divided into various algorithms such as CN, MSc (Mendocino Scenario), M8, and M8S. The common methodology to the different algorithms creates usage of generic concepts of pattern identification that allow dealing with several sets of EQ precursors, and permits for a regular seismicity monitoring as well as for an extensive testing of the predictions. In fact, predictions may be helpful if their precisions are recognized, but not certainly high. It is the standard exercise to all forecasting difficulties comprising national defense. For instance, the MSc algorithm reduces the estimation area to a fine range (2–3 times the EQ source zone size) and for some cases approximately a precise location (inside the source zone dimension). The M8 algorithm forecasts EQ with several years (intermediate‐term) middle‐range (5–10 times the EQ source zone size) precision [32]. Though, the long‐running global testing IMT and middle‐range EQ forecasts support the theory that, the algorithm of M8 can be efficient to globally decrease the effect of strong EQs (M ≥ 8.0) [32, 33], there is no connexion to implement events and improve EQ readiness in a reaction to them. This is not ended so far; the EQ prediction might have been used to fulfill measures and amend EQ preparedness beforehand; unfortunately, this was not achieved, in part owing to the limited distribution of predictions and the absence of applying current approaches in order to use IMT detections to take action and make decisions [32]. Overall, IMT estimations are not capable to be used for prevention of all damages and protect all human life, but they may be utilized to undertake certain affordable activities to decrease damage, losses and modify postdisaster relief. Davis et al. [34] proposed examples and methodologies on how activities may be occupied in reflex to predictions. Davis [35] explained how to employ economic parameters to ignored equations for optimization of activities that may be affected a forecast. Such activities are fruitful to improve the normal LT seismic risk reduction approaches, such as building codes or standard disaster preparation methods. As an example, these processes were recognized [34] and models were prepared for the Tohoku EQ on how prudent, cost effective, and reasonable conclusions can be made to decrease destructive EQ influences. Information provided through M8–MSc algorithms may reduce the worldwide effects from the strongest EQs. Retrospectively, there were many precursor‐like anomalies earlier the Wenchuan EQ, 2008 (see [36] for review), but no anomaly was so decisive until the SHT or IMT alarm was used. Indeed, so long as seismologists deal with the SHT to IMT EQ forecast, which has many uncertainties and is frequently linked to emergency management counteractions, e. g., evacuation activities, this public stress becomes even larger.
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A simple explanation is prepared herein to express some potentials on how the EQ forecasting may have been used by means of the IMT strategy, e. g., EQ prediction algorithms using CN, M8, and MSc, real‐time forecasts using the M8‐MSc algorithms and other applications of M8 algorithm. In brief, the CN, MSc, and M8 algorithms, as the main algorithms for IMT prediction of EQs, are present as below.
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2.2.1. CN algorithm
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CN algorithm is structured accordance with a pattern identification scheme to permit an analysis of the times of increased probability (TIP) for the event of strong EQs. It represents the possible occurrence (within a specified time window and region) with a magnitude larger than an immovable threshold, based on a measurable investigation of the seismic study. The seismicity patterns are obtained using a set of experiential time functions (assessed on the order of the incidences occurred in the analyzed zone) and description of the seismic activity level), seismic reticence and space‐time collecting of events. Therefore, CN algorithm makes usage of the information arranged by minor and moderate EQs, having more or less good stats within the surrounded region, to forecast the severer EQs, which are infrequent events.
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Since the period of TIPs varies from few months to few years, CN estimations are categorized with a time uncertainty over the years and with a space uncertainty of several kilometers, the so‐called medium‐range predictions, in relation to an entire single monitored area. Accordance with CN, once a TIP is notified, the strong EQ could take place in any location of the alerted region; accordingly, defined regions ought to be small. Nonetheless, the algorithm is on the basis of precursors, which can be hosted in a region with linear dimensions extremely larger than the dimension of the anticipated source. At the end, taking into consideration the accuracy of CN algorithm in global tests and the low proportion of occurrence of the strong EQs, it may be possible to predict the contingent possibility for a TIP about 40%. So, as revealed by Peresan et al. [37] a notified TIP has roughly 60% probability to be an incorrect alarm, whilst if TIP is not designated, at 96% of probability no potent EQ will happen.
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2.2.2. MSc algorithm
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Algorithm MSc was introduced and named by retroactivity examination of the local seismic catalog before the Eureka EQ, 1980 with M7.2, in California nearby Cape Mendocino. By having a TIP recognized for a given territory (U) at the time (T), the algorithm is considered to detect U, in a smaller area (V) in which the predicted EQ can be anticipated. An application of this algorithm needs a rationally complete catalog of EQs with ranged magnitudes (M ≥ 4.0), that is lesser than the least threshold normally utilized via M8 algorithm. The nature of MSc algorithm is briefly described as follows:\n
Coarse‐grained U (territory) is changed to fine squares with s × s dimension. Let say i and j is the center coordinates of the squares. In each coordinate (i, j), the number of EQs nij (k, the subsequence quantity of a specific time window) including aftershocks is computed for consecutive (short time windows), months long (u), onset from initial time to onward (=6 years), to permit the EQs which is associated with the TIP’s recognition. The considered time‐space should separate into small cases (i, j, k) of the dimensions (s × s × u).
”Quiet” cases are separate from each small square with i, j coordinate; they are differentiated by nij (k) that is under the nijQ percentile/centile.
The clusters of more quiet cases or Q linked in place or in a time are known. The area (V) is the regional pattern of these clusters.
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The adjusted values of parameters have been standardized for the Eureka EQ and are as follows:
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u = 2 months
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Q = 10%
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q = 4
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s = 3D/16
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where, D is the circle diameter used in M8 algorithm. Note that, the phenomenon utilized in the MSc algorithm may reflect the shorter‐term (second) phase of premonition increase of seismic motion near the initial source of main‐shock.
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2.2.3. M8 algorithm
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The M8 algorithm was introduced and named by retroactivity examination of the seismicity prior to the greatest EQs worldwide (M8+). It is according to an ordinary physical system of forecast, which is briefly presented as subsequent writing. M8 is a written program in Fortran 77 which agrees in order to predict the EQ in the IMT by means of the M8 algorithm. Also, the algorithm is used to evaluate a time series of integral numbers according to transient seismicity within a region.
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2.2.3.1. Prediction is designed at EQs with magnitude M ≥ 0
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Various values with a step of 0.5 for M0 are considered. Overlying circles with diameter of D (M0) monitor the seismic region. In each circle, EQ sequence is deliberated with after‐shocks eliminated {ti, hi, mi, bi(e)} where i = 1, 2,....
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Here, ti is the source time and ti ≤ ti + 1; hi and mi stand for focal depth and magnitude and bi(e) represents the number of after‐shocks within the first (e) days. The sequence is standardized by the less magnitude (С), in which C is the standard value for the annual average number of EQs in the sequence. The used magnitude scale must reflect the EQ source size.
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2.2.3.2. Calculation of numerous running norms in the time windows
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In this case, they illustrate diverse measures of severity in EQ flow, its deviance from the trend of long‐term and grouping of EQs. These averages contain:
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the number of main‐shocks, N(t), the deviance of N(t) from the trend of long‐term, L(t), and the linear concentration of the main‐shocks, Z(t), considered as the average diameter ratio of the source, i. e., l, to the r (average distance) between them. The EQ sequence, {i}, is deliberated within the time window. The functions of N, L, and Z are calculated for С = 20 and 10. Hence, the EQ sequence gives a strong averaged statement by seven functions; namely N, L, and Z (twice each) and B (the maximum number of after‐shocks). Figure 9 shows the criterion of the M8 algorithm in the seismic extended standardized phase space.
Figure 9.
The parameters of N, L, Z and be for the M8 algorithm in the seismic extended standardized phase space.
\n
\n
2.2.3.3. A TIP or an alarm (increased probability time)
\n
It is announced for 5 years once at least six out of seven aforesaid functions including B become too large within a limited time window; namely (t–u,t). To fix prediction, this statement is necessary for two successive periods, t and t + 0.5 years.
\n
To sum up, prediction algorithms of an EQ, through above mentioned algorithms, are in accordance with a comprehensive general scheme demonstrated in Figure 10. A seismically energetic region is considered with an example of areas, generally, (CIs) circles of investigation (a), in which the areas have their own seismic events “history” of various magnitude (b). Each “history” of seismic activity is described based on the specified exactly definable moving numbers (c), which synthesis is expose to pattern diagnosis of “precursor” representing whether the next periods are a TIP or not, for the expected target EQ occurrence (d).
