Hybrid Sensor Array Research Articles

Virtual sensor-based proxy for black carbon estimation in IoT platforms

Black carbon (BC) has been under the spotlight of research during the last few years due to its non-regulation, its role in air pollution, and its hazardous effects. Given the high cost of the instrumentation needed to measure BC concentrations, data-driven techniques have been adopted to implement proxies that provide BC measurements from other sensor measurements. These sensors may present data quality issues due to maintenance actions, loss of data, or relocation, among others. In this paper, we propose a data-driven proxy model for BC estimation that is powered by a hybrid sensor array, including physical and virtual sensors created from machine learning techniques and governmental air quality monitoring networks. Therefore, the proposed method provides an accurate alternative to traditional data-driven BC proxies in scenarios where some physical sensors are unavailable. The results show how a BC proxy can be partially implemented using virtual sensors, obtaining only an increase in the estimation error of around 4%, allowing the estimation of BC levels even when some physical sensors are absent.

A novel hybrid sensor array based on the polyphenol oxidase and its nanozymes combined with the machine learning based dual output model to identify tea polyphenols and Chinese teas

A novel sensor array was developed based on the enzyme/nanozyme hybridization for the identification of tea polyphenols (TPs) and Chinese teas. The enzyme/nanozyme with polyphenol oxidase activity can catalyze the reaction between TPs and 4-aminoantipyrine (4-AAP) to produce differences in color, and the sensor array was thus constructed to accurately identify TPs mixed in different species, concentrations, or ratios. In addition, a machine learning based dual output model was further used to effectively predict the classes and concentrations of unknown samples. Therefore, the qualitative and quantitative detection of TPs can be realized continuously and quickly. Furthermore, the sensor array combining the machine learning based dual output model was also utilized for the identification of Chinese teas. The method can distinguish the six teas series in China, and then precisely differentiate the more specific tea varieties. This study provides an efficient and facile strategy for the identification of teas and tea products.

Selection of Classifiers to Enhance Efficacy of Metal/Organic Hybrid Sensor Array for VOC and Toxic Gas Identification

Modern developments in gas sensor technology include a decrease in size and an increase in sensitivity and selectivity. These improvements, paired with postprocessing tools, such as machine learning, are pushing gas detection toward viability for complex tasks, such as volatile organic compound (VOC) analysis in human breath. In our research, we use a sensor array fabricated in our lab featuring a hybrid combination of metals and organic polymers [palladium (Pd), zinc oxide (ZnO), polypyrrole (PPy), and polyaniline (PANI)] designed to detect a range of VOCs and toxic gases (CO, H2, CH3OH, and NO2). An exhaustive analysis of 25 machine learning classifiers using three different feature sets was completed to find the best classifier and feature set combinations for one versus rest gas classification. We determined that ensemble classifiers, using normalized sensor data as a feature set, yield the best classification results. From these results, we demonstrated that Pd, PPy, and PANI are best suited to identify H2, NO2, and CH3OH, respectively. Furthermore, PANI is best suited to identify CO, so we correctly identified four gases from three sensor materials with sensitivity values all above 85%. These promising classification results could allow us to expand our set of gases and, therefore, make this sensor array viable for real-world applications.

Hybrid Gas Sensor Array to Identify and Quantify Low-Concentration VOCs Mixtures Commonly Found in Chemical Industrial Parks

The chemical industrial park is an important source of volatile organic compounds (VOCs) emissions which is difficult to monitor due to its diversity and low concentration. In this work, a hybrid sensor array consisting of six gas sensors (four semiconductor sensors, one electrochemical sensor, and one photo ionization detector) was constructed to measure VOCs. Three most frequently detected VOCs in a Chinese fine chemical industrial park, namely toluene, dichloromethane, ethyl acetate, and their mixtures, were tested all below 5 ppm (even below 1 ppm in many cases). Support vector machine (SVM) and back-propagation artificial neural network (BP-ANN) based on different input features, including steady-state responses (SS), the first four components preprocessed by principal component analysis (PCA) and linear discriminant analysis (LDA), were trained and evaluated. The 10-fold cross validation results indicate that SS-SVM and SS-ANN are two best models with accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula> of about 94% and 0.95 for the validation set, respectively. Compared with LDA, PCA is more suitable for dimension reduction in this study, since the performance of models with the first 4 PCs are closer to that of models with SS. The results demonstrate that the proposed hybrid sensor array system with SVM and ANN models is able to identify and quantify low-concentration VOCs mixtures. This method is promising to be used for low-cost and online monitoring of VOCs emissions in chemical industrial parks.

