Surface Acoustic Wave Sensor Array Research Articles

Simultaneous Detection of Exosomal microRNAs Isolated from Cancer Cells Using Surface Acoustic Wave Sensor Array with High Sensitivity and Reproducibility.

We present a surface acoustic wave (SAW) sensor array for microRNA (miRNA) detection that utilizes photocatalytic silver staining on titanium dioxide (TiO2) nanoparticles as a signal enhancement technique for high sensitivity with an internal reference sensor for high reproducibility. A sandwich hybridization was performed on working sensors of the SAW sensor array that could simultaneously capture and detect three miRNAs (miRNA-21, miRNA-106b, and miRNA-155) known to be upregulated in cancer. Sensor responses due to signal amplification varied depending on the concentration of synthetic miRNAs. It was confirmed that normalization (a ratio of working sensor response to reference sensor response) screened out background interferences by manipulating data and minimized non-uniformity in the photocatalytic silver staining step by suppressing disturbances to both working sensor signal and reference sensor signal. Finally, we were able to successfully detect target miRNAs in cancer cell-derived exosomal miRNAs with performance comparable to the detection of synthetic miRNAs.

Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2.

In this research, a compact electronic nose (e-nose) based on a shear horizontal surface acoustic wave (SH-SAW) sensor array is proposed for the NO2 detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH3), benzene (C6H6) and acetone (C3H6O). Carbon-based nanostructured materials (CBNm), such as mesoporous carbon (MC), reduced graphene oxide (rGO), graphene oxide (GO) and polydopamine/reduced graphene oxide (PDA/rGO) are deposited as a sensitive layer with controlled spray and Langmuir–Blodgett techniques. We show the potential of the mass loading and elastic effects of the CBNm to enhance the detection, the classification and the discrimination of NO2 among different gases by using Machine Learning (ML) techniques (e.g., PCA, LDA and KNN). The small dimensions and low cost make this analytical system a promising candidate for the on-site discrimination of sub-ppm NO2.

Bulk and Surface Acoustic Wave Sensor Arrays for Multi-Analyte Detection: A Review.

Bulk acoustic wave (BAW) and surface acoustic wave (SAW) sensor devices have successfully been used in a wide variety of gas sensing, liquid sensing, and biosensing applications. Devices include BAW sensors using thickness shear modes and SAW sensors using Rayleigh waves or horizontally polarized shear waves (HPSWs). Analyte specificity and selectivity of the sensors are determined by the sensor coatings. If a group of analytes is to be detected or if only selective coatings (i.e., coatings responding to more than one analyte) are available, the use of multi-sensor arrays is advantageous, as the evaluation of the resulting signal patterns allows qualitative and quantitative characterization of the sample. Virtual sensor arrays utilize only one sensor but combine it with enhanced signal evaluation methods or preceding sample separation, which results in similar results as obtained with multi-sensor arrays. Both array types have shown to be promising with regard to system integration and low costs. This review discusses principles and design considerations for acoustic multi-sensor and virtual sensor arrays and outlines the use of these arrays in multi-analyte detection applications, focusing mainly on developments of the past decade.

ZIF Nanocrystal-Based Surface Acoustic Wave (SAW) Electronic Nose to Detect Diabetes in Human Breath.

In the present work, a novel, portable and innovative eNose composed of a surface acoustic wave (SAW) sensor array based on zeolitic imidazolate frameworks, ZIF-8 and ZIF-67 nanocrystals (pure and combined with gold nanoparticles), as sensitive layers has been tested as a non-invasive system to detect different disease markers, such as acetone, ethanol and ammonia, related to the diagnosis and control of diabetes mellitus through exhaled breath. The sensors have been prepared by spin coating, achieving continuous sensitive layers at the surface of the SAW device. Low concentrations (5 ppm, 10 ppm and 25 ppm) of the marker analytes were measured, obtaining high sensitivities, good reproducibility, short time response and fast signal recovery.

A Novel Surface Acoustic Wave Sensor Array Based on Wireless Communication Network.

