New methodologies using nanostructures for sensing and quantification of heavy metals.
\r\n\t
",isbn:"978-1-83969-234-5",printIsbn:"978-1-83969-233-8",pdfIsbn:"978-1-83969-235-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"a5f5277a1c0616ce6b35f4b44a4cac7a",bookSignature:"Dr. Basel I. Ismail",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10013.jpg",keywords:"Thermodynamics, Heat Transfer Analyses, Geothermal Power Generation, Economics, Geothermal Systems, Geothermal Heat Pump, Green Energy Buildings, Exploration Methods, Geologic Fundamentals, Geotechnical, Geothermal System Materials, Sustainability",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 29th 2020",dateEndSecondStepPublish:"November 26th 2020",dateEndThirdStepPublish:"January 25th 2021",dateEndFourthStepPublish:"April 15th 2021",dateEndFifthStepPublish:"June 14th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Leading research investigator in a collaborative project (2007-2010) with Goldcorp-Musselwhite Canada Ltd. and Engineering of Lakehead University, owner of a Ph.D. degree in Mechanical Engineering from McMaster University, Hamilton, Ontario, Canada and postdoctoral researcher (2004 to 2005) at McMaster University.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"62122",title:"Dr.",name:"Basel",middleName:"I.",surname:"Ismail",slug:"basel-ismail",fullName:"Basel Ismail",profilePictureURL:"https://mts.intechopen.com/storage/users/62122/images/system/62122.jpg",biography:"Dr. B. Ismail is currently an Associate Professor and Chairman of the Department of Mechanical Engineering, Lakehead University, Thunder Bay, Ontario, Canada. In 2004, Prof. Ismail earned his Ph.D. degree in Mechanical Engineering from McMaster University, Hamilton, Ontario, Canada. From 2004 to 2005, he worked as a Postdoctoral researcher at McMaster University. His specialty is in engineering heat transfer, engineering thermodynamics, and energy conversion and storage engineering. Dr. Ismail’s research activities are theoretical and applied in nature. Currently, his research areas of interest are focused on green engineering technologies related to alternative and renewable energy systems for power generation, heating and cooling. Dr. Ismail was the leading research investigator in a collaborative project (2007-2010) with Goldcorp-Musselwhite Canada Ltd. and Engineering of Lakehead University. This innovative project was state-of-the-art in geothermal heat pump technology applied in Northwestern Ontario, Canada. Dr. Ismail has published many technical reports and articles related to his research areas in reputable International Journals and Conferences. During his research activities, Dr. Ismail has supervised and trained many graduate students and senior undergraduate students in Mechanical Engineering with projects and theses related to innovative renewable and alternative energy engineering, and technologies.",institutionString:"Lakehead University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Lakehead University",institutionURL:null,country:{name:"Canada"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"184402",firstName:"Romina",lastName:"Rovan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/184402/images/4747_n.jpg",email:"romina.r@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5084",title:"Advances in Geothermal Energy",subtitle:null,isOpenForSubmission:!1,hash:"d4647f1f9dae170acf327283d55abbf1",slug:"advances-in-geothermal-energy",bookSignature:"Basel I. Ismail",coverURL:"https://cdn.intechopen.com/books/images_new/5084.jpg",editedByType:"Edited by",editors:[{id:"62122",title:"Dr.",name:"Basel",surname:"Ismail",slug:"basel-ismail",fullName:"Basel Ismail"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5602",title:"Renewable Hydropower Technologies",subtitle:null,isOpenForSubmission:!1,hash:"15ea891d96b6c9f2d3f28d5a21c09203",slug:"renewable-hydropower-technologies",bookSignature:"Basel I. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"10821",title:"Diagnosis of Intermittent Faults and its dynamics",doi:"10.5772/9507",slug:"diagnosis-of-intermittent-faults-and-its-dynamics",body:'\n\t\tIntermittent faults (IFs) are difficult to diagnose and may cause a great disruption in industrial processes. Most IFs are related to gradual degradation of components or systems. For instance, evolution of connection failures is shown in Fig. 1 (Correcher et al., 2004), (Sorensen et al, 1998). Connection failures are rarely repaired so its behaviour worsens over time. Intermittent faults behave as small noise fluctuations in stage 1 of their development. As the amplitude and duration of the fluctuations increase (stage 2), IFs start to occur. The effects of IFs are severe in stage 3.
\n\t\t\tTherefore, in many instances, the occurrence of IFs in a device is a prelude of permanent failures (PFs). In these cases, if IFs can be detected then appropriate actions could be taken in order to minimise the economic impact.
\n\t\t\tIn (Correcher et al., 2004) an IFs diagnosis tool was presented by the authors. This tool was able to diagnose the failure and recovery events in a system with IFs. This paper presents an extension of the work in (Correcher et al., 2004) which includes not only event detection but also fault dynamics detection, defined as the evolution of IFs occurrence over time. Other approaches to IFs diagnosis (Contant et al., 2004), (Jinag et al. 2003), do not consider IF dynamics.
\n\t\t\tConnection IF evolution through device life.
However, the existence of IF dynamics is experimentally shown in (Sorensen et al., 1998) and in the destructive tests presented in this paper. Therefore, we can introduce the diagnosis problem to be addressed.
\n\t\t\t\n\t\t\t\tDefinition 1: IF diagnosis problem.\n\t\t\t
\n\t\t\tStarting from a temporal input (U) and output (Y) sequence, obtained by means of sensors in the process, compute the presence of any failure f (with f included in a failure set), its recoveries and its dynamics.
\n\t\t\tThe proposed solution has a clear industrial interest, as not only diagnoses IFs, but also provides valuable information on the fault evolution for maintenance purposes. First, section 2 presents a study about the evolution of the IF during its online diagnosis. This approach is able to extract some characteristic parameters of the IF. These parameters will be useful for estimating the behaviour of the fault in the future. The approach is also applied to experimental data.
\n\t\t\tSection 3 analyses the effectiveness of the approach in the solution of the problem stated in definition 1 when diagnosing Discrete Event Systems (DES) (Cassandras & Lafortune, 1999). Section 4 presents an application of IF dynamics diagnosis with Coloured Petri Nets. Finally, we present some conclusions in section 5.
\n\t\tIF dynamics characterization generates useful information for preventive maintenance scheduling. Two complementary parameters are defined in this section: temporal failure density (DF) and pseudoperiod (Ps). The goal of these parameters is to characterize the IF dynamics. DF and Ps can be on-line computed. DF and Ps can also predict future behaviour of the faulty device.
\n\t\t\t\n\t\t\t\tDF and Ps are computed from IF time occurrence and IF duration (defined as the time difference between fault and recovery). Therefore, two arrays are computed for each fault: the fault time vector (FT\n\t\t\t\tFj\n\t\t\t\t=[FT\n\t\t\t\t(1)Fj\n\t\t\t\t,FT\n\t\t\t\t(2)Fj\n\t\t\t\t,…,FT\n\t\t\t\t(n)Fj\n\t\t\t\t]) and the duration time vector (T\n\t\t\t\tFj\n\t\t\t\t=[T\n\t\t\t\t(1)Fj\n\t\t\t\t, T\n\t\t\t\t(2)Fj\n\t\t\t\t,…,T\n\t\t\t\t(n)Fj]), where F\n\t\t\t\tj stands for a fault included in the fault set and "n" is the index number of detected faults. Arrays and parameters are computed recursively on-line considering a moving time window of a given duration.