A Capacitive and Piezoresistive Hybrid Sensor for Long‐Distance Proximity and Wide‐Range Force Detection in Human–Robot Collaboration

Electronic Skins Safety is a core concern in human-robot collaboration (HRC), and the active safety control has attracted widespread attention. A capacitive and piezoresistive hybrid sensor array (e-skins) for longdistance proximity and wide-range force detection is presented in article 2100213 by Zhenlong Wang, Jie Zhao, PingAn Hu, Jia Zhang and co-workers. The performance for controlling commercial robots in HRC shows the potential usages in complex conditions.

Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath.

This article reports on a noninvasive approach in detecting and following-up individuals who are at-risk or have an existing COVID-19 infection, with a potential ability to serve as an epidemic control tool. The proposed method uses a developed breath device composed of a nanomaterial-based hybrid sensor array with multiplexed detection capabilities that can detect disease-specific biomarkers from exhaled breath, thus enabling rapid and accurate diagnosis. An exploratory clinical study with this approach was examined in Wuhan, China, during March 2020. The study cohort included 49 confirmed COVID-19 patients, 58 healthy controls, and 33 non-COVID lung infection controls. When applicable, positive COVID-19 patients were sampled twice: during the active disease and after recovery. Discriminant analysis of the obtained signals from the nanomaterial-based sensors achieved very good test discriminations between the different groups. The training and test set data exhibited respectively 94% and 76% accuracy in differentiating patients from controls as well as 90% and 95% accuracy in differentiating between patients with COVID-19 and patients with other lung infections. While further validation studies are needed, the results may serve as a base for technology that would lead to a reduction in the number of unneeded confirmatory tests and lower the burden on hospitals, while allowing individuals a screening solution that can be performed in PoC facilities. The proposed method can be considered as a platform that could be applied for any other disease infection with proper modifications to the artificial intelligence and would therefore be available to serve as a diagnostic tool in case of a new disease outbreak.

Correction: Label-free non-invasive fluorescent pattern discrimination of thiols and chiral recognition of cysteine enantiomers in biofluids using a bioinspired copolymer–Cu2+ hybrid sensor array regulated by pH

Correction for ‘Label-free non-invasive fluorescent pattern discrimination of thiols and chiral recognition of cysteine enantiomers in biofluids using a bioinspired copolymer–Cu2+ hybrid sensor array regulated by pH’ by Zi-Yang Lin et al., J. Mater. Chem. B, 2018, 6, 6877–6883.

Comparison of conductometric behavior of two hydrogel based sensing polymeric composites in the physiological pH range

The pH dependent conductance of the Chitosan/Gelatin and Hydroxy-Ethyl Methacrylate (HEMA)/Gelatin hydrogel based systems was mapped out using a data acquisition system and several aspects of conductance characteristics namely accuracy, response time and sensitivity were compared and discussed. The material characteristics of these sensors were mapped out through FTIR. Specific accuracy tests were designed and conducted and both hydrogel based sensors have been found to give a reasonably predictable outcomes in the physiological pH range (4–10). Although Chitosan/Gelatin sensing systems have been explored with regards to conductivity, to the best of our knowledge, HEMA/Gelatin composite has not yet been explored in this respect. There are observable differences in the conductometric system analysis results of both hydrogels; it has been found that HEMA based hydrogels have a longer response time, but also offer a better sensitivity as compared to Chitosan based conductometric hydrogels. This difference in response may be attributed to the material based characteristics of the conductometric sensing system; the possible underlying mechanisms have also been discussed in this research. Our findings indicate that these conductometric composite hydrogels have the potential to be used in physiological applications; where varying response times are required, a hybrid sensor array comprising of both types of hydrogel sensors may be used; that can utilize the advantages of faster response time in case of Chitosan/Gelatin and better sensitivity in case of HEMA/Gelatin sensors.

Label-free non-invasive fluorescent pattern discrimination of thiols and chiral recognition of cysteine enantiomers in biofluids using a bioinspired copolymer-Cu2+ hybrid sensor array regulated by pH.