A novel surface acoustic wave (SAW) sensor array based on wireless communication network is prepared. The array is composed of four SAW sensors, a wireless communication network module, and a global positioning system (GPS) module. The four SAW sensors of the array are coated with triethanolamine, polyepichlorohydrin, fluoroalcoholpolysiloxane, and L-glutamic acid hydrochloride to detect hydrogen sulfide (H2S), 2-chloroethyl ethyl sulfide (CEES), dimethylmethylphosphonate (DMMP), and ammonia (NH3) at film thicknesses of 50–100 nm. The wireless communication network module consists of an acquisition unit, a wireless control unit, and a microcontroller unit. By means of Zigbee and Lora technologies, the module receives and transmits the collected data to a PC work station in real-time; moreover, the module can control the sensor array’s working mode and monitor the working status. Simultaneously, the testing location is determined by the GPS module integrated into the SAW sensor array. H2S, CEES, DMMP, and NH3 are detected in 300 m at different concentrations. Given the practical future application in environment in the future, the low, safe concentrations of 1.08, 0.59, 0.10, and 5.02 ppm for H2S, CEES, DMMP, and NH3, respectively, are detected at the lowest concentration, and the sensitivities of different sensors of the sensor array are 32.4, 14.9, 78.1 and 22.6 Hz/ppm, respectively. With the obtained fingerprints and pattern recognition technology, the detected gases can be recognized.

Detection of Hazardous Vapors Including Mixtures in Varied Conditions Using a Surface-Acoustic-Wave Device

A surface-acoustic-wave (SAW) sensor was used to detect gaseous chemical compounds ammonia, methane and their mixtures in varied environmental conditions. The sensing chamber was improved using top-cover PMMA with 200-μL fluidic channels, compared with a traditional 1-L four-neck bottle. Small concentrations of ammonia ( ≈ 250 ppb) and methane ( ≈ 9 ppm) were detectable with this SAW sensor array with a sensing film, PNVP with mesoporous carbon hollow nanospheres. Tests of the stability and repeatability showed that the noise of the frequency change is about ± 12.5 Hz within 410 s; the signal loss is less than 9% after 16 detection cycles. According to the results of the sensing measurements, the frequency shift of the ammonia/methane mixture was equivalent to the sum of the frequency shifts of ammonia and methane. In highly humid conditions the loss of signal of ammonia was much larger than that of methane. Based on the results of quantitative measurements of ammonia/methane gaseous mixtures, we found that the superposition effect between sensing signals for ammonia and methane was useable to predict the signal intensity for ammonia/methane gaseous mixtures at varied relative concentrations.

A Non-invasive Monitoring of Propofol Concentration in Blood by a Virtual Surface Acoustic Wave Sensor Array.

Propofol (2,6-diisopropylphenol) is widely used in total intravenous anesthesia. An unknown drug concentration in blood always leads to some side effects in patients with propofol injection. However, the drug concentration in the blood is hard to be continuously measured since invasive sampling causes a loss of blood at each measurement. Here, we introduced a virtual surface acoustic wave sensor array (VSAWSA) to non-invasively detect the propofol concentration in blood through exhaled gases. Calibration was conducted by a parallel test using gas chromatography and mass spectrometry (GC-MS) with solid-phase micro-extraction (SPME) for preconcentration. The limit of detection of VSAWSA reached 0.15 nmol/L for propofol. Six cases of clinical trials was conducted to compare the exhaled propofol concentrations to the plasma concentrations controlled by target-controlled infusion (TCI). The calibration by GC-MS ensured the feasibility, reliability, and accuracy of the VSAWSA (R = 0.9904, p <0.001). The clinical monitoring data by VSAWSA showed an excellent consistency with TCI.

A Low Noise CMOS Readout Based on a Polymer-Coated SAW Array for Miniature Electronic Nose

An electronic nose (E-Nose) is one of the applications for surface acoustic wave (SAW) sensors. In this paper, we present a low-noise complementary metal–oxide–semiconductor (CMOS) readout application-specific integrated circuit (ASIC) based on an SAW sensor array for achieving a miniature E-Nose. The center frequency of the SAW sensors was measured to be approximately 114 MHz. Because of interference between the sensors, we designed a low-noise CMOS frequency readout circuit to enable the SAW sensor to obtain frequency variation. The proposed circuit was fabricated in Taiwan Semiconductor Manufacturing Company (TSMC) 0.18 μm 1P6M CMOS process technology. The total chip size was nearly 1203 × 1203 μm2. The chip was operated at a supply voltage of 1 V for a digital circuit and 1.8 V for an analog circuit. The least measurable difference between frequencies was 4 Hz. The detection limit of the system, when estimated using methanol and ethanol, was 0.1 ppm. Their linearity was in the range of 0.1 to 26,000 ppm. The power consumption levels of the analog and digital circuits were 1.742 mW and 761 μW, respectively.

