Higher-Order Chemical Sensing

Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization

This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin-Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentrations, amounting to 2500 data points per parameter set. After seven trials with four initial parameter sets (ten parameter sets were tested in total), the DBI was successfully reduced from 2.1 to 1.5. The classification accuracy for the test gases based on the support vector machine tends to increase with decreasing the DBI, indicating that the DBI acts as a good OBJ. Additionally, the accuracy itself increased from 85.4% to 93.2% through optimization. The deviation from the tendency that the accuracy increases with decreasing the DBI for some parameter sets was also discussed. Consequently, it was demonstrated that the proposed optimization method based on BO is promising for temperature modulation.

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

A single recognition unit-based virtual sensor Array: Applying 3D fluorescence spectroscopy to inner filter effect-based sensing

A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making.

Phospholipid Molecular Layer that Enhances Distinction of Odors Based on Artificial Sniffing

We propose a novel odor-sensing system based on the dynamic response of phospholipid molecular layers for artificial olfaction. Organisms obtain information about their surroundings based on multidimensional information obtained from sniffing, i.e., periodic perturbations. Semiconductor- and receptor-based odor sensors have been developed previously. However, these sensors predominantly identify odors based on one-dimensional information, which limits the type of odor molecule they can identify. Therefore, the development of odor sensors that mimic the olfactory systems of living organisms is useful to overcome this limitation. In this study, we developed a novel odor-sensing system based on the dynamics of phospholipids that responds delicately to chemical substances at room temperature using multidimensional information obtained from periodic perturbations. Odor molecules are periodically supplied to the phospholipid molecular layer as an input sample. The waveform of the surface tension of the phospholipid molecular layer changes depending on the odor molecules and serves as an output. Such characteristic responses originating from the dynamics of odor molecules on the phospholipid molecular layer can be reproduced numerically. The phospholipid molecular layer amplified the information originating from the odor molecule, and the mechanism was evaluated by using surface pressure-area isotherms. This paper offers a platform for an interface-chemistry-based artificial sniffing system as an active sensor and a novel olfactory mechanism via physicochemical responses of the receptor-independent membranes of the organism.

Alcohol Vapor Sensor Based on Quasi-2D Nb2O5 Derived from Oxidized Nb2CTz MXenes

MXenes are two-dimensional (2D) materials with a great potential for sensor applications due to their high aspect ratio and fully functionalized surface that can be tuned for specific gas adsorption. Here, we demonstrate that the Nb2CTz-based sensor exhibits high performance towards alcohol vapors at temperatures up to 300-350 °C, with the best sensitivity towards ethanol. We attribute the observed remarkable chemiresistive effect of this material to the formation of quasi-2D Nb2O5 sheets as the result of the oxidation of Nb-based MXenes. These findings are supported by synchrotron X-ray photoelectron spectroscopy studies together with X-ray diffraction and electron microscopy observations. For analyte selectivity, we employ a multisensor approach where the gas recognition is achieved by linear discriminant analysis of the vector response of the on-chip sensor array. The reported protocol demonstrates that MXene layers are efficient precursors for the derivation of 2D oxide architectures, which are suitable for developing gas sensors and sensor arrays.

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Chemical gradients and uneven distribution of analytes are common in natural and artificial systems. As a result, the ability to visualize chemical distributions in two or more dimensions has gained significant importance in recent years. This has led to the integration of chemical imaging techniques into all domains of analytical chemistry. In this review, we focus on the use of optical sensors, so-called optodes, to obtain real-time and multidimensional images of two or more parameters simultaneously. It is important to emphasize that multiparameter imaging in this context is not confined solely to multiple chemical parameters (analytes) but also encompasses physical (e.g., temperature or flow) or biological (e.g., metabolic activity) parameters. First, we discuss the technological milestones that have paved the way for chemical imaging using optodes. Later, we delve into various strategies that can be taken to enable multiparameter imaging. The latter spans from developing novel receptors that enable the recognition of multiple parameters to chemometrics and machine learning-based techniques for data analysis. We also explore ongoing trends, challenges, and prospects for future developments in this field. Optode-based multiparameter imaging is a rapidly expanding field that is being fueled by cutting-edge technologies. Chemical imaging possesses the potential to provide novel insights into complex samples, bridging not only across various scientific disciplines but also between research and society.