\n\t\t\tTemporal failure density (DF or density in the rest of the paper) is defined as the average time a particular fault (Fj) is active within a sliding time window of duration W. Therefore, if we define the current time as "ti", the density is computed from "ti-W" to "ti". Therefore, DF is computed for time "ti" as:
\n\t\t\t\twhere CNT is the number of faults inside the window, "k" stands for the index of the first fault detected inside the window {k: FT(k)Fj ≥ (ti-W) }(k: FT(\n\t\t\t\t\tk\n\t\t\t\t\t)\n\t\t\t\t\tF\n\t\t\t\t\tj > (ti-W) and FT(\n\t\t\t\t\tk-1\n\t\t\t\t\t)\n\t\t\t\t\tF\n\t\t\t\t\tj<(ti-W)} if exists, otherwise {k=CNT+1} and "T\n\t\t\t\t\tA\n\t\t\t\t\t" takes into account the duration of a fault occurred before "ti-W" which continues active inside the window. Therefore:
\n\t\t\t\tEquation (2) is valid only if "T\n\t\t\t\t\tA\n\t\t\t\t\t" is positive, otherwise "T\n\t\t\t\t\tA\n\t\t\t\t\t=0", as this fact would indicate that the first considered fault is completely outside of the window.
\n\t\t\t\tIn a real system, DF tends to increase with time; thus confirming the hypothesis that IFs progressively damage the faulty device. Figures 2 and 3 show the computed DF from experimental data and its filtered signal (low-pass fourth order Butterworth filter). The experimental data has been obtained from ten million operation tests on relays switching a resistive load. As the time between operations was 100 milliseconds, the overall duration of each experiment was 277.8 hours.
\n\t\t\t\tFault density. Window size is 10000 operations.
Fault density. Window size is 100000 operations.
The rising characteristic of the density can be used to estimate the optimal time to repair or substitute the faulty device. Effectively, a certain maximum density threshold can be defined as the limit for unacceptable behaviour. Then, an adequate extrapolation model can be used to predict the number of operations the device is capable of carrying out before reaching the unacceptable behaviour limit. Obviously, the unacceptable density threshold should be defined specifically for each process device, depending on its specific functionality and reliability requirements.
\n\t\t\t\tThe simplest prediction model consists of a density linear increase. In this case, filtered density data can be treated with classical techniques (Lemmis, 1995) such as least squares (LS) or recursive least squares (RLS). Therefore, we obtain a model:
\n\t\t\t\twhere D stands for the density, t stands for the time and the subindex ti stands for the time value when density is estimated. If we consider a threshold "Do", the time (tDo) when the density "D0" will be reached is:
\n\t\t\t\tTherefore, "t\n\t\t\t\t\tD0\n\t\t\t\t\t" is the time when the faulty device should be replaced. This time is named as Linear Substitution Time at time "ti"\n\t\t\t\t\t(LST\n\t\t\t\t\tti\n\t\t\t\t\t). In addition, it is possible to define another parameter much more suitable for preventive maintenance: Operations to Replacement at time "ti" (OTR\n\t\t\t\t\tti\n\t\t\t\t\t). This parameter represents an estimation of the useful operations left on a device, and can be computed as:
\n\t\t\t\tObviously, only positive values of OTR\n\t\t\t\t\tti are meaningful, otherwise the corresponding OTR\n\t\t\t\t\tti is considered equal to zero.
\n\t\t\t\tThe proposed linear prediction model has been found to be suitable for the use with the experimental used through the chapter. However, this kind of model might not be adequate for other devices. For instance, if the fault density follows first order dynamics then LSTti will predict an optimistic substitution time. This problem can be solved by using RLS with forgotten factor to fit LST\n\t\t\t\t\tti\n\t\t\t\t\t. In any case, LST\n\t\t\t\t\tti and OTR\n\t\t\t\t\tti reflect the underlying fault dynamics, and could be used to model systems that do not follow linear increase laws.
\n\t\t\t\tTherefore, the fault diagnosis system will predict the time when a device must be replaced in two stages. First, fault density will be computed and, from this value, LST\n\t\t\t\t\tti and OTR\n\t\t\t\t\tti will be predicted.
\n\t\t\t\tAs mentioned before, the sliding window size should be appropriately chosen, as short windows will imply high variability and noise in the calculated failure density and long windows, which exhibit a greater filtering effect, involve high computational costs and might mask part of the fault underlying dynamics.
\n\t\t\t\t\n\t\t\t\t\tFigures 2 and 3 show the effect of window size in the variability of the density. From calculations with a range of window sizes, it has been found that windows greater than 5000 operations include the same low frequency component, as shown in figs. 2 and 3. Therefore, LST\n\t\t\t\t\tti and OTR\n\t\t\t\t\tti computed from window sizes greater than 5000 operations are identical. Figure 4 shows the evolution of OTR\n\t\t\t\t\tti obtained from the experiments in figs 2 and 3. Figure 4 shows that the device should be replaced when reaching 6 million operations instead of the substitution time recommended by the manufacturer (10 million operations).
\n\t\t\t\n\t\t\t\t\t\tOTR\n\t\t\t\t\t\tti for an acceptable density threshold below 15%.
Fault density can be used to predict the device substitution time, however it does not completely explain IF dynamics. For instance, fig. 5 shows two cases with exactly the same failure density. However, the effects on the device are clearly different.
\n\t\t\t\tIF with the same density but different dynamics.
The difference between the two behaviours can be modelled by the time difference between the occurrences of two consecutive faults.
\n\t\t\t\tThe time difference can be measured with a new parameter, the Pseudoperiod (Ps). Ps is defined as the average time difference between faults inside a sliding window. Moreover, Ps is normalized by the number of faults, and can be computed at time "ti" as:
\n\t\t\t\twhere "ti" stands for current time, "FT" is the detection time for fault "i", and "j", "k" are the first and last fault indexes in the window, respectively.
\n\t\t\t\tPseudoperiod is clearly a magnitude related to mean time between failures (MTBF), commonly used to model reliability of reparable systems. Moreover, pseudoperiod is a dynamic magnitude. Therefore, we can compute a Ps curve for any IF. This curve can be used to predict the substitution time of the device.
\n\t\t\t\tThe evolution of Pseudoperiod (figs. 6 and 7) shows an increase until a maximum value is reached, to decrease towards a value close to zero. This dynamic behaviour is consistent with the nature of IFs (fig. 1). The computed Pseudoperiod remains in the range 600 to 800 from 4 million operations onwards. Therefore, it is possible to conclude that, in average, the number of failures from the 4 millionth operation remains reasonably constant. Moreover, the average duration of each failure slowly increases with the number of operations, as the density (fig. 3) increases.
\n\t\t\t\tPseudoperiod. Window size is 100000 operations.
Zoom in Pseudoperiod. Window size is 100000 operations.
Pseudoperiod can be used to compute some limit in the desirable behaviour of the system. The shape of the Pseudoperiod signal suggests the estimation of this limit with RLS with forgetting factor. Another solution could be a mixed model with a polynomial and linear estimation for each side of the signal. This last approach seems to be more promising but its computing is no trivial. Continuity and derivability must be guaranteed. Moreover, the order of the filter used in the Pseudoperiod signal will affect in the delay of the model. These problems will be addressed in future works.
\n\t\t\tIt is necessary to ascertain if the proposed dynamic parameters can be used to perform the complete fault diagnosis as per definition 1. To complete definition 1, a definition of fault dynamics diagnosis is introduced, based on the definition of discrete-time systems observability (Smolensky et. al, 1996) "A discrete-time system is observable if a finite k exists such that knowledge of the outputs to k-1 is sufficient to determine the initial state of the system. "
\n\t\t\tDefinition 2. IF dynamics diagnosis problem.\n\t\t\tGiven a temporal fault sequence, \n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
Definition 2 states that IF dynamics is diagnosable if we can compute the next time when a fault or recovery will occur. IFs are asynchronous and non-deterministic. So, IF dynamics cannot be diagnosed with deterministic precision. Therefore, we propose a relaxed definition.
\n\t\t\tDefinition 3. Bounded IF dynamics diagnosis problem.\n\t\t\tGiven a temporal fault sequence, \n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
This uncertainty can be used to compare different methods of diagnosis.