Detection of coffee flavour ageing by solid-phase microextraction/surface acoustic wave sensor array technique (SPME/SAW)

The use of polymer coated surface acoustic wave (SAW) sensor arrays is a very promising technique for highly sensitive and selective detection of volatile organic compounds (VOCs). We present new developments to achieve a low cost sensor setup with a sampling method enabling the highly reproducible detection of volatiles even in the ppb range. Since the VOCs of coffee are well known by gas chromatography (GC) research studies, the new sensor array was tested for an easy assessable objective: coffee ageing during storage. As reference method these changes were traced with a standard GC/FID set-up, accompanied by sensory panellists. The evaluation of GC data showed a non-linear characteristic for single compound concentrations as well as for total peak area values, disabling prediction of the coffee age. In contrast, the new SAW sensor array demonstrates a linear dependency, i.e. being capable to show a dependency between volatile concentration and storage time.

Bi‐channel Surface Acoustic Wave Gas Sensor for Carbon Disulfide and Methanol Vapors in Polymer Plants

AbstractA C60‐polyphenylacetylene (C60‐PPA) and polyvinylpyrrolidone (PVP) coated two‐channel surface acoustic wave (SAW) crystal gas sensor with a homemade computer interface for data acquisition and data processing was developed and employed to detect carbon disulfide (CS2) and methanol (CH3OH) vapors in polymer plants. The frequency of surface acoustic wave oscillator decreases due to the adsorption of gas molecules on the coated materials of the SAW sensor. Six coating materials (C60‐PPA, nafion, PPA, crytand [2,2], polyethene glycol and PVP) were used to adsorb and detect carbon disulfide and methanol gases. Adsorption of all the six coating materials to CS2 and CH3OH was found to be physical adsorption. The C60‐PPA coated SAW detector exhibited more sensitive to CS2 than the other coating materials. In contrast, the PVP coated SAW detector was more sensitive to CH3OH than the other coating materials. With the two‐channel SAW sensor, the C60‐PPA coated SAW showed a good detection limit of 0.4 ppm and good reproducibility with RSD of 3.37 % (n=10) for CS2. Similarly, the PVP coated SAW also showed a good detection limit of 0.05 ppm and good reproducibility, with RSD of 0.86 % (n=10) for CH3OH. The interference effect of other organic molecules on the SAW detection system was negligible, except for the irreversible adsorption of C60‐PPA to propylamine. The frequency signals from the two‐channel SAW sensor array C60‐PPA and PVP coatings were processed by a back‐propagation artificial neural network (BPN) and multiple regression analysis (MRA). Thus a two‐channel SAW sensor array with BPN and MRA has been successfully applied for the qualitative and quantitative analyses of CS2 and CH3OH in mixtures.

Neural, fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array

Present study evaluates application of adaptive neuro-fuzzy inference system (ANFIS) for concentration estimation of volatile organic compounds (VOCs) by analyzing response matrix of polymer-functionalized surface acoustic wave (SAW) sensor array. The performance of ANFIS is compared with that of subtractive clustering based fuzzy inference system (SC-FIS) and backpropagation artificial neural network (BP-ANN). For analysis, the raw SAW sensor array data is preprocessed by logarithmic scaling followed by dimensional autoscaling and the feature extraction by principal component analysis (PCA). For concentration prediction, the extracted feature vectors were fed as input to the three methods (ANFIS, SC-FIS and BP-ANN) independently. The performance of the three methods were evaluated on the basis of root mean square error (RMSE) and correlation value involving actual and estimated values of concentration. Five sets of SAW sensor array responses are analyzed. The analysis includes both experimental and synthetic (sensor model generated) data sets. It is found that the ANFIS has the least value of RMSE and highest value of correlation compared to SC-FIS and BP-ANN. This signifies the relative superiority of ANFIS method.

Feature Scores Fusion for Chemical Class Recognition of Volatile Organic Compounds by Response Analysis of Surface Acoustic Wave Sensor Array

paper presents fusion based pattern recognition approach of feature scores for chemical class identification of volatile organic compounds (VOCs) by response analysis of model surface acoustic wave (SAW) sensor array. Diverse features are generated by analysis of sensor array response using three feature extraction methods: principal component analysis (PCA), independent component analysis (ICA) and kernel principal component analysis (KPCA). Thereafter feature vectors are fused with three straightforward fusion strategies including i) summation, ii) multiplication and iii) combination of feature vectors. Chemical class recognition efficiency of fused feature is experimentally verified by feeding them to the input of support vector machine (SVM) classifier. Experimental outcomes are based on analysis of 12 data sets generated with SAW sensor model simulation, containing different intensity of noise and outliers. It has been observed that in research of three fusion schemes; fusion by summation of feature vectors achieves persistently highest correct chemical class recognition rate (average 90%) of VOCs followed by combination and multiplication. Though in case of less noisy SAW sensor array response, fusion by combination of feature vectors results comparable class recognition efficiency to that of fusion by summation.