\n
\n
\n
\n
\n
2.3. Long‐term (LT) prediction
\n
Despite the serious effort done and the several models developed [38], no prosperous technique has been detected yet. Because of the random actions of EQs, it may not be possible to determine the exact location, magnitude, and time of the next fatal EQ. Most recently, Tohoku, Japan was hit by an EQ with M9.1, on March 11, 2011. This excitation caused approximately 20,000 deaths, more than $300 billion (USD) in detriment and it proved impossible to predict the LT disasters (it ranks amongst the most devastating natural hazards ever recorded). As regards, it occurred in a well‐prepared and well‐organized country where great efforts have been made to reduce seismic hazard, it was a stupendous experience. Long‐term procedures are occasionally defined as timeless historical risk valuation, whereas SHT preEQ processes mean a detected process happening minutes to months earlier than an EQ. Considering long‐term seismicity properties is much more problematic than similar investigations of SHT and IMT changes of EQ event rates [39]. A reason for this can obviously be lack of good documents and analogous long time catalogs of EQs. Existing historical catalogs are not strongly homogeneous both in space and time. In fact, primary investigations of active faults as well as their LT seismicity spreading disclosed that, majority of active regions instrumental seismicity catalogs do not include the seismic sequence. Even, in active areas where the epochal seismicity covers more than 1000 years, e.g., the Fault of Dead Sea, paleoseismology prepared substantial results on the return period of fatal EQs with fault properties and corresponding magnitude of past seismic actions that complete old scripts [40, 41]. Investigations of the LT performance of seismogenic faults and correlated paleoseismic statistics are today often mandatory in seismic hazard characteristics of applications for building services and as participation to national and international requirements for seismic safety [42]. More significantly, current works query the credit of seismic hazard maps mentioning the weakly constrained EQ limitations resulting from overlooking paleoseismic data for the risk analysis [43]. Prospective EQ predictions make scientific theories of EQ occurrence refutable, transparent, and testable. A main step along this direction was undertaken by the Regional Earthquake Likelihood Models (RELM) working group, which demanded LT (5 year) predictions for California city in a particular format to ease syllogistic testing [44, 45].
\n
Figure 10.
A general design of an EQ detection tool.
\n
The aim of this section is to present the current progress in LT EQ prediction and its effect on the seismic risk assessment in zones with long epochal EQ excitation records. Herein, the LT forecast has been described as a strategy which has a long path to complete the mission that is EQ prediction with a secure and accurate manner. Therefore, an effort is made to present an example of this prediction, in the particular application of neural networks, in a short manner, as presented in the subsequent writings (Section 2.3.1), to acquaint. Despite using Bat‐ANN algorithm (a combination of Bat Algorithm, BA, with Artificial Neural Network, ANN, algorithm) in one of the most recent investigations [46] to predict EQs in Pakistan regions, but in conclusion the research is confessing that, due to more diversity provided by the method and stochastic approach, used algorithm has “further chances” to detect global goals (still waveringly). In a nutshell, an EQ location of a given magnitude range and time prediction can be arranged into the listed categories as illustrated in Table 1 based on spatial accuracy and its temporal [47].
\n
\n
\n
Temporal, in years
\n
Spatial, in source zone size
\n
\n
\n
Short‐term (SHT)
\n
0.01–0.1
\n
Narrow
\n
2–3
\n
\n
\n
Intermediate‐term (IMT)
\n
1
\n
Middle‐range
\n
5–10
\n
\n
\n
Long‐term (LT)
\n
10
\n
Long‐range
\n
Up to 100
\n
\n\n
Table 1.
Precision classification of EQ prediction.
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\n
2.3.1. Artificial neural networks (ANN)
\n
A number of EQ forecast approaches including artificial intelligence (AI) have used. One of these techniques is the ANN which has presented a good ability for detecting solutions in different fields. Variations of different algorithmic have been designed and proposed to expand the ANN accuracy. The ANN algorithm is a challenge to model things with sophisticated software or professionalized hardware, the several layers of processing elements via neurons. The human brain as the most fundamental element is a particular kind of neuron that furnishes one with the capacities to think, remember, and apply past skills to every practice. The benefit of this frame is that, the ANN delivers a black‐box method and the operator does not require to distinguish much about the process nature being simulated. Having this in mind, ANNs have been promising methods in order to predict and detect locally imminent EQs according to reliable seismic information. For these reasons, the ANN has recently been broadly applied to different areas to prevail the problem of exclusive and nonlinear relationships.
\n
Herein, the section tries to briefly investigate the methodical possibilities of EQ forecast by means of Neural Networks (NNs). Moreover, this short investigation provides an accurate layout of different precursors; namely PGA, and main‐shock detection, however, other precursors such as radon detection, liquefaction, and aftershocks should also be taken into consideration. In addition, it is also discussed how these precursors are used by NN in order to EQ detection and prediction. A corresponding network analysis is stated for each seismic precursor, beside the type used NN.
\n
2.3.1.1. Prediction of peak ground acceleration (PGA)
\n
A PGA is an acceleration measure of EQ due to extensive ground motion and it is induced by a powerful energy released from an EQ, leading to earth deformation like landslides, surface ruptures, and liquefaction. In this regard, Derras and Bekkouche [48] presented a comprehensive method to assessing the maximum PGA by means of a feed‐forward back propagation neural network (FFBPANN). The result was compared with those ground motion prediction equations (GMPEs) demonstrated by Ambraseys and Douglas [49]. The GMPEs were utilized as a substitute method for the prediction of the PGA where accelerogram monitoring stations do not exist. Such an approach needs a wide data of PGA values as well as the site coefficients. An FFBPANN was projected with an overall selected 1000 epochs set and a tangent hyperbolic sigmoid activation function containing five input parameters; namely the depth of focus where an EQ was activated, the Japanese Meteorological Agency magnitude (MJMA), the epicentral distance, the resonant frequency, fx, and the sedimentary layers’ thickness, Zx. Prominently, shear wave velocity with x = 800 m/s is constant for both fx and Zx parameters. Figure 11 presents a visual view of the site seismic parameters used for a PGA estimation [48].
Figure 11.
Site seismic parameters in assessment of a PGA.
\n
The configuration of ANN used in Figure 11 contains of 1850 testing data taken out from Kiban Kyoshin network (KiK‐net) data and 326 training. An evaluation between an FFBPANN and selected GMPE model [49] indicated that, the FFBPANN performance is far compared to those GMPEs. The determination coefficient (R2) predicted by an NN was 0.94 in comparison of those two GMPE methods, which were 0.82 and 0.76. In the same site, the normalized root mean square error (NMRSE) for the NN models was noticeably smaller, 0.11%, in comparison with the selected GMPE approaches, which depicted a corresponding NMRSE of 0.17 and 0.25%. It is approving that, a PGA estimation by means of an NN is much better than the GMPE approaches from both accuracy and performance point of view. Specifically, the outcomes showed that, the parameter of epicentral distance heavily affects the result of a PGA value, procuring the best mean square error (MSE)and R values of 0.075/0.076 and 0.51/0.48 for the testing phases and training, respectively. In contrary, the site parameters and focal depth have the minimum effect on the PGA value outcome. In addition to these, a mixture of altogether five parameters was seen to return the optimum results, keeping the MSE value with 0.0205 and 0.0203 and an R‐score (correlational coefficient) with 0.84 and 0.85, for the testing phases and training. The R‐score shows the independence degree betwixt an output and an input. An R coefficient closer to ± 1 displays a powerful correlation, though, R = 0 is considered as an inaccurate prediction.
\n
For prediction of PGA values in a specific site a different direction was discovered [50] using an ANN algorithm and microtremor measurements. On the other hand, microtremor measurements are an experiential method to collect all vibrations of ground in a very scant time interval, thereby taking an obvious superiority over a traditional record database. Applying predefined data for training and network validating utilizing a BP syllogism, the ANN method was developed. Particularly, three types of input neurons were assessed: the focal depth, epicentral distance, and EQ magnitude. The results revealed that, using input parameters obtained 0.972 for an R‐score, which was interestingly higher superior to two other input parameters (on an average 0.6–0.9) and one input parameter (on an average under 0.6). A comparative study of this issue has given clear consequences that microtremor measurements are literally efficient once time is limited, but do not have performance ability. On the contrary, an ANN is noteworthy in performance, nonetheless as a weak point extremely relies on predefined data. The models of three neural networks are shown in Figure 12 and a comparison between microtremor measurements and a used ANN for the PGA approximation is shown in Figure 13, respectively [50].