\n\t\t\tIF dynamics diagnosis start from a temporal fault event sequence and a temporal recovery event sequence until the actual time\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
The estimation error is therefore, the error in the RLS estimation (Smolensky, et. al, 1996).
\n\t\t\t\tIf we want to compute the time for a recovery, (TA≠0) the density will decrease in, at least, "tm/w", so:
\n\t\t\t\tWe can conclude that the density allows for the complete diagnosis of the IF dynamics with a bounded error.
\n\t\t\t\tIn order to compute the IF density, the fault diagnosis system should include the identification of fault starting time and duration. The fault diagnosis system should also be able to compute the corresponding fault densities.
\n\t\t\tThe previous section showed that the diagnosis of an IF involves not only the diagnosis of fault and recovery events, but also the diagnosis of its dynamics. This section shows how to diagnose IF dynamics with the methodology based on Coloured Petri Nets (CPN) presented in (Garcia et al., 2008) (Rodriguez et al., 2008). This methodology allows IF dynamics diagnosis because it includes timing information. We also include a complete diagnosis example of an industrial process.
\n\t\t\tA Coloured Petri Net for Fault Diagnosis (DCPNs) is:
\n\t\t\twhere
\n\t\t\tP is a finite set of places.
T is a finite set of transitions.
Pre and Post are input and output arc functions.
M0 is the initial marking.
C is the colour set assigned to different identifiers. \n\t\t\t\t\t\t
Fault verification places are P-timed. Therefore, they include pairs <R, TimR>; where R is a coloured mark and TimR is a timer. TimR will be used to compute IF Density and Pseudoperiod.
\n\t\t\tThorough this paper the notation M(Pl(\n\t\t\t\t
The first step of the method consists of the dynamic system modelling. The system model is designed with generalized Petri Nets (PNs) (David & Alla, 2005), for simple systems, or with CPNs (for complex systems) (Hensen, 1997).
\n\t\t\t\tLet us show the methodology applied to a rectifying industrial machine. The machine rectifies blocks of a synthetic compound that imitates the natural stone. Figure 8 shows the process scheme. The rectifying process consists of the mechanical elimination of some material in order to achieve the desired width. The system can be divided into four subsystems. Since each milling works with an independent motor, each subsystem will consist of a pair motor-milling with a blade cooling and lubrication system. The blade cooling system consists of a pair pump-valve that pours cutting oil over the millings. The motors can suffer Ifs resulting from fretting corrosion in their electrical contacts. The millings fail when there is any defect in the tool. This failure is a PF that can be due to maintenance failure. Milling failure will cause a great torque and, therefore, a power consumption greater that usual.
\n\t\t\t\tArtificial Stone rectifying process.
Moreover, a fault in a previous subsystem can cause the same symptoms because the milling will cut more material. The cooling and lubrication system can also suffer IFs. Typical IFs in a pump-valve system are electrical contact failures and valve blocking.
\n\t\t\t\t\n\t\t\t\t\tFigure 9 and table 1 show the PN system model. Table 2 shows the relationship between places and system states.
\n\t\t\t\tSystem PN model.
Transitions in PN system model.
Places in PN system model.
The next step consists of the folding to a CPN (Garcia et al., 2008). Figure 10 shows the result. The CPN system model stars in Pc1. Tr1 starts all subsystems and generates normal working tokens in Pc2. Arc functions g and g1 denote the relationship between the general normal working token with particular normal working tokens:
\n\t\t\t\twhere \n\t\t\t\t\t
Transition Tr2 puts a stone in the machine. This action starts the first subsystem (place Pc4) and moves the other three subsystems to "waiting for stone arrival" state (Pc3). Function gs1 and gsk model the different paths followed by subsystems:
\n\t\t\t\tTransition \n\t\t\t\t\t
\n\t\t\t\t\t
The next step is the fault analysis of system devices. The goal is to define the faults to be diagnosed in each device. Each fault has a coloured fault token. Therefore, the fault set consists of the union of the coloured fault tokens f = {f1, f2,, fi }. Fault isolation will be guaranteed because any fault is associated to a device. Each subsystem includes four devices: motor, milling, pump, and valve. Table 3 shows the faults included in the example:
\n\t\t\t\tFault set definition
The next step consists of marking all fault coloured tokens in specific CPN places. These places are called "Places of latent nestling faults" (PLNf). Expert\'s empirical knowledge sets the rules for this operation. Figure 10 shows the marking for this example.
\n\t\t\t\tSystem CPN model and fault allocation (¡={1,2,3,4}).
The computing of the thresholds to generate k(N) and Ii(S) events is not trivial, because the sensor will observe an overcurrent when the tool touches the block (normal working). Nevertheless, we can easily generate these events if having a normal working current pattern. Let us suppose that we have a current pattern for each motor: Patt(t). The continuous measure of the sensor is Ii\n\t\t\t\t\t(t). Therefore, for each time tk:
\n\t\t\t\twhere dmax is the maximum difference allowed between both signals. Therefore, we can define the three current events as:
\n\t\t\t\twhere y is close to zero and it allows noise filtering.
\n\t\t\t\tFlow sensors, \n\t\t\t\t\t
\n\t\t\t\t\t
The set of sensor values is SM. The set of possible sensor values combinations for marking Mk is denoted as "SROVj(Mk)"\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\tTable 4 defines the trajectories of fault verification for the diagnoser. "*" stands for any value of other sensors. Function g2 evaluates place markings and returns fault marks when necessary.
\n\t\t\t\tg2(Pl,V) {Pl ∈P; V∈f} :ifThe diagnoser must solve the problem of chained faults. The main effect of any subsystem fault is less material cut. Therefore, the following subsystem will observe greater currents. Nevertheless, this situation does not involve two faults. Tf3 (table 4) solves the problem by including previous subsystem faults in the diagnosis.
\n\t\t\t\tDiagnoser.
Fault and recovery transition definition
PVF place diagnoses subsystem faults. IF dynamics can also be diagnosed by collecting diagnosed faults temporal information. Table 5 shows the information required for IF dynamics diagnosis:
\n\t\t\t\tWhere CNT, FT, T, Ta, DF, Ps, LST, and OTR have the same meaning as in equations (1), (2), (3), (4), (5), and (6).
\n\t\t\t\tThe diagnoser builds a table like table 5 for each IF. Moreover, the diagnoser updates the tables each sampling time. Figure 13 shows the updating algorithm.
\n\t\t\t\tUpdating algorithm.
Therefore, the diagnoser computes LST and OTR each sampling time. Moreover, each table must be cleared when the faulty device is replaced with a new one.
\n\t\t\tThis chapter presents the problem of diagnosing IFs dynamics. We have presented a way of modelling IF dynamics and we have tested it with real data. The density model allows us to compute the best time to repair or substitute the faulty device. This model does not need historical data.
\n\t\t\tIF dynamics diagnosis has the problem of determine a sliding window size. This problem will be treated in a future work.
\n\t\t\tWe have stated a new definition for IF dynamics and we have proved that the model is able to diagnose the IF dynamics.
\n\t\t\tWe have also presented the integration of IF dynamics diagnosis within a diagnosis technique for discrete event systems based on CPNs. This integration allows the acquisition of temporal information required to compute density and pseudoperiod. Therefore, the diagnosis system will be able to diagnose the IF dynamics.
\n\t\tPollution due to heavy metals possesses a serious issue not only to human health but also to the environment and urban infrastructure. Heavy metals can be found in wastewater, groundwater, lakes, and streams, but also in soils or sediments. Heavy metals come from natural sources, but they can also originate from different anthropogenic activities (Figure 1). Human exposure to them has risen dramatically as a result of an exponential increase in their use in several industrial, agricultural, domestic, and technological applications [1]. Even though metals such as cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), selenium (Se), or zinc (Zn) play essential biochemical roles in the cells of living beings, in high concentrations or as a result of long-term exposure, they are associated with cellular and tissue damage leading to adverse effects and diseases [2]. Notably, arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg) are ranked among the metals of great public health significance [1].