Volatile toxic compound detection by surface acoustic wave sensor array coated with chemoselective polymers deposited by laser induced forward transfer: Application to sarin

In this paper we present a sensor array based on surface acoustic wave (SAW) resonators coated with three chemoselective polymers: polyepichlorhydrine (PECH), polyisobutylene (PIB), and polyethylenimine (PEI), deposited by laser-induced forward transfer (LIFT) using a XeCl laser. The targets for the LIFT process were prepared by matrix assisted pulsed laser evaporation (MAPLE). The sensor array was tested upon exposure to dimethyl methylphosphonate (DMMP, a simulant for nerve gases and pesticides containing phosphonate ester groups), dichloromethane (DCM, an industrial applied toxic compound and simulant for choking agents, such as chlorine or phosgene), and ethyl acetate (EtOAc, a wide spread solvent in medical and industrial applications which can be harmful to humans) vapors, and sarin (GB) gas, showing, for each of the sensors, a different sensitivity to the selected chemical agents. The best sensitivities for DMMP and DCM, obtained by using a PECH coated sensor, are 66.23 Hz/ppm and 0.034 Hz/ppm, respectively, whereas the best sensitivity for EtOAc, obtained using PIB, is 0.33 Hz/ppm. A very low concentration (1.8 ppm) of GB was detected within a time to reach the saturation less than 1 min. The measured concentration is below the median lethal dose inhaled (11.3–16.2 ppm min) [1] . A detection limit of 9.24 ppb was calculated for PECH coated sensor exposed to GB.

Designing Optimal Surface Acoustic Wave Electronic Nose for Body Odor Discrimination

The paper suggests a method for selection of polymer coatings for vapor sensor array based electronic nose systems. It is proposed that prior to actual experimental sensor array fabrication one can (i) consider all available potential polymers whose interaction chemistry with target vapors is documented, (ii) apply statistical clustering methods for grouping polymers of similar chemistry into clusters, and then (iii) make an optimal selection of only one polymer from each cluster for defining the sensors array. The process at stage (iv) would be based on simulation results for vapor discrimination by using best known sensor response models. In this work we have applied this strategy for designing a surface acoustic wave sensor array for discrimination of odorants from living and dead human body. After making optimal selection of polymers we generate a model based sensor array data for 33 volatile organics from human body. In brief, principal component analysis and hierarchical cluster analysis methods have been used for making polymer selection by analyzing vapor-polymer partition coefficient data matrix. The latter is generated by using the available solvation parameters and the linear-solvation-energy relationship for 22 potential polymers. Of these, only 3 polymers—SXPYR (alkyl-amino-pyridyl substituted polysiloxane), P4V (poly 4-hexafluoro isopropanol-styrene) and PEI (poly ethylenimine) were selected to make an optimal set on basis of principal components load analysis. The simulation data were generated by using a 3-element surface acoustic wave sensor array model functionalized with the selected polymers. Finally, the principal component analysis is shown to yield good separability in feature space between the living and the dead body odors, and also between various components in the living body odor.

An Electronic-Nose Sensor Node Based on a Polymer-Coated Surface Acoustic Wave Array for Wireless Sensor Network Applications

This study developed an electronic-nose sensor node based on a polymer-coated surface acoustic wave (SAW) sensor array. The sensor node comprised an SAW sensor array, a frequency readout circuit, and an Octopus II wireless module. The sensor array was fabricated on a large K2 128° YX LiNbO3 sensing substrate. On the surface of this substrate, an interdigital transducer (IDT) was produced with a Cr/Au film as its metallic structure. A mixed-mode frequency readout application specific integrated circuit (ASIC) was fabricated using a TSMC 0.18 μm process. The ASIC output was connected to a wireless module to transmit sensor data to a base station for data storage and analysis. This sensor node is applicable for wireless sensor network (WSN) applications.