\n
Figure 12.
Neural networks models.
\n
Figure 13.
PGA approximation.
\n
Additionally, several novel methods have been examined to estimate PGA values, giving different accuracy rates and performance levels. Günaydın and Günaydın [51] presented a comparative method to PGA forecast by assessing three ANN types; namely a generalized regression neural network (GRNN), feed‐forward back propagation (FFBP) and a radial basis function (RBF). All these ANN types were trained by means of a back propagation (BP) procedure through solely one hidden layer and were also assessed using four input request parameters; namely the EQ moment magnitude, focal depth, site conditions, and hypocentral distance. These request parameters, through 15 accelerometers for 95 records, were taken from three wave directions including east‐west, north‐south, and up‐down). In summary, for forecasting horizontal and vertical values of PGA the FFBPANN and GRNNANN were the ideal options.
\n
\n
2.3.1.2. Prediction of large‐magnitude of main‐shocks
\n
Panakkat and Adeli [13] performed an analysis using three different types of NNs; namely a Levenberg‐Marquardt back propagation neural network (LMBP)recurrent neural network (RNN), and a radial basis function (RBF) neural network, to eventually forecast a strongest EQ one month earlier. The input request parameters were alike to the neurons described in [52]. Comparative results amongst three different networks indicated that, for ranging from M5.0 to M6.0, the RNN showed 0.20–0.51 for an R‐score, while LMBP and RBF networks acquired ranging from 0.01 to 0.14 and 0.12 to 0.37, respectively, for an R‐score. Consequently, it was apparent that, an RNN is able to determine large EQs (M6.0+) more accurate and faster than both RBF and LMBP networks. Figure 14 shows an RNN layout. Despite the fact, the presented EQ forecast cannot be completed with a satisfactory degree of certainty.
Figure 14.
Structure of the back propagation NN to predict an EQ occurrence [13].
\n
Meantime, Reyes et al. [12] lately developed an approach for determining EQs in Chile by employing a BPANN in a three layer using the b‐value, Omori‐Utsu\'s and Bath\'s law. The network result is twofold [53]. First, it shows the probability of a magnitude beyond a defined data threshold. Next, the network will produce a possible magnitude that might take place within an interval of 5 days. A comparison of different NNs like a KNN [54], an ANN, K‐means clustering [55], and an SVM [56] indicated that, except SVM, a KNN, an ANN, and K‐means clustering give better forecast precisions. For validation of the used network, 500 eons were utilized in four unlike zones in Chile. A comparison among the NNs demonstrated that, the overall measured performance according to sensitivity and specificity values was highly location dependent. Specifically, the sensitivity of 35.7, 42.9, and 50% was recorded for the ANN, KNN, and K‐means clustering, respectively. Inconclusive results were given for the SVM.
\n
\n
\n
\n
\n
\n
3. Conclusions
\n
A brief description in anatomy of an EQ causes has been made and the existing problem in relation to predict and detect this fatal phenomenon has been stated. Therefore, the prediction strategies have been classified into three main scenarios; namely short‐term (SHT) prediction, intermediate‐term (IMT) prediction and long‐term (LT) prediction. For each strategy, an attempt has been made to present either an example or a general trend of that strategy to familiarize readers more in the corresponding field as well as presenting an overall background of the researches done by several investigators worldwide. Based on these explanations the below conclusion is drawn:
\n
In the field of EQ prediction, numerous researchers have vastly done serious investigations by means of diverse techniques to improve seismic reliability, recognize warning of EQ in a specific zone, and eventually decrease the negative effects of this natural hazard on human life. The improvement of science and equipment leads experts to develop the strategies of earthquake prediction more accurately. Based on the practical, numerical, and equational studies, it can be detected that majority of SHT predictions cannot be fruitful. Indeterminate and random surface displacement is normally seen in the LT predictions. Practical applications need the knowledge of ground shaking especially in analyses of liquefaction. In addition to these, AI branches/algorithms such as ANN, KNN, SVM, RBF LMBP, and RNN, however, have been proven with more accuracy and intensively used in EQ estimation field because of obtaining more satisfactory results, but regrettably, no general beneficial approach to precisely predict EQs has been detected yet. As a matter of fact, it may not be possible to determine the exact time once a devastating EQ will take place. It is because, as soon as sufficient strain has made, a fault possibly will become innately unstable and all teeny background EQ may or may not keep rupturing and become a large EQ. In particular, the major novelty of these efforts is the growth of a system capable of forecasting EQ occurrence for phases of time from statistical point of view, in order to permit the administrations to organize cautionary policies.
\n
\n
Acknowledgments
\n
The authors gratefully acknowledge the supports given by University of Malaya Research Grant (UMRG - Project No. RP004A/13AET) and Fundamental Research Grant Scheme, Ministry of Education, Malaysia (FRGS - Project No. FP028/2013A).
\n
\n',keywords:"earthquake prediction, artificial intelligence, short‐term prediction, intermediate‐term prediction, long‐term prediction, neural network",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/52565.pdf",chapterXML:"https://mts.intechopen.com/source/xml/52565.xml",downloadPdfUrl:"/chapter/pdf-download/52565",previewPdfUrl:"/chapter/pdf-preview/52565",totalDownloads:1499,totalViews:523,totalCrossrefCites:6,totalDimensionsCites:14,hasAltmetrics:0,dateSubmitted:"May 2nd 2016",dateReviewed:"September 5th 2016",datePrePublished:null,datePublished:"February 1st 2017",dateFinished:null,readingETA:"0",abstract:"Among the countless natural disasters, earthquakes are capable to inflict vast devastation to a large number of buildings and constructions at the blink of an eye. Lack of knowledge and awareness on earthquake as well as its comeback is conspicuous and results in disaster; leading to bitter memories. Therefore, earthquake forecast has been a polemical study theme that has defied even the most intelligent of minds. In this chapter, an attempt was made to do an extensive overview in the area of the earthquake prediction as well as classifying them into the main strategies comprising short‐, immediate‐, and long‐term prediction. An example of each strategy was carried out by mentioning their corresponding approaches/algorithms, such as ΔCFS, CN, MSc, M8, ANN, FFBPANN, KNN, GRNN, RBF, and LMBP; depending on the importance of each strategy. Based on these, it was concluded that, after the Tohoku‐Oki earthquake with M9.0, the current orientation of the Headquarters for earthquake Research Promotion of MEXT in Japan declare that, their mission would be long‐term statistical forecast of seismicity. Even, it is claimed that they do not emphasize on short‐term forecasting. Besides, intermediate‐term estimations are not capable to be used for prevention of all damages and protect all human life, but they may be utilized to undertake certain affordable activities to decrease damage, losses, and modify postdisaster relief. And, despite the long‐term prediction is more concerned by researchers, there is no certain satisfactory level to content them. De facto, the made covenant of 1970 that investigators will be capable to forecast/predict ground excitations within a decade, still remains unmet.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/52565",risUrl:"/chapter/ris/52565",book:{slug:"earthquakes-tectonics-hazard-and-risk-mitigation"},signatures:"Khaled Ghaedi and Zainah Ibrahim",authors:[{id:"190572",title:"Dr.",name:"Khaled",middleName:null,surname:"Ghaedi",fullName:"Khaled Ghaedi",slug:"khaled-ghaedi",email:"khaledqhaedi@yahoo.com",position:null,institution:{name:"University of Malaya",institutionURL:null,country:{name:"Malaysia"}}},{id:"196228",title:"Prof.",name:"Zainah",middleName:null,surname:"Ibrahim",fullName:"Zainah Ibrahim",slug:"zainah-ibrahim",email:"zainah@um.edu.my",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Earthquake prediction strategies",level:"1"},{id:"sec_2_2",title:"2.1. Short‐term (SHT) prediction",level:"2"},{id:"sec_3_2",title:"2.2. Intermediate‐term (IMT) prediction",level:"2"},{id:"sec_3_3",title:"2.2.1. CN algorithm",level:"3"},{id:"sec_4_3",title:"2.2.2. MSc algorithm",level:"3"},{id:"sec_5_3",title:"2.2.3. M8 algorithm",level:"3"},{id:"sec_5_4",title:"2.2.3.1. Prediction is designed at EQs with magnitude M ≥ 0",level:"4"},{id:"sec_6_4",title:"2.2.3.2. Calculation of numerous running norms in the time windows",level:"4"},{id:"sec_7_4",title:"2.2.3.3. A TIP or an alarm (increased probability time)",level:"4"},{id:"sec_10_2",title:"2.3. Long‐term (LT) prediction",level:"2"},{id:"sec_10_3",title:"2.3.1. Artificial neural networks (ANN)",level:"3"},{id:"sec_10_4",title:"2.3.1.1. Prediction of peak ground acceleration (PGA)",level:"4"},{id:"sec_11_4",title:"2.3.1.2. Prediction of large‐magnitude of main‐shocks",level:"4"},{id:"sec_15",title:"3. 