Thiols play a crucial role in various biological processes, and the discrimination of thiols in biofluids is a significant but difficult issue. Herein, a facile label-free non-invasive fluorescent sensor array has been presented based on PDA/PEIn-Cu2+ in three different pH buffer solutions for pattern discrimination of thiols and chiral recognition of cysteine (Cys) enantiomers in biofluids toward health monitoring. The proposed sensor array was fabricated based on the fact that Cu2+ has a strong affinity toward thiols, which prevents Cu2+ from binding PDA/PEIn, and the fluorescence properties of PDA/PEIn were recovered to a certain degree. Different thiols exhibited different affinities toward Cu2+, generating distinct fluorescence response patterns. These response patterns are characteristic for each thiol and can be discriminated by principal component analysis (PCA). In this work, three types of PDA/PEI48-Cu2+ sensors (PDA/PEI48-Cu4 2+, PDA/PEI48-Cu4.5 2+ and PDA/PEI48-Cu5 2+) were prepared by using acetate buffer with different pHs (at 4, 4.5, and 5) to form our proposed sensor array, which could realize the pattern discrimination of 8 thiols. Moreover, we successfully realized the sensitivity and selectivity assays to these thiols. Furthermore, the proposed sensor array could discriminate mixtures of thiols as well as the chiral recognition of mixtures of Cys enantiomers, promising its potential practical usage. Significantly, the resultant practical application in real samples showed that it could be a fascinating assay for the development of non-invasive diagnosis. This method promises the facile, sensitive and powerful discrimination of thiols in biofluids and would sprout more relevant strategies toward a broad range of applications.

Design of a novel hybrid sensor with microelectrode array and LAPS for heavy metal determination using multivariate nonlinear calibration

This paper presents a novel hybrid sensor array for heavy metal determination with self-calibration process by multivariate nonlinear regression (MNLR). Microelectrode array (MEA) and light addressable potentiometric sensor (LAPS) were synchronously fabricated on the same wafer by microfabrication technology. The electrochemical behavior of gold MEAs in different sizes was characterized by cyclic voltammetry. Meanwhile, the detection and quantification of zinc, lead and copper were conducted by differential pulse stripping voltammetry. The sensitivity of zinc, lead and copper are 36.3nA/ppb, 11.2nA/ppb and 4.6nA/ppb, respectively. The electrochemical performance of pH in LAPS was studied with sensitivity of 52.1mV/pH. The reproducibility and long-term stability were investigated in which relative standard deviation of merely 1.45% was observed and less than 4mV deviation was presented in a continuous test of 2h. The acidity of solutions influences the stripping process to great extent due to hydrogen evolution, hydrolysis of heavy metal and hydroxylation of the working electrode. Therefore, a self-calibration process through MNLR was carried out to calibrate the influence of pH and to enhance the detection performance based on the outcomes of heavy metal in MEA and pH in LAPS. The results validate good capability of the hybrid sensor array for trace heavy metal analysis.

Analysis of Volatile Fingerprints for Monitoring Anti-Fungal Efficacy Against the Primary and Opportunistic Pathogen Aspergillus fumigatus

The aims of this study were to use qualitative volatile fingerprints obtained using a hybrid sensor array system to screen anti-fungals for controlling the important lung infecting fungus, Aspergillus fumigatus, especially in immunocompromised patients. SIFT-MS was also used to try and identify key volatiles produced by A. fumigatus. Initial studies were carried out to identify the ED(50) and ED(90) (effective dose) for inhibiting growth of A. fumigatus using three anti-fungal compounds, benomyl, tebuconazole and fluconazole. Subsequent studies involved inoculation of malt extract agar plates with spores of A. fumigatus (25 and 37°C) over periods of 24-72 h to examine the headspace volatile fingerprints generated from the sample treatments using the hybrid sensor array system to compare controls and ED(50)/ED(90) concentrations. The sensor responses showed discrimination between treatments after 48-h incubation when benomyl and tebuconazole were used against A. fumigatus at 37°C using Principal Components Analysis and Cluster Analysis. SIFT-MS analysis showed that methyl pentadiene, ethanol, isoprene and methanol were key biomarker volatiles produced by A. fumigatus in the presence of anti-fungal compounds. This may also be a good approach for the development of rapid screening of anti-microbial compounds and potentially useful for monitoring the possible build up of resistance to specific drug types. Volatile fingerprints produced by patient samples could also be used to evaluate whether lung infections are caused by bacteria or specific fungi to facilitate early diagnosis and enable the right drug treatment to be prescribed.

A Novel Thin‐Film Copper Array Based on an Organic/Inorganic Sensor Hybrid: Microfabrication, Potentiometric Characterization, and Flow‐Injection Analysis Application