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.

A method of gas sensor drift compensation based on intrinsic characteristics of response curve

Sensor drift, which is an inevitable and challenging problem in gas sensing, seriously affects the detection performance of sensor. In this study, a new sensor drift compensation method, which is based on intrinsic characteristic of sensory response, is proposed. The dataset of gas sensor for two types of gas with a period of 36 months are collected and two features (one is steady-state feature, another is transient feature) are extracted. Their relationship, which is found to be certain for different months and sensors, is explored. Then, drift compensation method is processed based on this relationship, aiming to make the drifted sensor features adjusted to that of month 1, which is considered as having no drift phenomenon. Moreover, small amount of dataset is necessary for model building and it has strong scalability. Finally, SVM is employed for proving the performance of the drift compensation method proposed in this study. The results show the efficacy of 22 month of continuous monitoring, which has been enough for most application scenario, and almost 20% of increasement of correct classification rate of SVM after drift compensation, which indicates the effect of drift compensation method.

A chemiresistive-potentiometric multivariate sensor for discriminative gas detection

Highly efficient gas sensors able to detect and identify hazardous gases are crucial for numerous applications. Array of conventional single-output sensors is currently limited by problems including drift, large size, and high cost. Here, we report a sensor with multiple chemiresistive and potentiometric outputs for discriminative gas detection. Such sensor is applicable to a wide range of semiconducting electrodes and solid electrolytes, which allows to tailor and optimize the sensing pattern by tuning the material combination and conditions. The sensor performance is boosted by equipping a mixed-conducting perovskite electrode with reverse potentiometric polarity. A conceptual sensor with dual sensitive electrodes achieves superior three-dimensional (sub)ppm sensing and discrimination of humidity and seven hazardous gases (2-Ethylhexanol, ethanol, acetone, toluene, ammonia, carbon monoxide, and nitrogen dioxide), and enables accurate and early warning of fire hazards. Our findings offer possibilities to design simple, compact, inexpensive, and highly efficient multivariate gas sensors.

Low-Power, Multi-Transduction Nanosensor Array for Accurate Sensing of Flammable and Toxic Gases

Toxic and flammable gases pose a major safety risk in industrial settings; thus, their portable sensing is desired, which requires sensors with fast response, low-power consumption, and accurate detection. Herein, a low-power, multi-transduction array is presented for the accurate sensing of flammable and toxic gases. Specifically, four different sensors are integrated on a micro-electro-mechanical-systems platform consisting of bridge-type microheaters. To produce distinct fingerprints for enhanced selectivity, the four sensors operate based on two different transduction mechanisms: chemiresistive and calorimetric sensing. Local, in situ synthesis routes are used to integrate nanostructured materials (ZnO, CuO, and Pt Black) for the sensors on the microheaters. The transient responses of the four sensors are fed to a convolutional neural network for real-time classification and regression of five different gases (H₂ , NO₂ , C₂ H₆ O, CO, and NH₃ ). An overall classification accuracy of 97.95%, an average regression error of 14%, and a power consumption of 7 mW per device are obtained. The combination of a versatile low-power platform, local integration of nanomaterials, different transduction mechanisms, and a real-time machine learning strategy presented herein helps advance the constant need to simultaneously achieve fast, low-power, and selective gas sensing of flammable and toxic gases.

A recent advancement on the applications of nanomaterials in electrochemical sensors and biosensors

Industrialization and globalization, both on an international and local scale, have caused large quantities of toxic chemicals to be released into the environment. Thus, developing an environmental pollutant sensor platform that is sensitive, reliable, and cost-effective is extremely important. In current years, considerable progress has been made in the expansion of electrochemical sensors and biosensors to monitor the environment using nanomaterials. A large number of emerging biomarkers are currently in existence in the biological fluids, clinical, pharmaceutical and bionanomaterial-based electrochemical biosensor platforms have drawn much attention. Electrochemical systems have been used to detect biomarkers rapidly, sensitively, and selectively using biomaterials such as biopolymers, nucleic acids, proteins etc. In this current review, several recent trends have been identified in the growth of electrochemical sensor platforms using nanotechnology such as carbon nanomaterials, metal oxide nanomaterials, metal nanoparticles, biomaterials and polymers. The integration strategies, applications, specific properties and future projections of nanostructured materials for emerging progressive sensor platforms are also observed. The objective of this review is to provide a comprehensive overview of nanoparticles in the field of electrochemical sensors and biosensors.