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Wyss (Eds.), “Hazards and Disasters Series; Earthquake Hazard, Risk, and Disasters,” Acad. Press, Elsevier, vol. 53, no. 9, pp. 1689–1699, 2014.'},{id:"B48",body:'B. Derras and A. Bekkouche, “Use of the artificial neural network for peak ground acceleration estimation,” Lebanese Sci. J., vol. 12, p. 101, 2011.'},{id:"B49",body:'N. N. Ambraseys and J. Douglas, “Near‐field horizontal and vertical earthquake ground motions,” Soil dyn. earthq. eng., vol. 23, no. 1, pp. 1–18, 2003.'},{id:"B50",body:'T. Kerh and D. Chu, “Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion,” Adv. Eng. Softw., vol. 33, no. 11, pp. 733–742, 2002.'},{id:"B51",body:'K. Günaydın and A. Günaydın, “Peak ground acceleration prediction by artificial neural networks for northwestern turkey,” Math. Probl. Eng., vol. 2008, Article ID 919420, p. 20 pages, 2008.'},{id:"B52",body:'H. Adeli and A. Panakkat, “A probabilistic neural network for earthquake magnitude prediction,” Neural Netw., vol. 22, no. 7, pp. 1018–1024, 2009.'},{id:"B53",body:'A. Sriram, S. Rahanamayan, and F. Bourennani, “Artificial neural networks for earthquake anomaly detection,” J. Adv. Comput. Intel. Intel. Informat., vol. 18, no. 5, 2014.'},{id:"B54",body:'T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Tran. Inform. Theory., vol. 13, no. 1, pp. 21–27, 1967.'},{id:"B55",body:'J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the 5th Berkeley Symposium on Mathematical Statististics and Probability, 1968, pp. 281–297.'},{id:"B56",body:'V. V. C. Cortes, “Support‐vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Khaled Ghaedi",address:"khaledqhaedi@yahoo.com",affiliation:'
Department of Civil Engineering, University of Malaya, Kuala Lumpur, Malaysia
Department of Civil Engineering, University of Malaya, Kuala Lumpur, Malaysia
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\n
1. Introduction
\n
Increased environmental pollution, numerous motor vehicles, factory wastes and urbanization factors have been the source of high increases in the release of toxic, explosive and flammable gases in the environment of developed countries. High rate of gas emissions has both a negative impact on human/animal health and it can also have bad consequences on the environment and natural resources from day by day.
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With the start of the Industrial Revolution, the acceleration of coal and mine quarries caused a significant increase in deaths due to toxic gas. First, canaries were used in gas detectors in mines. The cost and difficulty of using different methods for determination of toxic gases have revealed the gas sensors. In 1815, British scientist H. Davy developed a gas meter called ‘Davy’s lamp’ against methane gas [1]. In 1926, Johnson produced the first commercial catalytic, combustion gas sensor, and in 1929, the company they founded with Williams became the first company in Silicon Valley in electronics [2].
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Gas sensors are used to detect combustible, explosive and toxic gases, when the measured gas concentration exceeds the threshold value they can give an alarm (sound, signal, etc.) that can be used as portable or fixed devices. The most important part of this device production is the sensor which determines 4S parameters (sensitivity, selectivity, stability, speed). Apart from them, recovery time, response time and power consumption are also other parameters. The sensor part records changes in the physical conditions or chemical components as signals (permeability, resistance, temperature, acoustic wave, capacitance, etc.) as a result of interaction between target gas and surface atoms (O−, O2−, H+ and OH−) by absorption/desorption of gas on the material surface at a specific operating temperature. Signal can correlate concentration of target gas [3].
\n
The recent change in the OSHA Time Weighted Average (TWA) Permissible Exposure Limit (PEL) is 25, 35 and 1 ppm for NH3, CO and NO2 gases, respectively [4].
\n
CO is a toxic colorless gas, environmental pollutant and kills by causing hypoxia with damaged hemoglobin cells in the blood. In general, the measurement of CO gas is realized by detection of percentage of carboxyhemoglobin in the blood. Another important issue is creation of residential and automotive environment so it is so necessary fast and sensitive detection. Difficulty in detecting very low levels and continuous CO formation in the air poses problems [5].
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Odorless and toxic ammonia (NH3) combustion, which is used in a large area as a fertilizer, refrigerant material and household cleaning product, is a major hazard. Using or producing ammonia besides any uncontrolled leaks by the infrastructures or its explosion causes health hazards. In addition, it is a chemical pollutant in the production of silicon type devices in clean room [6].
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Nitrogen dioxide (NO2) is a volatile and toxic gas. It has hazardous effects in environment as a secondary pollutant and its detection is so important. NO2 gas generates fuel burning at high temperature and in nitrogen cycle, including acid rains. Under even very low concentrations (<10 ppm) it causes serious damages for human health such as throat discomfort, transient coughs, eye irritation, fatigue and nausea [7].
\n
With nano-sized designed gas sensors, surface to volume ratio is increased for absorbed target gas as well as higher efficiency is obtained than traditional bulk-scale designed devices, because different atomic coordination and translational symmetry at the surface ensure electrical properties changing in semiconductors [8]. In particular, a dramatic increase using the nano-sized designed gas sensors have been observed in industrial areas such as pharmaceuticals, medical, automotive, building automation, space tools, wearable devices. The first study of the semiconductor material group was given by Brattain and Bardeen on germanium (Ge) in 1953 [9]. In the next study, in 1954, Heiland had a research report on the gas sensitivities of metal oxides, and also in 1962 Seiyama showed that ZnO structures were sensitive to reactive gases in the air [10]. In 1968, Taguchi-type sensors were introduced to market and metal oxide (SnO2) gas sensors were moved to industrial level [11].
\n
Nano-scale designed gas sensors are usually classified depending on measurement data as follows; (i) chemiresistors, (ii) thermal conductivity gas sensors, (iii) acoustic wave gas sensors, (iv) calorimetric gas sensors, (v) optical gas sensors (vi) electrochemical gas sensors and (vii) infrared absorption gas sensors [13, 14].
\n
Chemiresistive gas sensor working principle can be explained simply as adsorption of electron with target gas on the surface can cause charge transfer (a change in charge carrier concentration) between target gas/material surface region (receptor function) so electrical properties can be (resistance or conductivity) increase or decrease. Easy measurement with two electrodes is a factor in their preference and supplying safety.
\n
Today, using chemiresistive metal oxide (MO) semiconductors, real-time gas sensor has gained great importance both in the science/industrial world due to their high sensitivity to chemical environments, low price, simple implantation, safety and durable to high temperature/high pressure, indicating that compelling conditions. Companies such as FIS, Mics, UST, CityTech, Appliedensors and Newcosmos produce millions of MO gas sensor per year, especially the Figaro company which produces Taguchi type sensors [15].
\n
Gas selectivity is a critical problem for metal oxide gas sensors. To increase the selectivity of metal oxide sensors, it is proposed to use a heating mode of a gas-sensing floor with rapid temperature modulation in the last studies.
\n
Metal oxide semiconductor gas sensors are focused on different and new materials at room temperature with the increasing need for faster, more precise and easy gas sensing, as showed in \nFigure 1\n. Thus, the most important parameter mechanism is gas sensitivity, which still does not reveal the exact reasons (strongly related to surface reactions), can be detailed. Production techniques (spray pyrolysis, pulsed laser deposition, magnetron sputtering, spin coating, and chemical bath deposition) are undeniable facts because structure parameters, grain boundaries, point defects, surface morphology, porosity, etc. must be affected. Additionally, reducing (H2, H2S, etc.)/oxidizing (NH3, NO2, etc.) gas types and p- or n-type is also effective on the chemiresistive MO performance, as showed in \nFigure 2\n. Oxidizing or reducing gas is associated with electron affinity, which is compared to the work function of most metal oxide so in the case of oxidizing gas, the adsorbed gas molecules on the surface of the MO are anions.
\n
Figure 1.
Advantages and disadvantages of semiconductor metal oxides (reprinted from study of [12] with their permission).
\n
Figure 2.