The origin of some of the heavy metals released to the environment.
In order to minimize the damage to public health and other living organisms due to heavy metals, remediation appeared to rectify and redeem the environment after anthropogenic disturbance [3]. Remediation aims to implement and realize efficient actions for the eradication of diverse forms of pollution in three main activities: (1) remove formation damage, (2) testing before each remediation, and (3) routine treatment applications (maintenance) [4].
Remediation is just one of the multiple promising applications of nanotechnology. The utilization of nanotechnology concerning the environment classifies as follows: (1) the design and production of sustainable materials, (2) remediation using nanostructures, and (3) nano-based sensors [5]. Nanotechnology may contribute to any of the remediation facets, listed above, providing high selectivity (speciation and clean-up) and sensitivity (preconcentration) [6] to achieve trace levels in water, usually 100 microgram per gram.
The nanomaterials mainly used for removal are metal and iron oxides as they facilitate the precipitation and reduction of heavy metals to less harmful ionic species [7]. They are followed by carbonaceous materials such as fibers, carbon nanotubes, graphene, and graphene oxide. The porosity and surface area of carbonaceous materials allow them to easily form membranes for an efficient metal removal; besides, they serve as support of nanometals for easy recovery [8, 9, 10]. Other nanostructures based on silicon, such as silica and zeolite, are widely used due to its versatility and availability. The reactive and high surface area of porous silica allows the functionalization for multiple adsorptions of heavy metals, and zeolite allows selective separation of cations by ion exchange and adsorption from water [11, 12].
From this brief review, three main facts can be listed: (1) the importance of removing toxic heavy metals, (2) the role of nanotechnology on remediation, and (3) the use of diverse nanostructures for the removal of heavy metal from water. In this context, the need for rapid and efficient removal of pollutants focused on toxic heavy metals with high selectivity and sensitivity from water is addressed in this work [1, 5, 6], considering the advantages offered by nanostructures. This book chapter summarizes some examples of nanostructures employed for wastewater treatment considering the three aspects listed above, as well as ways to reach heavy-metal concentrations lower than trace-level limit, after removal.
Nanotechnology is a multidisciplinary science focused on the design, processing, manipulation, and application of new materials at the nanoscale; it means between 1 and 100 nanometers (nm). It also comprises the modification of some properties of bulk materials. Nanostructures are nanometric materials with a defined morphology. They form a self-assembling of atoms, molecules, or macromolecules.
Nowadays, thanks to the application of nanostructures, nanotechnology can contribute to the solution or mitigation of part of the worldwide problems involving environmental pollution [13]. The main strategies focus on different categories: (1) sustainable design and synthesis of nanostructures; (2) new generation technology using functionalized nanostructures to remove contaminants from water, air, and soil; (3) sensor design for the estimation and quantification of pollutants; (4) study of toxicity and evaluation of environmental and health implications associated with the use of nanostructures for remediation; (5) emerging solutions [5, 14, 15].
Nanostructures are materials or structures with at least one dimension in the nanometer scale (1–100 nm) [16]. A zero-dimension (0D) material has a nanometer scale in each of the three directions (x, y, z), while a one-dimension (1D) material has a nanometer-scale only in two directions. A two-dimension (2D) structure contains a nanometer-scale only in one direction. Three-dimensional (3D) nanostructures are included in this classification even though their dimensions are higher than 100 nm, but their construction is a hierarchical architecture that grows in all directions using 0D, 1D, and 2D nanostructures. Figure 2 presents a scheme classification of some examples of nanostructures: 0D nanostructures are quantum dots, nanoparticles, fullerenes, clusters, metal nanoparticles, or graphene quantum dots, among others. One-dimensional structures are carbon nanotubes, single layer graphene nanoribbons, graphite nanoribbons, or nanobars. Two-dimensional nanostructures include nanofilms, graphene, graphene oxide, and two layered graphene. Three-dimensional nanomaterials include graphite, metallic-organic frameworks, aerogels, and composites.
Schematic for the classification of low dimensional nanostructures: zero dimension (0D), one dimension (1D), two dimension (2D), and three dimension (3D). Examples for each dimension are mentioned in the text.
All the elements from the periodic table are suitable to form nanostructures in all dimensions, individually or combined, increasing in this way the variety of properties, behavior, and functionality. As a result, new opportunities are now available with nanotechnology to enhance the efficiency of existing methods for detection, quantification, and remediation of pollutants. However, environmental remediation is still a challenge and some important facts should be considered, such as selectivity, efficiency, reusability, eco-friendly, and low cost. It is important to notice that nanomaterials are non-biodegradable and so they tend to accumulate in organisms, leading to serious health problems when ingested by humans [17]. Thus, using nanotechnology either as a remediation strategy or measurement method needs to consider the recuperation and reutilization of nanomaterials as mandatory steps.
Some authors have conducted relevant investigations to know the levels of heavy metals in water. Florence Lagarde and Jaffrezic-Renault proposed complementary methods for the monitoring and treatment of metal-contaminated water [18], which Agostino et al. resumed in two main sections: fast and conventional [19]. The first consists of the development of in-situ, low cost, non-specific early warning systems to operate online. The second one is focused on accurate detection and quantification using conventional methods that are expensive, more time consuming, and require manual operation.
Detection and quantification of heavy metals are crucial tasks in the characterization of different water types: surface water, groundwater, tap water, and wastewater. Nowadays, the monitoring of toxic effects becomes mandatory since water resources are being scarce, and the development of monitoring tools is essential for an easy, fast, and accurate detection of heavy metals in a few steps. Sensing is an integral part of both fast and conventional measurement methods for the quantification of heavy metals proposed in nowadays research [20]. Nanotechnology used in sensing improves not only the efficiency but also the selectivity and reduces the time needed in each step of the methodology of quantification of heavy metals in water. Sensing using nanotechnology can be implemented in-situ for a faster detection of heavy metals in water with a low-cost technique, followed by the quantification with a sensitive and accurate analytical method on-lab such as atomic absorption spectroscopy, X-ray fluorescence, electrochemical techniques, and among others that are described below.
Some of the 0D nanostructures used for the detection and quantification of heavy metals in water are the metal nanoparticles. These nanoparticles can be made of gold (Au), silver (Ag), titanium oxide (TiO2), iron oxide (Fe3O4), zinc sulfide (ZnS), or cadmium sulfide (CdS) due to the facility that these materials offer to modify their surface, and the easiness they offer to control their reproducibility and flexibility in an aqueous medium [21]. The heavy metals are detected by a spectroscopic, electrochemical, or optical technique. The methodologies adapted to these strategies using nanostructures have some advantages and disadvantages as can be expected [22]. The spectroscopic methods allow not only the detection but also the quantification step, and they are usually very sensitive to multiple heavy metal ions. Some examples of spectroscopic techniques are ICP-MS (inductively coupled plasma mass spectrometry), AAS (atomic absorption spectroscopy), HR-SPS (high-resolution surface plasmon resonance spectroscopy), and SERS (surface-enhanced Raman spectroscopy). SERS is useful for sensing applications since the surface plasmon resonance of metal nanoparticles can be identified with a detection limit of 10 ppt in a pH buffered solution, and of 70 ppt in a real sample when compared with AAS, whose detection limit is around 0.2 ppb [22]. ICP-MS quantifies a wide variety of elements in one-single analysis step with high accuracy, sensitivity, and selectivity. Low detection limits for metal ions can be achieved by using ICP-MS, normally in the ppt range [23, 24]. Such techniques are typically used for quantification but not for sensing. The main disadvantage of the spectroscopic methodologies is the high cost of the analytical instrumentation, the time-consuming procedures, and the use of sophisticated equipment that requires trained personnel [22, 25]. On the other hand, electrochemical methods are low cost, low time-consuming, and easy to handle. They can be conducted under any of the following techniques: potentiometric, amperometric, voltammetric, impedimetric, or electrochemiluminescence. They have a high sensitivity and low detection limits (from 0.25 ng/L to 0.12 μg/L) [26, 27, 28]. The main advantage of the electrochemical methods is the possibility of sensing or monitoring of pollutants with in-line systems coupled with water streams. However, the design and miniaturization of the electrodes needed to build portable sensors are one of their major challenges [22, 28]. The use of optical methods for the detection of heavy metals is also an economical and fast option. Optical procedures usually utilized for this purpose are absorption, reflection, or luminescence spectrometry. The main limitations could be that some non-optical indicators interfere with some metal ions [22].