AbstractA first step towards the microfabrication of a thin‐film array based on an organic/inorganic sensor hybrid has been realized. The inorganic microsensor part incorporates a sensor membrane based on a chalcogenide glass material (Cu‐Ag‐As‐Se) prepared by pulsed laser deposition technique (PLD) combined with an PVC organic membrane‐based organic microsensor part that includes an o‐xylyene bis(N,N‐diisobutyl‐dithiocarbamate) ionophore. Both types of materials have been electrochemically evaluated as sensing materials for copper(II) ions. The integrated hybrid sensor array based on these sensing materials provides a linear Nernstian response covering the range 1×10−6–1×10−1 mol L−1 of copper(II) ion concentration with a fast, reliable and reproducible response. The merit offered by the new type of thin‐film hybrid array includes the high selectivity feature of the organic membrane‐based thin‐film microsensor part in addition to the high stability of the inorganic thin‐film microsensor part. Moreover, the thin‐film sensor hybrid has been successfully applied in flow‐injection analysis (FIA) for the determination of copper(II) ions using a miniaturized home‐made flow‐through cell. Realization of the organic/inorganic thin‐film sensor hybrid array facilitates the development of a promising sophisticated electronic tongue for recognition and classification of various liquid media.

Table olives volatile fingerprints: Potential of an electronic nose for quality discrimination

Abstract In the present work, the potential of an electronic nose to differentiate the quality of fermented green table olives based on their volatile profile was investigated. An electronic gas sensor array system comprising a hybrid sensor array of 12 metal oxide and 10 metal ion-based sensors was used to generate a chemical fingerprint (pattern) of the volatile compounds present in olives. Multivariate statistical analysis and artificial neural networks were applied to the generated patterns to achieve various classification tasks. Green olives were initially classified into three major classes (acceptable, unacceptable, marginal) based on a sensory panel. Multivariate statistical approach showed good discrimination between the class of unacceptable samples and the classes of acceptable and marginal samples. However, in the latter two classes there was a certain area of overlapping in which no clear differentiation could be made. The potential to discriminate green olives in the three selected classes was also evaluated using a multilayer perceptron (MLP) neural network as a classifier with an 18–15–8–3 structure. Results showed good performance of the developed network as only two samples were misclassified in a 66-sample training dataset population, whereas only one case was misclassified in a 12-sample test dataset population. The results of this study provide promising perspectives for the use of a low-cost and rapid system for quality differentiation of fermented green olives based on their volatile profile.

AeroMEMS polyimide based wall double hot-wire sensors for flow separation detection

A polyimide-based MEMS hot-wire sensor featuring two resistors in close proximity (200 μm) has been developed. Combining a flexible printed circuit board and a number of such MEMS wall double hot-wires, a so called hybrid AeroMEMS sensor array is established. Setup of both single double-wire sensor and hybrid array, as well as wind tunnel experiments to determine the position of flow reattachment behind an airfoil are described. Further wind tunnel measurements on a generic two-dimensional model of a high-lift system of an airplane, consisting of a main airfoil and a flap, are presented. Evaluation of the experiments proves that the hybrid sensor array is well capable of detecting near-wall regions of flow separation as well as complete flow separation from the flap. This sensor system permits the detection of small near wall regions of flow separation on this specific aerodynamic configuration which were not detected by other measurement methods.

Monitoring of the ageing of red wines in oak barrels by means of an hybrid electronic tongue

A novel hybrid sensor array based on voltammetric electrodes has been successfully applied to monitor the ageing of red wines and to discriminate wine samples aged in oak barrels of different characteristics (wood origin and the toasting level). The hybrid sensor array is formed by three families of chemically modified electrodes, including polypyrrole sensors doped with a range of counterions, carbon paste electrodes modified with metallophthalocyanine complexes and finally, carbon paste electrodes modified with perylene imide derivatives. The diversity of the sensing materials has allowed obtaining a high cross-selectivity in the responses of the sensors forming the array. This voltammetric signals provided by the sensors have been used as data source for a principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) to confirm the high capability of discrimination and classification of the e-tongue. The array of voltammetric sensors has been able to follow the changes experienced by red wines aged in oak barrels after 3 and 6 months of ageing. In addition, the differences caused by oak barrels of different origins (French, Lithuanian and American) and toasting levels have also been discriminated.

E-tongue based on a hybrid array of voltammetric sensors based on phthalocyanines, perylene derivatives and conducting polymers: Discrimination capability towards red wines elaborated with different varieties of grapes

An innovative hybrid sensor array formed by voltammetric electrodes chemically modified with three families of electroactive substances (conducting polymers, phthalocyanine complexes and perylenes) has been developed. Electroactive materials have been deposited onto gold electrodes lithographied in an alumina card.Polymeric sensors have been obtained by electrodeposition of pyrrole in the presence of different doping agents. Phthalocyanine and perylene-based electrodes have been prepared using the carbon paste electrode technique.This e-tongue has demonstrated a good-quality ability in discriminating and recognising among 12 Spanish red wines on account of their denomination of origin, grape variety and vintage. The quality of the performance attained is due to the versatility, cross-selectivity and reproducibility of the sensors forming the array.