English

The paper presents a new approach to surface acoustic wave (SAW) chemical sensor array design and data processing for recognition of volatile organic compounds (VOCs) based on transient responses. The array is constructed of variable thickness single polymer-coated SAW oscillator sensors. The thickness of polymer coatings are selected such that during the sensing period, different sensors are loaded with varied levels of diffusive inflow of vapour species due to different stages of termination of equilibration process. Using a single polymer for coating the individual sensors with different thickness introduces vapour-specific kinetics variability in transient responses. The transient shapes are analysed by wavelet decomposition based on Daubechies mother wavelets. The set of discrete wavelet transform (DWT) approximation coefficients across the array transients is taken to represent the vapour sample in two alternate ways. In one, the sets generated by all the transients are combined into a single set to give a single representation to the vapour. In the other, the set of approximation coefficients at each data point generated by all transients is taken to represent the vapour. The latter results in as many alternate representations as there are approximation coefficients. The alternate representations of a vapour sample are treated as different instances or realisations for further processing. The wavelet analysis is then followed by the principal component analysis (PCA) to create new feature space. A comparative analysis of the feature spaces created by both the methods leads to the conclusion that both methods yield complimentary information: the one reveals intrinsic data variables, and the other enhances class separability. The present approach is validated by generating synthetic transient response data based on a prototype polyisobutylene (PIB) coated 3-element SAW sensor array exposed to 7 VOC vapours: chloroform, chlorobenzene o-dichlorobenzene, n-heptane, toluene, n-hexane and n-octane. Defence Science Journal, 2010, 60(4), pp.377-386 , DOI:http://dx.doi.org/10.14429/dsj.60.494

English

The paper proposes an effective method to design and develop surface acoustic wave (SAW) sensor array-based electronic nose systems for specific target applications. The paper suggests that before undertaking full hardware development empirically through hit and trial for sensor selection, it is prudent to develop accurate sensor array simulator for generating synthetic data and optimising sensor array design and pattern recognition system. The latter aspects are most time-consuming and cost-intensive parts in the development of an electronic nose system. This is because most of the electronic sensor platforms, circuit components, and electromechanical parts are available commercially-off-the-shelve (COTS), whereas knowledge about specific polymers and data analysis software are often guarded due to commercial or strategic interests. In this study, an 11-element SAW sensor array is modelled to detect and identify trinitrotoluene (TNT) and dinitrotoluene (DNT) explosive vapours in the presence of toluene, benzene, di-methyl methyl phosphonate (DMMP) and humidity as interferents. Additive noise sources and outliers were included in the model for data generation. The pattern recognition system consists of: (i) a preprocessor based on logarithmic data scaling, dimensional autoscaling, and singular value decomposition-based denoising, (ii) principal component analysis (PCA)-based feature extractor, and (iii) an artificial neural network (ANN) classifier. The efficacy of this approach is illustrated by presenting detailed PCA analysis and classification results under varied conditions of noise and outlier, and by analysing comparative performance of four classifiers (neural network, k-nearest neighbour, naive Bayes, and support vector machine). Defence Science Journal, 2010, 60(4), pp.364-376 , DOI:http://dx.doi.org/10.14429/dsj.60.493

Optimized design of a SAW sensor array for chemical warfare agents simulants detection

Chemical warfare agent simulants detection is of great interest for security reasons. An electronic nose based on Surface Acoustic Wave (SAW) sensors has been developed and sensor polymer coatings have been optimized. These allowed us to detect very low concentrations of certain simulants. A good classification was achieved.

A fuzzy filter to study the selectivity and sensitivity of a SPME enhanced SAW sensor system characterizing virgin olive oil aroma

An electronic nose based on a solid phase microextration (SPME) unit coupled with a surface acoustic wave (SAW) sensor array has been optimized towards the analysis of virgin olive oil aroma. The parameters of the system, the SPME fibre coating and the kind of carrier gas, have been adjusted to get the optimum response with the minimum relative standard deviation. The selectivity and sensitivity of the sensors were studied by means of a fuzzy filter based on the membership grade functions of the sensor responses for the “non-lampante and “lampante” classes. A multilayer perceptron (MLP) was designed to classify non-lampante and lampante virgin olive oils. Only one sample out of 26 was erroneously classified in the training set, whereas a classification rate of 100% was obtained in a external validation set of 16 samples.

Wine classification with a zinc oxide SAW sensor array

An array of surface acoustic wave (SAW) sensors using Zinc oxide films has been developed in order to discriminate Spanish wine coming from different grape varieties and elaboration processes. Sensor array is composed by eight surface acoustic wave sensors, one of them used as reference sensor and the others coated with diverse polymers by spray coating technique. Linear techniques as principal component analysis (PCA) and non-linear ones as probabilistic neural networks (PNN) have been used for pattern recognition. A classification success rate (correct predicted number over total number of measurements) of 90% has been achieved.