Table 1 summarizes some examples of detection and quantification of heavy metals using nanostructures, reported in recent literature, where the spectroscopic, electrochemical, and optical methodologies have been applied.
Ion | Nanostructure and sizes | D | Main methodology | Ref. |
---|---|---|---|---|
Cd2+ Hg2+ Cu2+ | Spherical gold nanoparticles ~15 nm | 0D | Smartphone-based colorimetric reader system | Xiao et al., Gan eta al., and Cao et al. [29, 30, 31] |
Hg2+ Pb2+ Cd2+ | Mn–doped ZnS quantum Dots ~5 nm | 0D | Detection through the quenching of QD emission | Devaiah Chonamada et al. [34] |
Hg2+ | Spherical silver nanoparticles (10–30) nm | 0D | Green approach using aqueous extract of Vigna mungo beans for reduction | Choudhary et al. [35] |
Ag+ Cu2+ Hg2+ | Core-shell nanoparticles. ZnS coated up conversion nanoparticles 60 nm in diameter and 20 nm in the shell | 0D | Detection of residuals in water using ion metals | Chu et al. [36] |
Pb2+ Cd2+ Cu2+ Zn2+ | Rhodium/antimony co-doped TiO2 nanorod and titanate nanotube ~20 nm | 2D in 2D | Quantification and removal of metals and degradation of organic pollutants. The adsorbed metal enhanced the photocatalytic degradation of organic pollutants | Dhandole et al. [37] |
Pb2+ Cd2+ | Functionalized Fe3O4/NaP zeolite nanocomposite ~30 nm | 0D in 3D | Quantification of metals and removal of bacteria | Zendehdel et al. [38] |
Pb2+ Cd2+ | SnS-decorated Bi2O3 nanosheets (3–4) nm | 2D in 3D | Electrochemical detection of Cd(II) and Pb(II) in real samples from lake and tap water | Jin et al. [39] |
New methodologies using nanostructures for sensing and quantification of heavy metals.
For a fast and in situ detection, some authors propose an innovative and cheap methodology based on an online operation aided by a smartphone. The strategy uses the wavelength shifting of the resonant plasmon. It means that nanoparticles suffer a color change, from red to purple, when cadmium (Cd2+), mercury (Hg2+), or copper (Cu2+) ions are present on a solution [29, 30, 31]. The nanosensor uses a gold nanoparticle-based assay platform that records, processes signals within the range of 1 ng/mL–32 ng/mL of Hg2+, and proposes a concentration of 0.28 ng/mL as the limit of detection [29]. The in situ detection of Cd2+ with the nanogold-based detection system controlled by the smartphone is fast and reliable as it displays a linear range for the Cd2+ concentration, between 2 and 20 μg/L with a detection limit of 1.12 μg/L [30]. This range is below the permissible limit of Cd2+ ion in water and smaller than those based on non-portable and other nano-based technologies [32]. The main advantage for these types of systems is the portability, as well as the immobilization of metal ions to gold nanoparticles that allows the tuning of sensitivity. For example, the aptamer is a particular recognition of Cd2+ [33], with a low recognition of Pb2+, and acceptable detection even though the specimen contains other metal ions in high concentration [30].
The quenching of quantum dot (QD) was studied for cadmium (Cd2+), mercury (Hg2+), and lead (Pb2+) to understand the colorimetric/visual sensors. The composite formed by Mn-doped ZnS QD and graphene oxide detects Pb2+ down to 0.4 ppb. Further, the interaction between the metal ion and the passivating ligands attached to the QD also contributes partway to the specific detection combined with the quenching of QD [34]. The green approach is also present on the fast detection, which is vital since the production of nanostructures requires to minimize the use of solvent, energy, time, and the release of odd substances to the environment. An extract of Vigna mungo beans was used as a reducing agent of nanoparticles to minimize the use of traditional chemical compounds and solvents; the detection limit for Hg2+ ions was near 0.13 μM [35].
In previous investigations, two processes were combined to enhance the implementation of nanostructures on heavy metal detection [36, 37, 38]. The main advantage of this approach is that after the detection and quantification steps, the removed metal agglomerates in a nanometric size scale that is useful for further remediation. After quantification and removal of any heavy metal, it is possible to convert core-shell nanoparticles, such Ag+, Cu2+, and Hg2+ coated on ZnS and use them for the detection of another kind of contaminants. The ZnS nanoparticle can remove up to 3.98 μmol of Ag+ [36]. The metal adsorbed on TiO2 nanostructures enhanced the photocatalytic degradation of organic pollutants. The degraded pollutants were orange (II) dye and bisphenol-A. Metals increased photodegradation after 5 hours in a batch experiment (70% of dye and 80% of Bisphenol A) due to the photodeposition of metal ions on the TiO2 nanoparticle surface [37]. Functionalized Fe3O4/NaP zeolite nanocomposite removed metal, more than 95%, after removed bacteria (Bacillus subtilis) [38]. A different methodology was employed for in situ measurements by electrochemical detection using SnS-decorated Bi2O3 nanosheets. The composite exhibited high sensitivity and efficient detection for the removal of heavy metals with high toxicity, which limits of detection was 1.50 nM for Cd2+ and 1.40 nM for Pb2+ [39].
Quantification of metallic ions is essential during the different steps of the water cycle that goes through various biological processes. The main quantification techniques are atomic absorption spectrometry, inductively coupled plasma mass spectrometry, anodic stripping voltammetry, X-ray fluorescence spectrometry, and microprobes. These techniques require sample pretreatment procedures, analyte pre-concentration steps, and expensive instrumentation [40]. Due to the presence of dangerous heavy metal ions in water, an instantaneous measurement and quantification are desirable. The use of nanotechnology, along with the investigation of novel approaches, will allow the quantification of heavy metals in an inexpensive, rapid, and simple way. For example, Stenberg, Massad-Ivanirb, and Ester Segal presented a nanostructured porous silicon (Si) biosensor for the detection and quantification of copper ion in real water samples. The monitoring is based on the Laccase relative activity. Lacasse is an enzyme, a multi-copper oxidase, immobilized within the oxidized nanostructured porous silicon. The Laccase-based biosensor exhibited a lower detection limit of 1.30 μM, smaller than other values [41].
Quantification of the metal ion content, as well as the chemical speciation of the different chemical forms of a specific metal in water, becomes essential to estimate its toxicity and persistence in living organisms. The environmental health effects can be understood by the study of natural chemistry, quantification, and speciation of chemical species of metal ions. The chemical form of any metal depends strongly on the chemical conditions in which it is exposed, and the toxicity not only relays on its ionic form but also on the chemical species that formed. For example, the methylmercury ion (CH3Hg+) is the toxic specie of the inorganic mercury II (Hg2+). Speciation analysis requires complex, expensive, and time-consuming pre-separation analyses, which are incompatible with in situ measurements. Guerrini et al. [42] presented a Surface-Enhanced Raman Scattering (SERS) methodology for the chemical speciation of Hg2+ and CH3Hg+. In this work, the SERS consisted of an active platform of closely spaced spherical gold nanoparticles anchored on polystyrene microparticles. The ion receptor, mercaptopyridine (MPY), forms strong bonds between the gold atoms and the mercaptan group, and coordinates a nitrogen group with both species Hg2+ and CH3Hg+. The coordination with nitrogen is determined by the changes in vibrational SERS of MPY, which could give insights on the qualitative and quantitative modifications correlated with Hg2+ or CH3Hg+.