Sensitivity measurement of material type and target gas type (reprinted from study of [16] et al. with their permission).
\n
The change in electrical resistance of semiconductors can be explained as follows; formation of the space-charge depletion zone on the surface and around the particle and the energy band bending. Surface energy barriers with variable heights and widths depend on the relationship between charging the surface states of the adsorbed species for conduction electrons. In gas sensors using n-type semiconductor oxide, it has been observed that the resistance of the oxide increases with the interaction of gases such as O3 or NO2, while the resistance decrease of the oxide occurs with interaction of gases such as CH4 and CO, as showed in \nFigure 2\n.
\n
It is discussed that resistive-type metal oxide semiconductors produced by nanostructures (especially thin films) in detail toward NH3, NO2 and CO gases. Additionally, effect of doping and nanocomposite forming with C-based material (especially carbon nanotubes) were studied.
\n
\n
\n
2. Metal oxide (MO) gas sensors
\n
Since 1962, the addition of the oxygen contained in the metal oxides to the reaction so increase of reactions and their stable chemical transduction properties which can reversibly convert chemical reactions on a surface make the metal oxides attractive for detect various harmful, toxic, and explosive gases. Development of gas sensors, which are almost 21% of the metal oxides used in the field, is rapidly increasing [17]. Because they have unique properties such as low cost, long lifetime, fast response time and relatively high sensitivity. However, some restrictions are detected in these structures such as background gas effect, poor selectivity and power consumption in high temperature conditions which could not be proper for especially wireless applications.
\n
Basically, the main challenge is they operate only at elevated temperatures and consume more power with high operating temperatures. Physisorption and chemisorption are surface adsorption forms of oxygen. Physisorption to chemisorption needs activation energy with realized by increasing operating temperature. In addition, forming of oxygen species depends on the operating temperature substantially. Sun et al. reported that molecular species are more than atomic species below 150°C, this cause a decrease in gas sensitivity [18].
\n
Another goal of gas sensitivity works is to ensure that electrical change in the gas environment occurs not only at grain boundaries but on the entire material surface. Since grain boundaries are smaller than MO particles, surface chemistry is more effective and the effect of grain boundaries on electrical change is not considered.
\n
To achieve high performance from MO gas sensors, detailed knowledge of the gas sensing mechanism is essential. In general, it can be explained as follows; oxygen adsorption on the surface of sensing material, adsorbed oxygen species (extrinsic surface acceptor states) molecular (\n\n\nO\n2\n−\n\n\n) or atomic (O−, O2−), captured from the interior of the sensing material, resulting in a depletion layer on the surface due to oxygen species. Eventually observing a decrease in the conductivity/resistance [19]. In other words, oxygen ions on the surface of metal oxides are highly active interactions with the target gas molecule. When O2 molecules adsorb from the surface of the MO, they break off electrons from the conductivity band (Ei) and trap electrons form on the surface, which come across in ion form. This causes band bending and electron depletion layer (space charge layer) formation. When the electron concentration in the conductivity band decreases, the conductivity decreases as well. At the same time, negatively charged traps in these different types of adsorbed oxygen cause downward bending of the band curve, which, compared to the flat state of the band, decreases conductivity. The thickness of the electron depletion layer is the width of the band bending region. The displacement of adsorbed oxygen with other molecules and the reaction of different oxygen ions with reduced gas changes conductivity.
\n
Among metal oxide gas sensors single (ZnO, NiO, TiO2, SnO2, WO3, etc.), binary and ternary samples have unique properties such as chemical stability, relatively low harmful for environment, abundant in nature and low cost. Wang et al. showed that metal oxides selected for real gas sensors can be separated according to their electronic structure [20];
d0 transition metal oxides: In this group (WO3, V2O5, TiO2 and etc.), d0 electronic configurations are preferred with their wide band gap energy and surface forms so it can measure easily.
pre-transition metal oxides: In this group (Al2O3, MgO and etc) are not preferred due to neither electrons nor holes forming so occurs very band gap energy, structural instability and difficulty of measure electrical conductivity.
post-transition metal oxides: They have d10 electronic configuration. ZnO, SnO2 Ga2O3 and In2O3 are preferred in MO gas sensor applications. Because they are so proper for electron accumulation and chemisorption of donor-like species occurrence.
\n\n
\n
\n
3. Thin film metal oxide gas sensors
\n
In semiconductor gas sensor applications, advantages of thin film using are low resource waste, high surface/volume ratio, low power consumption, easy compliance with integrated circuits and easy alteration of electrical properties with changing film production parameters. Thin film technology allows the film properties to be changed by keeping the thickness parameter under considerable control. In this way, thin films are easily integrated into the device during the material production process. They can also be used as electronic circuit elements by acting as new materials when they are produced in multilayer.
\n
Thin film metal oxides are used by the detection a lot of gas types such as Carbon-based (CO, CO2, CH4, C2H5OH, C3H8), nitrogen-based (NH3, NO, NO2), H2, H2S, ethanol, acetone, LPG and moisture.
\n
The large number of grain boundaries in thin film polycrystalline MO’s limits mobility, thus reducing carrier concentration and decreasing gas sensitivity. The presence of depletion layers in these grain boundaries is the most important factor that reduces mobility. Grain boundaries affect mobility due to their positioning to potential barriers with high intensity defect levels.
\n
There have been a lot of ZnO thin film study to detect NO2 gas sensing that have been reported with different morphologies nanowires, nanorods [21], nanoprisms [22] and nanospheres [23] in order to enhance surface area. In 2019, Duoc et al. synthesized ZnO nanowires and nanorods with using on-chip grown via hydrothermal method at room temperature NO2 gas sensing [24]. The diameter of these structures severely affected gas sensing, indicating nanowires were more sensitive than nanorods. ZnO nanobarded fibers were synthesized by electrospinning and chemical bath deposition. These structures showed improved NO2 detection performance for gas concentrations up to 30 ppb [25].
\n
In our previous study, nanoflower shaped n-type ZnO films synthesized by chemical bath deposition and their 0.5 ppm NO2 gas sensing was detected, showing in \nFigures 3\n and \n4\n [26]. Operating temperature was chosen at 200°C due to statical recovery kinetics were worse under this temperature. Oxygen vacancies (oxygen-deficient ZnO) acted as adsorption sites, electron donor sites and nucleation centers for small metal clusters. Reaction on the ZnO film surface was given by two equations between exposing oxidizing type NO2 molecules and oxygen species in the ZnO grain boundaries;
\n
Figure 3.
SEM images of (a) ZnO and annealed ZnO films at (b) 450°C, (c) 500°C and (d) 550°C (reprinted from [26]).
\n
Figure 4.
0.5 ppm NO2 gas sensitivity of ZnO thin films at 200°C (reprinted from [26]).
With increasing annealing temperature and thereby decreased grain sizes caused an increase surface/volume ratio and NO2 gas sensing, as expected for n-type ZnO. It was interesting that very high annealing temperature (>500°C) could lead to deterioration on the substrate/deposited layer interface, as showed in \nFigure 3d\n.
\n
\n
\n
4. Doping
\n
To arrangement structural, morphological and gas sensing properties of MO nanomaterials, doping is an effective method with metallic ions (Al, Fe, Co, Cu, Ag and etc.). Defect sites and location of a host or doping ions determines grain size and electronic band of nanomaterials thereby sensing layer resistance. The substituted atoms can act as reactive sites for gas adsorption [27]. On the other word, surface impurities and defects with generating doping ions and thereby adsorption sites can cause extrinsic electronic states [28]. The reduction of the grain size to nanometers or to a scale comparable to the thickness of the charge depletion layer leads to a dramatic improvement in the gas sensitivity. It has been also found that the crystal structure of the grains affects the absorption of gases. Metal atom doping can also increase gas selectivity as reported by Govardhan and Grace [29].
\n
Ionic radius difference plays a very important role between metal dopant and host metal (Zn, Sn, Fe, etc.) in gas sensing. Interstitial sites and oxygen vacancies are so critical in physisorption and chemisorption processes. To determine electronic traps in the doped structure deep level transient spectroscopy is an effective method.
\n
However, heavily doped metal oxides (>10%) showed poor gas performance with high concentration defect regions, which is attributed to limitation on the Fermi level shift during interaction with the target gas [30].
\n
The highest surface roughness values are 5% Al doping, and samples with this dopant have the highest NH3 response times, explained by Aydın et al. [31]. Other Al:ZnO film studies were received by Dimitrov et al. [32] and Patil and Sondkar [33] toward CO gas.