Tague defines remediation as “all measures taken for treatment of damaged wells for restoring an optimal performance” [4]. Nowadays, the environment requires those actions for the protection of human health and all living systems on Earth. Environmental remediation consists of the eradication, removal, or transformation of contaminants from natural resources [43]. Although remediation is a complex task, nanoremediation has emerged as an optimal alternative for the removal of pollutants from different waters (groundwater, surface water, and residual water), soil, air, and sediments [43].
Water is by far, one of the most contaminated resources in the planet; that is why the remediation and removal of contaminants are an urgent need together with easy and fast monitoring tools. The available treatments used for removal of heavy metals from water are classified as follows: chemical precipitation, membrane filtration, ion exchange, reverse osmosis, and adsorption [44]. The adsorption using nanomaterials has been of great interest since several nanostructured adsorbents have demonstrated a high performance [44, 45, 46]. Adsorption on nanostructured materials is complicated, but some authors have proposed possible mechanisms that depend mainly on the nature of the surface area. The fundamental mechanisms are based on physical adsorption (physisorption), chemical adsorption (chemisorption), electrostatic attraction, and sorption-precipitation [9, 47, 48]. Figure 3 shows a schematic representation for the adsorption mechanisms of heavy metal on porous nanomaterials. Lu et al. reported that biochar, a 3D network, is a material rich in cations and surface interaction sites for lead adsorption. Electrostatic cation exchange or metal exchange reactions mechanisms may occur when calcium (Ca2+), magnesium (Mg2+), potassium (K+), and sodium (Na+) ions released from biochar in the adsorption of Pb ions, but the electrostatic interaction and surface complexation with pi-cationic and functional groups interaction, may also happen in the adsorption of Pb ions [48].
Fundamental mechanisms for the adsorption of heavy metal ions on porous nanostructured materials.
The removal of heavy metals with carbon nanotubes (CNT) varies because the adsorption mechanisms depend on the affinity order of metals ions and the surface functionalization of CNT. Some examples of adsorption have been summarized by Ihsanullah et al. [47]. The mechanism is based on the release of protons (H+) from the surface when oxidation of CNT was achieved. After that, the attachment of divalent metals occurs on the surface. This process depends on the concentration of the metal ion. A higher number of ions increase the pH value due to the increment of H+ in the solution. It was found that the adsorption of divalent ions, such as Cd2+ and Zn2+, is dominated by a combination of physisorption (Van deer Waals forces) and sorption-precipitation when the pH is increasing in the solution. It means that the acid treatment and surface modification have a significant effect on the adsorption capacity of CNT. Some examples of carbon materials used for metal removal under different metal concentrations in the ppm range are listed in Table 2 [49, 50, 51, 52]. Those experiments were performed without temperature or pH variation, except for the analysis that uses activated carbon from coconut waste, where the pH was adjusted.
Material | Copper | Lead | Other metals | Reference |
---|---|---|---|---|
Activated carbon | — | 30 ppm – 83% 50 ppm – 74% | Chrome 30 ppm – 50.6% 50 ppm – 48.2% Nickel 30 ppm – 90% 50 ppm – 87.8% Cadmium 30 ppm – 86% 50 ppm – 84% | Karnib et al. [49] |
Activated carbon (coconut waste) | 126 ppm 73% pH 5 | 709 ppm – 100% pH 4 | Nickel 996 ppm – 92% pH 4 | Kadirvelu et al. [50] |
Graphite oxide | — | 30 ppm – 85% 50 ppm – 84% | Chrome 30 ppm – 63% Nickel 30 ppm – 89.9% | Sheet et al. [51] |
Carbon nanotubes | 100 ppm – 41% | 40 ppm – 83% | Chrome 0.5 ppm – 66% Nickel 15 ppm −75.4% Cadmium 6 ppm - 87% | Mubarak et al. [52] |
N-doped carbon nanotubes growth on red volcanic rock | 10 ppm – 82% 20 ppm – 84% 40 ppm – 51% | 10 ppm – 91% 20 ppm – 90% 40 ppm – 93% | — | Gonzalez Hodges [53] |
N-doped carbon nanotubes growth on black volcanic rock | 10 ppm – 90% 20 ppm – 95% 40 ppm - 71% | 10 ppm – 91% 20 ppm – 81% 40 ppm – 98% | — | Gonzalez Hodges [53] |
N-doped carbon nanotubes growth on yellow volcanic rock | 10 ppm – 92% 20 ppm – 83% 40 ppm – 63% | 10 ppm – 100% 20 ppm – 89% 40 ppm - 99% | — | Gonzalez Hodges [53] |
Red volcanic rock | 5 ppm – 15% | 5 ppm – 14% | — | Mabel et al. [54] |
Heavy metal removal percentages using different carbon-based materials.
Table 2 presents higher percentages of copper and lead removal when using nitrogen-doped carbon nanotubes grown on volcanic rock [53] than other carbon materials or red volcanic rock [54] without any treatment. The increased efficiency of these carbon nanotubes grown on volcanic rock can be correlated with the presence of nitrogen on their graphitic lattice. Doping carbon nanostructures with heteroatoms, such as nitrogen, increases the adsorption capacity without any acid treatment [4]. Nitrogen has a similar atom diameter but an extra electron as carbon, favoring the atom replacement, and the electron-donating properties [55]. N-doping occurs in different forms, such as pyridine, pyrrole-like, and quaternary-N salts, but may include nitrogen oxide groups and amines [56]. In particular, quaternary nitrogen atoms are considered to increase the adsorption capacity by electrostatic interaction, as the surface carbon nanostructures doped in this way are more negatively charged than the undoped ones [55, 57]. Besides, nitrogenated carbon nanostructures act as reducing agents for toxic Cr6+ converting it into harmless Cr3+ [55].
The high surface area found on porous materials, such as the silica-based ones, is attributed to the 3D network that displays a hierarchical organization that can be found at different scales (nano, micro, and milli). The nanostructured organization is mainly attributed to the form and size of the pore, which allows the accommodation of other nanostructures, as well as the functionalization of the surface. The methodology to produce nanostructured porous silica is based on the liquid-crystal template mechanism. This allows getting pores around 2–10 nm in size [58]. A surfactant at high concentration is set at a specific pH and temperature values to form a liquid-crystalline phase. Then, the crystalline form may be organized by spherical or rod-shaped micelles arranged on a periodic 3D structure, which serves as a template to be filled with tetraethoxysilane (TEOS) or tetramethylorthosilica (TMOS). Finally, the surfactant is removed to form a mesoporous material [59].
Surface functionalization is a useful tool for the realization of a well-defined set of functions which improves the adsorption properties of silica. It can be carried out by chemical bonds or physical interactions. Silica surface functionalization is of interest due to the multiple options that it offers for heavy metals removal. For example, the thiol-functionalization of mesoporous silica has been proved to exhibit a high affinity for thiophilic heavy-metal ions like Cu2+, Zn2+, Cr3+, and Ni2+ but focused on highly toxic metals such Hg2+. At the same time, the amino functionalization increases the efficiency of the adsorption of Hg2+ [59, 60]. For some specific types of silica materials such as SBA-15 (Santa Barbara Amorphous-15), the imidazole functionalization improves the selective affinity adsorption of Pd2+ and Pt2+, despite the high concentration of Ni2+, Cu2+, and Cd2+ in the mixture [61]. Specific morphologies, such as PMO-like (periodic mesoporous organosilica) or spherical mesostructured, increased the surface area and the particular site of functionalization, consequently, affinity, and selectivity are improved [62, 63].