\n
In our previous study, Al-source effect was investigated on the NH3 gas sensing and response time parameters as showed in \nFigures 5\n\n–\n\n7\n [34]. Alteration of surface particle type and dissolve depending on Al-source were caused by gas sensing parameters severely due to changing the energy-band gap structure, surface effective/contact area and NH3 gas adsorption rate. Oxygen molecules that are adsorbed convert into oxygen species depending on temperature by capturing free electrons from the oxide. Then, depletion layers form in surface areas, leading to an increase in oxide resistance. According to Eq. (3), the electrons were released back to the conduction band, finally resulting in the decrease of the resistance.
\n
Figure 5.
SEM images of (a) pure ZnO and (b, c, and d) different Al:ZnO films depending on Al-source reprinted from [35].
\n
Figure 6.
NH3 sensing response of Al:ZnO films as a function of time (reprinted from [34]).
\n
Figure 7.
(a) NH3 gas response and (b) NH3 gas recovery times of Al:ZnO films (reprinted from [34]).
As showed in \nFigures 5\n and \n6\n, nanorod formations (\nFigure 5b\n) had highest response times and gas sensing at low temperatures in powder Al-source used samples. Al-sources have high impact on gas sensing character due to changing film growth process and surface morphologies.
\n
\n
\n
5. MO/CNT nanocomposites
\n
The exceptional and unique properties of carbon-based materials (carbon nanotubes, graphene, graphite, and plumbane) offer a great advantage for the production of improved composites, while their applications as a matrix element depends primarily on the relationship between the matrix and the other material. Gas sensor sensitivity of some MO-C-based nanostructures (MO: ZnO, SnO2, TiO2) is showed in \nTable 1\n and SWCNT-MO structure studies are so rare until now, interestingly. Because SWCNTs are much more expensive than MWCNTs and titanium oxide film production is usually expensive by physical methods. Defects forms such as atom vacancies, functional groups and stone wall defects on nanotubes can enhance the sensitivity toward different gases with metal oxide compositions. Additionally, as a matrix material supplies high quality of crystal lattice leading to a quite low electronic noise and they act as the Schottky barrier. These defect sites lower the activation energy barrier thus enabling chemisorptions of analytes on the surface of CNTs and make room temperature measurements possible [35].
Comparison of some MO/C-based nanostructure gas sensors sensitivity (S%) toward NO2, NH3, and CO gases.
\n
In general, incorporation of C-based material into MO structure, n-type to p-type convert or p-n junction are observed so active sites available for gas adsorption and formation desired depletion layer [36].
\n
Another improvement mechanism approach at room temperature proposed by Tai et al., indicating that supporting role of MO nanoparticles layer (first depletion layer from adsorption of ionized oxygen) as well as formed accumulation heterojunction at interface between MO and C-based material (second depletion layer) [37].
\n
In a recent study, Lee et al. explained that improvement mechanism that was attributed the removal of oxygen-containing functional groups, the supply of electrons from the oxygen vacancies of ZnO material, and the formation of C-O-Zn bonds in ZnO-rGO membrane and operation under 100 ppm NO2 at room temperature [55].
\n
Among C-based materials, two types of carbon nanotubes (CNTs) (both single-walled [SWCNT] and multi-walled [MWCNT] carbon nanotubes) are so attractive in gas sensor support material studies due to their room temperature gas sensing, fast response and good reversibility properties. Hollow cores and inner/outside walls of CNTs supply large gas adsorption regions so they allow donating/withdrawing charge carrier mobilization [56]. Therefore, it causes a change in charge carrier concentration.
\n
Multi-walled carbon nanotubes (MWCNTs) are nanoscale materials that comprise of several concentric single walled carbon nanotubes (SWCNTs) and exhibit diameters in the range of 5 and 30 nm [57]. Purification of MWCNTs (acid treatment, oxidation by heating, filtration, centrifugation, size-exclusive chromatography, etc.) is a preferred method to observation of no signal between target gas/CNT surface [58].
\n
Sputter of nanoclusters of proper type atoms on surface provides catalysis process, enhancing gas sensing with functionalization of CNTs [59].
\n
As reported to our previous study, MWCNT coating and MWCNT etching with HCl acid treatment effect was investigated on nanoflower ZnO seed layer against CO gas, showed in \nFigures 8\n and \n9\n [60]. The gas-sensing results had been shown that the response had been dramatically enhanced with the decoration of MWCNTs and rMWCNTs/ZnO sensor had exhibited the highest response to CO gas at 70°C. Consequently, it had been determined that gas sensing performance of the MWCNTs-decorated ZnO sensors had improved surface reactions with ZnO lattice. This may be attributed to the diffusion of the target gas through MWCNTs nanochannels.
\n
Figure 8.
SEM images of (a) ZnO/MWCNT and (b) ZnO/etched MWCNT films (reprinted from [60]).
\n
Figure 9.
Gas sensing parameters of ZnO/MWCNT film (reprinted from [60]).
\n
\n
\n
6. Conclusion
\n
In global, gas sensor market demands high performance on all 4S parameters (most common from ppb to ppm), miniaturization of weight, compatibility with other device components/wireless, flexibility for especially wearable devices and fabrication cost. It is expected to reach nearly 3 billion dollars in 2027. Recently, chemiresistive metal oxide semiconductor gas sensors are so interesting due to low cost, relatively high sensitivity and easy integration with CMOS compatible devices. The fact that the metal oxide gas sensor studies are very wide and there are quite a lot of publications in the literature about this topic. Hence some limitations are obligatory in this chapter.
\n
Unlike other gas sensors in chemiresistive gas sensors, target gas concentration variation can be done in a quantitative way by direct measurement of electrical resistance. A change in the barrier height occurs between the particles due to the reducing or oxidizing of target gas. This detection largely depends on the grain size, depletion layer width and conduction characteristics of the nanostructures. Debye length must be compatible to the depletion layer.
\n
Long-life sensitivity is still a key challenge. Today, the first and most common approach can be given as rapid decrease of material dimension (3D to 1D) and thus it has rapid expansion on the sensitive region but other factors (background gas, grain boundaries, granular forms, humidity and etc.) can be disregarded. Additionally, minimum particle size and enhanced/tunable surface reactivity at room temperature are main goals in a lot of studies. However, particle stability thereby gas sensing performance is not stable especially with particle size changing. Gas transfer via micro-, meso-, and nano-porous sensing films with their assembled hierarchical, hollow, and yolk-shell forms has an enormous effect on interaction of target gas-oxygen species-nanoparticles.
\n
In this study, metal oxide gas sensors by nanostructures were investigated comprehensively. ZnO nanoflower, Al:ZnO depending on Al-solution type and ZnO/MWCNT films were investigated toward different gases from our previous studies. Gas sensitivity was preferred main gas sensor parameter.
\n
The results show that there is an interaction between the gas molecules and the sample surface based on the exchange of charges. While there is no gas in the environment, O2 molecules adsorbed on the sample surface form an electron depletion zone. When the sample interacts with gas molecules, O2 molecules also interact with the gas, and O2 molecules begin to be dislocated from the surface. By separating O2 molecules from the surface, electrons are released according to the property of the gas (reducing or oxidizing), or an electron is ionized from the sample. Thus, the change in electrical conductivity is observed. The detection rates and return mechanisms of the samples have also been fairly quick. Return times indicate that the main mechanism between the gases and the sample surface is physical adsorption. In physical adsorption, gas molecules are held in structurally formed cavities on the surfaces of the container in which they are located, interacting with the surface atoms Van der Waals. This phenomenon is reversible.
\n
In MO and metal doping MO studies, film growth process must be under control to avoid agglomerative formations and un-expected ion positions in crystal structure, this causes gas adsorption process decreasing. Similar effect also occurs in C-based material/MO nanocomposites however having bonds of C-based materials and p- to n-type conversion/p-n junction have improvement effect on the gas sensitivity with expanded depletion region, indicating room temperature sensing.
\n
On the other hand, in improvement studies of gas sensors, metal oxide gas sensors based on micro-hotplates fabricated with micro-electro-mechanical system (MEMS) technology that needs to be developed due to being restrictions on material and design. Uniform mesoporous structures are also desirable because they allow more sensing regions for gas diffusion. Additionally, metal organic frameworks (MOFs) with ultrahigh porosity have been also so attractive especially last years.
\n
Considering the circumstances mentioned above, engineering control over the metal oxide structure and sensor design is so critical in order to obtain high stability as well as high gas sensitivity. Development of new metal oxide material compositions and their high stability/crystallinity will bring high performance gas sensors. New nanofabrication techniques and surface improved studies have contributed to development metal oxide gas sensors.
\n
\n
Acknowledgments
\n
I would like to thank Emin Yakar and Sani Demiri for academic support. Also, I would like to thank Irmak Karaduman Er and Selim Acar for their help in the gas sensor performance measurements section.
\n
Thank you to the Science and Technology Application and Research Center (ÇOBILTUM/ÇOMU) for supporting instrumental analysis.
\n
\n',keywords:"metal oxide, gas sensor, toxic gas, doping, multiwalled carbon nanotube",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68941.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68941.xml",downloadPdfUrl:"/chapter/pdf-download/68941",previewPdfUrl:"/chapter/pdf-preview/68941",totalDownloads:1126,totalViews:0,totalCrossrefCites:5,dateSubmitted:"February 14th 2019",dateReviewed:"July 27th 2019",datePrePublished:"September 6th 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Recently, metal oxide gas sensors by nanostructures have stirred interest and have found their way in many applications due to their high sensitivity, material design compliance and high safety properties. Gas performance tests of n-type ZnO, Al-doped ZnO and ZnO/MWCNT structures toward different type gases from our previous studies have been reported. It is indicated that nanoparticle formations on the film surfaces, grain sizes, gas types and operating temperatures have a severe effect on the chemisorption/physisorption process. Low concentration detection, determination of grain size limit values and reducing operating temperature to room temperature are already obstacles on long-life sensitivity and long-term stability characters. Doping is an effective way to increase gas sensitivity with atomic surface arrangement and active gas adsorption sites, which are generated by doping atoms. However, C-based material/MO nanostructures are preferred than doped MO films with their working even at room temperature. Up to now, a lot of methods to improve the gas sensitivity has been proposed. With the help of the development of surface modification methods such as different types of doping and MO-C composite, sensitivity, which is the most important parameter of sensor performance, can also be stable as well as increasing later on.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68941",risUrl:"/chapter/ris/68941",signatures:"Fatma Sarf",book:{id:"8724",title:"Gas Sensors",subtitle:null,fullTitle:"Gas Sensors",slug:"gas-sensors",publishedDate:"March 25th 2020",bookSignature:"Sher Bahadar Khan, Abdullah M. Asiri and Kalsoom Akhtar",coverURL:"https://cdn.intechopen.com/books/images_new/8724.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"245468",title:"Dr.",name:"Sher Bahadar",middleName:null,surname:"Khan",slug:"sher-bahadar-khan",fullName:"Sher Bahadar Khan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Metal oxide (MO) gas sensors",level:"1"},{id:"sec_3",title:"3. Thin film metal oxide gas sensors",level:"1"},{id:"sec_4",title:"4. Doping",level:"1"},{id:"sec_5",title:"5. MO/CNT nanocomposites",level:"1"},{id:"sec_6",title:"6. Conclusion",level:"1"},{id:"sec_7",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\nThomas JM. Sir Humphry Davy and the coal miners of the world: A commentary on Davy (1816) an account of an invention for giving light in explosive mixtures of fire-damp in coal mines. Philosophical Transactions of the Royal Society A: Mathematical Physical and Engineering Sciences. 2015;373:20140288-20140299. DOI: 10.1098/rsta.2014.0288\n'},{id:"B2",body:'\nLiu X, Cheng S, Liu H, Hu S, Zhang D, Ning HA. Survey on gas sensing technology: Review. Sensors. 2012;12:9635-9665. DOI: 10.3390/s120709635\n'},{id:"B3",body:'\nNazemi H, Joseph A, Park J, Emadi A. Advanced micro- and nano-gas sensor technology: A review. 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DOI: 10.3762/bjnano.9.264\n'},{id:"B37",body:'\nLi J, Liu X, Sun J. One step solvothermal synthesis of urchin-like ZnO nanorods/graphene hollow spheres and their NO2 gas sensing properties. Ceramics International. 2016;42:2085-2090. DOI: 10.1016/j.ceramint.2015.09.134\n'},{id:"B38",body:'\nTai H, Yuan Z, Zheng W, Ye Z, Liu C, Du X. ZnO nanoparticles/reduced graphene oxide bilayer thin films for improved NH3-sensing performances at room temperature. Nanoscale Research Letters. 2016;11:130. DOI: 10.1186/s11671-016-1343-7\n'},{id:"B39",body:'\nSrivastava V, Jain K. At room temperature graphene/SnO2 is better than MWCNT/SnO2 as NO2 gas sensor. Materials Letters. 2016;169:28-32. DOI: 10.1016/j.matlet.2015.12.115\n'},{id:"B40",body:'\nChen Y, Zhang W, Wu Q. A highly sensitive room-temperature sensing material for NH3:SnO2-nanorods coupled by rGO. Sensors and Actuators B. 2017;242:1216-1226. DOI: 10.1016/j.snb.2016.09.096\n'},{id:"B41",body:'\nShojaee M, Nasresfahani S, Sheikhi MH. Hydrothermally synthesized Pd-loaded SnO2/partially reduced graphene oxide nanocomposite for effective detection of carbon monoxide at room temperature. Sensors and Actuators B. 2018;254:457-467\n'},{id:"B42",body:'\nYe Z, Tai H, Guo R, Yuan Z, Liu C, Su Y, et al. Excellent ammonia sensing performance of gas sensor based on graphene/titanium dioxide hybrid with improved morphology. Applied Surface Science. 2017;419:84-90. DOI: 10.1016/j.apsusc.2017.03.251\n'},{id:"B43",body:'\nBandı S, Hastak V, Peshwe DR, Srıvastav AK. In-situ TiO2–rGO nanocomposites for CO gas sensing. Bulletin of Materials Science. 2018;41:115. DOI: 10.1007/s12034-018-1632-0\n'},{id:"B44",body:'\nKwon YJ, Mirzaei A, Kang SY, Choi MS, Bang JH, Kim SS, et al. Synthesis, characterization and gas sensing properties of ZnO-decorated MWCNTs. Applied Surface Science. 2013;413:242-252. DOI: 10.1016/j.apsusc.2017.03.290\n'},{id:"B45",body:'\nSchütt F, Postica V, Adelung R, Lupan O. Single and networked ZnO−CNT hybrid tetrapods for selective room-temperature high-performance ammonia sensors. ACS Applied Materials & Interfaces. 2017;9:23107-23118. DOI: 10.1021/acsami.7b03702\n'},{id:"B46",body:'\nChoi K, Park J, Park K, Kim HJ, Park H, Kim S. Low power micro-gas sensors using mixed SnO2 nanoparticles and MWCNTs to detect NO2, NH3, and xylene gases for ubiquitous sensor network applications. Sensors and Actuators B. 2010;150:65-72\n'},{id:"B47",body:'\nWei L, Shizhen H, Wenzhe C. An MWCNT-doped SnO2 thin film NO2gas sensor by RF reactive magnetron sputtering. Journal of Semiconductors. 2010;31(2):024006-024006. DOI: 10.1088/1674-4926/31/2/024006\n'},{id:"B48",body:'\nKaushik P, Eliáš M, Prášek J, Pytlíček Z, Zajíčková L. Titanium dioxide modified multi-walled carbon nanotubes as room temperature NH3 gas sensors. IEEE. 2018; DOI: 10.1109/ICSENS.2018.8589876\n'},{id:"B49",body:'\nLee J-S, Ha T-J, Hong M-H, Park C-S, Park H-H. The effect of multiwalled carbon nanotube doping on the CO gas sensitivity of TiO2 xerogel composite film. Applied Surface Science. 2013;269:125-128\n'},{id:"B50",body:'\nBarthwal S, Singh B, Singh NB. ZnO-SWCNT nanocomposite as NO2 gas sensor. Materials Today: Proceedings. 2018;5:15439-15444\n'},{id:"B51",body:'\nHernández SC et al. Hybrid ZnO/SWNT nanostructures based gas sensor. Electroanalysis. 2012;24(7):1613-1620. DOI: 10.1002/elan.201200135\n'},{id:"B52",body:'\nCarpenter MA, Mathur S, Kolmakov A. Metal Oxide Nanomaterials for Chemical Sensors. Berlin, Germany: Springer Science & Business Media; 2012. 548 p\n'},{id:"B53",body:'\nSu HC, Zhang M, Bosze W, Myung NV. Tin dioxide functionalized single-walled carbon nanotube (SnO2/SWNT)-based ammonia gas sensors and their sensing mechanism. Journal of the Electrochemical Society. 2014;161(14):B283-B290\n'},{id:"B54",body:'\nYang A, Tao X, Wang R. Room temperature gas sensing properties of SnO2/multiwall-carbon-nanotube composite nanofibers. Applied Physics Letters. 2007;91:133110\n'},{id:"B55",body:'\nLee H, Heish Y, Lee C. High sensitivity detection of nitrogen oxide gas at room temperature using zinc oxide-reduced graphene oxide sensing membrane. Journal of Alloys and Compounds. 2019;773:950-954. DOI: 10.1016/j.jallcom.2018.09.290\n'},{id:"B56",body:'\nCastro EA. Nanoscience and Advancing Computational Methods in Chemistry: Research Progress. IGI Global; 2012. 321 p\n'},{id:"B57",body:'\nGangu KK, Maddila S, Jonnalagadda SB. A review on novel composites of MWCNTs mediated semiconducting materials as photocatalysts in water treatment. The Science of the Total Environment. 2019;646:1398-1412. DOI: 10.1016/j.scitotenv. 2018.07.375\n'},{id:"B58",body:'\nGao C, Guo Z, Liu J, Huang X. The new age of carbon nanotubes: An updated review of functionalized carbon nanotubes in electrochemical sensors. Nanoscale. 2012;4:1948. DOI: 10.1039/c2nr11757f\n'},{id:"B59",body:'\nNguyen LQ , Phan PQ , Duong HN, Nguyen CD, Nguyen LH. Enhancement of NH3 gas sensitivity at room temperature by carbon nanotube-based sensor coated with Co nanoparticles. Sensors. 2013;13:1754-1762. DOI: 10.3390/s130201754\n'},{id:"B60",body:'\nÖzutok F, Karaduman I, Acar S, Demırı S. Enhancing the CO gas sensing properties of ZnO thin films with the decoration of MWCNTs. Journal of Materials Science: Materials in Electronics. 2018;47(5):2648-2657. DOI: 10.1007/s10854-018-0288-2\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Fatma Sarf",address:"fatmaozutok@comu.edu.tr",affiliation:'