Zeolites are known as microporous materials with a crystalline structure composed by tetrahedral building blocks of alumina (AlO4) and silica (SiO4) that give rise to a three-dimensional network linked by oxygen atoms [64]. Zeolites are an effective, economical, and eco-friendly option to remove heavy metals and organic contaminants from wastewater [64, 65, 66]. There are more than 40 naturally occurring zeolites each with different physicochemical properties. Among their most interesting characteristics are their high chemical stability, molecular sieve, adsorption, and ion exchange capacity [67].
The Si/Al ratio in the structure of a zeolite determines its adsorption and ion exchange capacities, which are directly related to the amount of aluminum contained in the natural zeolite [66], so the isomorphic replacement of Si4+ by Al3+ (aluminum-rich zeolites) will have a more negative charge on the framework, providing a higher affinity for polar molecules. The negative charge is balanced with interchangeable cations (generally sodium, potassium, or calcium), therefore these cations are used for cation exchange processes in solutions containing lead, chromium, or mercury, among others ions [68]. The adsorption capacity of zeolites also depends on the charge density and the diameter of the hydrated ion, which is why zeolites have a great potential to remove heavy metal ions from wastewater [64].
Natural zeolites such as clinoptilolite, mordenite, and chabazite were investigated for the removal of heavy metals [64, 65]. The natural zeolite clinoptilolite has attracted attention because of its abundance. The selectivity of the sodium form of clinoptilolite, extracted from natural deposits, was found to be Pb2+ > Cd2+ > Cs+ > Cu2+ > Co2+ > Cr3+ > Zn2+ > Ni2+ > Hg2+. This behavior confirmed that natural zeolites have a great ability to selectively remove metallic ions from wastewater. In particular, sodium loaded zeolites resulted the most effective because sodium acts as an exchangeable ion with heavy metals [69]. Further, clinoptilolite exhibited a higher capacity than carbonaceous materials such as carbon nanotubes (CNTs) for lead removal (up to 15.5 times higher) [70, 71].
Nano zeolite [72] and modified zeolites [73, 74, 75] demonstrated a higher removal capacity of lead than that reported by natural zeolites, even much higher than functionalized reduced graphene oxide (RGO) [71]. The silica nano-zeolite X [72] presented an adsorption capacity of 909.09 mg/g of Pb2+ being 5.5 times higher compared to that reported for natural clinoptilolite (166 mg/g) [69]. The main characteristic of nano zeolites and modified zeolites is a larger surface area and pore size, which make them more efficient and facilitate the adsorption of heavy metals and organic molecules, compared to natural zeolites [76]. The efficiency in the removal of heavy metals and other cations will strongly depend on the high surface area of the zeolites, and in their efficiency in removing the metal ions present in wastewater. The surface area in porous materials is determined by the specific surface area (BET) by standard multipoint techniques of nitrogen adsorption. There are several studies where the largest surface area is for nano zeolites (692 m2/g) [76], followed by modified silica natural zeolites (702 m2/g) [72]. A smaller surface area is found for the sodium form of clinoptilolite (70.4 m2/g) [77], and the smallest area is for the simple natural zeolite clinoptilolite (15.36702 m2/g) [78].
Iron oxide nanostructures are gaining attention for metal removal from water due to their high surface area, excellent adsorption capacities, innocuousness with the environment, and easiness of separation as one can make good use of their magnetic properties. Despite their advantageous characteristics, their use in real scenarios has not been proved. It is essential to explore their removal mechanisms, not only for iron oxides but also for zerovalent iron nanomaterials.
Zerovalent iron nanoparticles (nZVI) are considered a strong reducing agent that is bringing degradation to less harmful substances from a wide range of organic and inorganic pollutants. Sorption of co-precipitation of heavy metals on the surface forms an iron oxide or hydroxide shell when nZVI is exposed to air [79]. The most cited example is the transformation of Cr6+, which precipitates on nZVI with corrosion products. However, this property usually depends on the surface functionalization of the nZVI. Functionalization is important since iron nanoparticles form aggregates very easily and suffer oxidation under acidic and oxygenated environments. The best approach is to coat the nanoparticle surface with Fe3O4 or some polymer to reduce the contact with the environmental oxygen, but maintaining the reactivity [79].
Previous studies on the adsorption of Cr6+ on the surface of Fe3O4 show the formation of a different crystalline structure by chemical adsorption. For chromite (FeCr2O4), the Cr6+ is reduced to Cr3+, followed by the precipitation of Cr3+ onto the F3O4 nanoparticles. Low desorption is indicative of adsorption because desorption is due to physical adsorption, mainly by electrostatic interactions. This adsorption process predominates on γ-Fe2O3, as previously demonstrated by X Ray Photoelectron Spectroscopy (XPS). Another evidence of the physical adsorption is the non-modification of the crystallographic structure, an indication that the removal process was not due to a chemical interaction [80, 81]. The positively charged surface on the iron oxide nanoparticles determines the attraction for negatively charged pollutants; the positive charge of iron oxides depends on the polarization of oxygen atoms on the surface, which can be modified by pH. The successful removal of pollutants such as Cr6+ and As5+ is highly pH-dependent [82].
Some of the problems faced when iron nanostructures are used in water remediation are related to their capture, recovery, and reuse. Sometimes complicated steps are required. Many authors have proposed to take advantage of the magnetic nanoparticles as an alternative to capture contaminants and nanostructures that could remain in the environment. Goon et al. [45] studied the capture and quantification of cupric ions at trace level using a composite formed by polyethyleneimine (PEI-) coated with Fe3O4 nanoparticles. They captured trace levels (∼2 ppb) of Cu2+. The PEI is amine-rich, so it captures the Cu ions easily, while the magnetite nanoparticles allow the magnetic separation of the material from water. Hu and coworkers [44] used graphene oxide coped Fe3O4 nanoparticles for highly efficient removal of Pb+2.
The particularity of the composite with graphene oxide is that adsorption capacity improved at pH 7, the natural pH value found in a faucet. They also observed that the system graphene oxide/Fe3O4 could be recyclable because it maintains an 80% adsorption capacity after 10 adsorption-desorption cycles. This process can be generalized to the removal, or capture, of any contaminant by the interaction with a specific functionalized nanoparticle followed by its recovery from water (see the scheme in Figure 4). Tang and Lo [79] consider that magnetic separation could be a low-cost and a convenient method over the use of a membrane-separation filtration method because the separation of tiny magnetic nanoparticles with the adsorbed heavy metal is easier. The magnetic separation usually occurs with the help of a magnetic field or with a hand-held magnet [79].
The different steps on removal of contaminants from water using magnetic nanoparticles.
Natural resources, such as biological systems as microorganisms and plants can also be mixed with nanostructured nanoparticles, usually by cross-linking bonding using a bifunctional reagent; nanoparticles should be inert and biocompatible materials. Even though this methodology is fast, simple, and exhibits an electron transfer, the main disadvantage is the formation of covalent bonds between the functional groups at the outer membranes of the biological living system [18]. Biological systems can be used to directly originate nanomaterials for heavy metal remediation, but they can also be the active coating on nanostructured materials for similar purposes. Even more, the utilization of microbes for intracellular/extracellular synthesis of nanoparticles with different chemical composition, size/shapes, and controlled monodispersity can be a novel, economically viable, and eco-friendly strategy that can reduce toxic chemicals in the conventional protocol [83]. Studies were conducted on some bacteria to produce an iron sulfide compound, which acts as an adsorbent for several toxic metal ions [84]. Nanoparticles obtained from the plant Noaea mucronata were used for the accumulation of heavy metals, such as Pb, Cu, Cd, Zn, Fe, and Ni in groundwater, streams, and rivers [85]. The study conducted on plant species such as Centaurea virgata, Scariola orientalis, Noaea mucronata, Chenopodium album, Cydonia oblonga, Reseda lutea, and Salix excelsa revealed that these plants are very good heavy metal accumulators. Specifically, Noaea mucronata is a suitable accumulator for Pb to a level higher than 1000 ppm [86].