Physics Department, Çanakkale Onsekiz Mart University, Çanakkale, Turkey
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Asiri and Kalsoom Akhtar",coverURL:"https://cdn.intechopen.com/books/images_new/8724.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"245468",title:"Dr.",name:"Sher Bahadar",middleName:null,surname:"Khan",slug:"sher-bahadar-khan",fullName:"Sher Bahadar Khan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"176007",title:"Dr.",name:"Feng",middleName:null,surname:"Li",email:"lifeng2729@sxicc.ac.cn",fullName:"Feng Li",slug:"feng-li",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"Institute of Coal Chemistry",institutionURL:null,country:{name:"China"}}},booksEdited:[],chaptersAuthored:[{title:"Copper-based Perovskite Design and Its Performance in CO2 Hydrogenation to Methanol",slug:"copper-based-perovskite-design-and-its-performance-in-co2-hydrogenation-to-methanol",abstract:"Three series of perovskite-type catalysts, i.e., La–M–Mn–Cu–O (M = Mg, Y, Zn, Ce), La–M–Cu–Zn–O (M = Ce, Mg, Zr, Y), and La–Mn–Zn–Cu–O, were designed and applied in CO2 hydrogenation to methanol. The materials were characterized by XRD, N2-adsorption, N2O-adsorption, ICP-OES, XPS, and TPD techniques. Perovskite structures were observed and the ‘‘metal on oxide’’ could be realized via reduction. Upon the introduction of the fourth elements, more structure defects, smaller particles, higher Cu dispersion, larger amount of hydrogen desorption at low temperature, and more amount of basic sites were obtained. The selectivity for methanol and the TOF values were higher for the catalysts derived from perovskite-type precursors. The catalytic performance was related to Cuα+ and/or Cu0 species, low-temperature H2 adsorption on the unit, and the weak basic sites.",signatures:"Feng Li, Haijuan Zhan, Ning Zhao and Fukui Xiao",authors:[{id:"176007",title:"Dr.",name:"Feng",surname:"Li",fullName:"Feng Li",slug:"feng-li",email:"lifeng2729@sxicc.ac.cn"}],book:{title:"Perovskite Materials",slug:"perovskite-materials-synthesis-characterisation-properties-and-applications",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"175909",title:"Prof.",name:"Yoon Hee",surname:"Jeong",slug:"yoon-hee-jeong",fullName:"Yoon Hee Jeong",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Pohang University of Science and Technology",institutionURL:null,country:{name:"Korea, South"}}},{id:"175923",title:"Prof.",name:"Jinguang",surname:"Cheng",slug:"jinguang-cheng",fullName:"Jinguang Cheng",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"176174",title:"Dr.",name:"Martin",surname:"Schmal",slug:"martin-schmal",fullName:"Martin Schmal",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rio de Janeiro State University",institutionURL:null,country:{name:"Brazil"}}},{id:"176181",title:"Prof.",name:"Yiguo",surname:"Su",slug:"yiguo-su",fullName:"Yiguo Su",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Inner Mongolia University",institutionURL:null,country:{name:"China"}}},{id:"176204",title:"Prof.",name:"Xiaojing",surname:"Wang",slug:"xiaojing-wang",fullName:"Xiaojing Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"176205",title:"Prof.",name:"Chunfang",surname:"Du",slug:"chunfang-du",fullName:"Chunfang Du",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"177318",title:"Dr.",name:"Fabio",surname:"Souza Toniolo",slug:"fabio-souza-toniolo",fullName:"Fabio Souza Toniolo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"177339",title:"Dr.",name:"Junyu",surname:"Lang",slug:"junyu-lang",fullName:"Junyu Lang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"177405",title:"Prof.",name:"Ki-Seok",surname:"Kim",slug:"ki-seok-kim",fullName:"Ki-Seok Kim",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"194151",title:"Dr.",name:"Abhijit",surname:"Biswas",slug:"abhijit-biswas",fullName:"Abhijit Biswas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/194151/images/system/194151.png",biography:"Dr. Abhijit Biswas is a research associate at the Indian Institute of Science Education and Research (IISER) Pune, in India. His research goal is to design and synthesize highest quality epitaxial heterostructures and superlattices, to play with their internal degrees of freedom to exploit the structure–property relationships, in order to find the next-generation multi-functional materials, in view of applications and of fundamental interest. His current research interest ranges from growth of novel perovskite oxides to non-oxides epitaxial films, down to its ultra-thin limit, to observe unforeseeable phenomena. He is also engaged in the growth of high quality epitaxial layered carbides and two-dimensional non-oxide thin films, to exploit the strain, dimension, and quantum confinement effect. His recent work also includes the metal-insulator transitions and magneto-transport phenomena in strong spin-orbit coupled epitaxial perovskite oxide thin films by reducing dimensionality as well as strain engineering. He is also extremely interested in the various energy related environment friendly future technological applications of thin films. In his early research career, he had also extensively worked on the tailoring of metal oxide crystal surfaces to obtain the atomic flatness with single terminating layer. Currently, he is also serving as a reviewer of several reputed peer-review journals.\nDr. Biswas received his B.Sc. in Physics from Kalyani University, followed by M.Sc in Physics (specialization in experimental condensed matter physics) from Indian Institute of Technology (IIT), Bombay. His Ph.D., also in experimental condensed matter physics, was awarded by POSTECH, South Korea for his work on the transport phenomena in perovskite oxide thin films. Before moving back to India as a national post-doctoral fellow, he was a post-doc at POSTECH working in the field of growth and characterizations of strong spin-orbit coupled metal oxide thin films.",institutionString:"Indian Institute of Science Education and Research Pune",institution:{name:"Indian Institute of Science Education and Research Pune",institutionURL:null,country:{name:"India"}}}]},generic:{page:{slug:"partnerships",title:"Partnerships",intro:"
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Crossref
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Crossref is the official Digital Object Identifier (DOI) Registration Agency for scholarly and professional publications with a goal of making scholarly communications more effective. IntechOpen deposits metadata and registers DOIs for all content using the Crossref System. IntechOpen also deposits its references and uses the Crossref Cited-by service that enables researchers to track citation statistics.
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Altmetric and Dimensions from Digital Science
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Digital Science is a technology company serving the needs of scientific and research communities at key points along the full cycle of research. They support innovative businesses and technologies that make all parts of the research process more open, efficient and effective. IntechOpen integrates tools such as Altmetric to enable our researchers to track and measure the activity around their academic research and Dimensions, to ease access to the most relevant information and better understand and analyze the global research landscape.
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Enago
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