Biological substrates like bacteria, fungi, algae, yeast, and plant derivatives can be immobilized on nanomaterials or nanoparticles and play an essential role in the retention of metal ions. They offer several advantages, namely biodegradability, natural abundance, low cost, simple production, high surface to volume ratio, and various active sites such as carboxyl, hydroxy, amino, sulfate, or phosphate groups [8]. Besides, immobilized biological substrates have been employed as living and non-living cells. The use of dead bacteria offers the possibility to develop continuous flow systems on different solid supports [87]. The main advantage is that dead microorganisms avoid the risk of contamination of water with bacteria. Some examples were summarized by Escudero et al. [87] in an extensive literature review of biological substrates, which includes composites with biological materials and nanostructures as a green alternative in trace elemental preconcentration and speciation analysis. Such nano-based technology has been proved successful on the laboratory scale, but only a few have been used for small-scale testing or commercialization [88]. Specific studies are summarized in the following lines to exemplify the advantages of the biological substrate-based methods in the removal of heavy metals from water. According to the information reported by Escudero and collaborators, the most employed biological substrates are plant-derivatives, bacteria, and fungi, and they are mainly used for water treatments [87].
Heat inactivated Fusarium verticillioides filamentous fungi has been immobilized on nano-silica particles for biosorption of calcium (Ca2+) and magnesium (Mg2+) cations, in helping the preconcentration technique of solid-phase extraction to reduce the hardness of aqueous solutions. Maximum capacities were found to be 1000.0 μmol/g for magnesium and 1333.3 μmol/g for calcium [89]. Lead (Pb) has also been removed using a chromatographic column filled with biomass of this same fungal species immobilized on TiO2 nanoparticles and using hydrochloric acid (HCl) as eluent [90]. Dead coliform bacteria have been immobilized on nanoparticles of titanium oxide (TiO2) for Pb preconcentration which was then analyzed using a flow injection analysis system coupled to a flame atomic absorption spectrometer [91]. The biosorption of aluminum (Al3+) and cadmium (Cd2+) ions over an exopolysaccharide obtained from the bacterium Lactobacillus rhamnosus was possible due to the presence of hidroxyl (▬OH) and carboxyl (▬COOH) groups that facilitated a complex formation with the target analytes [92]. The well recognized bacteria Escherichia coli was immobilized on multiwalled carbon nanotubes to help in the determination of trace elements such as Cd, Co, Cu, and Ni by flame atomic absorption spectrometry [93]; while the metallothionein of the cyanobacterium genera Synechococcus decorated graphene oxide nanosheets for the selective adsorption of Cd from different waters [94]. Different mechanisms are involved in the extraction of metal ions by biological substrates. They include ion exchange, microprecipitation, complexation, and oxide-reduction processes whose adequate election depends basically of the target contaminant and the type of sample to be treated.
There is still a need to develop smarter nanomaterials for remediation purposes on different environments, particularly at trace levels. Significant facts can be listed as follows:
Laboratory experiences on the removal of heavy metals based on the use of nanomaterials must be extended toward real environments potentializing their advantages, but also having in mind their potential risks to human health and to the ecosystems, areas which are poorly understood and might lead to unintended consequences.
The mobility, bioavailability, toxicity, and persistence of a wide variety of manufactured nanoparticles that are already in use, need to be studied [95]. This will provide qualitative and quantitative information for a better understanding of their potential risks, beyond their use in the heavy metal removal. For this issue, full-scale ecosystem-wide studies can be carried out by machine learning programs [96, 97].
More studies focused on profitable strategies applied to the recovery and reuse of nanostructures need to be achieved. They will also allow the proposal of safe handling and disposal guidelines for the already used nanoparticles not only in environmental remediation protocols but in any other area that is already releasing them into the environment.
Accessible and low-cost pollution sensors based on an electrochemical or an optical response that can be easily implemented in an electronic device such as a smartphone, are urgently needed. These technologies are much less expensive that the traditional spectroscopic techniques and can transform every device into a mobile sensor, with probably thousands displayed in huge territorial areas.
Strategies for the maintenance or the improvement of water quality become mandatory in times when water access is becoming more and more difficult. In this context, the presence of different nanomaterials as an efficient, low-cost, and environmentally friendly alternative for the removal of heavy metals from waters to keep healthily environments [98] is a research and educational area that is just emerging.
The increasing pressure that the exacerbated demand on primary goods and commodities is placing to our environment by the development of larger communities will lead us to catastrophic and irreversible damages if a change of actions is not taken. Toxic and dangerous pollutants that are discharged in increasing quantities mean negative impacts to our natural resources, among which water is one of the most vulnerable as it is an absolutely necessary requisite for any form of life on Earth. Palliative solutions have been proposed in different scenarios with variable degrees of success. In this sense, we have presented a summary of some of the alternatives that have been explored to help in the remediation of water reservoirs, based on the application of a variety of nanomaterials coupled with traditional analytical techniques, and on the other hand supported with some innovations in terms of the design, reuse or efficiency.
One of the multiple promising applications of nanotechnology is remediation. The current work mentioned some examples of applied nanotechnology such as testing before and after a remediation strategy is applied. The presented low dimensional (0D, 1D, 2D, and 3D) nanomaterials were coupled with traditional analytical techniques, and on the other hand, supported with some innovations in terms of the design, reuse, or efficiency. Most of the examples showed that concentration values lower than trace levels can be reached, which is an important fact concerning highly toxic metals.
The combination of nanotechnology and remediation opens an avenue of multiple options for environmental improvement strategies. It is an efficient alternative for the sensing, quantification, and removal of contaminants from water, specifically the cleaning of heavy metals, ideally to quantities lower than trace levels, as was exemplified in this chapter. The main advantages of the nanomaterials used for this purpose reside in their high sensitivity and selectivity that can be achieved at a reduced cost and with a lower time-consumption than other non-processed materials (for example volcanic rock shown in Table 2). Also, the nanoscale size means an impressive increment of the surface area of nanomaterials that can be translated into a higher reactivity. The reactivity allows the tuning of selectivity or combined selectivity; it means that any molecule with a specific affinity to any metal can be anchored to the nanostructure surface. Besides, nanostructured materials could be adapted for subsequent remediations and magnetic removal, reducing secondary contamination.
The combination of living systems, nanotechnology, and remediations extends the possibilities. Some microorganisms (dead or dead) are natural accumulators at trace levels and combined with nanostructures make a synergistic effect with improved sorption capacity; such combination allows the production of sustainable materials. Toxicity also concerns with this field, due to one of the main concerns about the extensive use of nanomaterials resides on the fact that the details on the nanostructure interaction with the environment and with different living organisms are still unknown. It means that systematic and extensive studies are required to aid to fill the current voids of data or information. Then, the toxicity is still a limitation on the use of nanomaterials in real samples. Other limitations such as the industrial manufacture tackle multiple difficulties due to do not readily exist regulation neither guidance on information requirements nor safety assessment. Apart from regulation, the right on intellectual property delays the industrial application.
It is true that one of the main concerns about the extensive use of nanomaterials resides on the fact that the details on the nanostructure interaction with the environment and with different living organisms are still unknown. It means that systematic and extensive studies are required to aid to fill the current voids of data or information. Another important issue is the recuperation of the nanomaterials once used. In some cases, the magnetic removal represents a practical option for recovery and reuse, but additional innovative strategies are needed. Overall, the application of nanomaterials to the removal of heavy metals from water is still a vast research opportunity area to be covered by current and future generations of scientists.
The authors declare no conflict of interest.
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