Biomimetic Transparent Nanoplasmonic Meshes by Reverse-Nanoimprinting for Bio-Interfaced Spatiotemporal Multimodal SERS Bioanalysis

Multicellular systems, such as microbial biofilms and cancerous tumors, feature complex biological activities coordinated by cellular interactions mediated via different signaling and regulatory pathways, which are intrinsically heterogeneous, dynamic, and adaptive. However, due to their invasiveness or their inability to interface with native cellular networks, standard bioanalysis methods do not allow in situ spatiotemporal biochemical monitoring of multicellular systems to capture holistic spatiotemporal pictures of systems-level biology. Here, a high-throughput reverse nanoimprint lithography approach is reported to create biomimetic transparent nanoplasmonic microporous mesh (BTNMM) devices with ultrathin flexible microporous structures for spatiotemporal multimodal surface-enhanced Raman spectroscopy (SERS) measurements at the bio-interface. It is demonstrated that BTNMMs, supporting uniform and ultrasensitive SERS hotspots, can simultaneously enable spatiotemporal multimodal SERS measurements for targeted pH sensing and non-targeted molecular detection to resolve the diffusion dynamics for pH, adenine, and Rhodamine 6G molecules in agarose gel. Moreover, it is demonstrated that BTNMMs can act as multifunctional bio-interfaced SERS sensors to conduct in situ spatiotemporal pH mapping and molecular profiling of Escherichia coli biofilms. It is envisioned that the ultrasensitive multimodal SERS capability, transport permeability, and biomechanical compatibility of the BTNMMs can open exciting avenues for bio-interfaced multifunctional sensing applications both in vitro and in vivo.

Multisite Dopamine Sensing With Femtomolar Resolution Using a CMOS Enabled Aptasensor Chip

Many biomarkers including neurotransmitters are found in external body fluids, such as sweat or saliva, but at lower titration levels than they are present in blood. Efficient detection of such biomarkers thus requires, on the one hand, to use techniques offering high sensitivity, and, on the other hand, to use a miniaturized format to carry out diagnostics in a minimally invasive way. Here, we present the hybrid integration of bottom-up silicon-nanowire Schottky-junction FETs (SiNW SJ-FETs) with complementary-metal–oxide–semiconductor (CMOS) readout and amplification electronics to establish a robust biosensing platform with 32 × 32 aptasensor measurement sites at a 100 μm pitch. The applied hetero-junctions yield a selective biomolecular detection down to femtomolar concentrations. Selective and multi-site detection of dopamine is demonstrated at an outstanding sensitivity of ∼1 V/fM. The integrated platform offers great potential for detecting biomarkers at high dilution levels and could be applied, for example, to diagnosing neurodegenerative diseases or monitoring therapy progress based on patient samples, such as tear liquid, saliva, or eccrine sweat.

Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System

Discriminating structurally similar volatile organic compounds (VOCs) molecules, such as benzene, toluene, and three xylene isomers (BTX), remains a significant challenge, especially, for metal oxide semiconductor (MOS) sensors, in which selectivity is a long-standing challenge. Recent progress indicates that temperature modulation of a single MOS sensor offers a powerful route in extracting the features of adsorbed gas analytes than conventional isothermal operation. Herein, a rectangular heating waveform is applied on NiO-, WO₃-, and SnO₂-based sensors to gradually activate the specific gas/oxide interfacial redox reaction and generate rich (electrical) features of adsorbed BTX molecules. Upon several signal preprocessing steps, the intrinsic feature of BTX molecules can be extracted by the linear discrimination analysis (LDA) or convolutional neural network (CNN) analysis. The combination of three distinct MOS sensors noticeably benefits the recognition accuracy (with a reduced number of training iterations). Finally, a prototype of a smart BTX recognition system (including sensing electronics, sensors, Wi-Fi module, UI, PC, etc.) based on temperature modulation has been explored, which enables a prompt, accurate, and stable identification of xylene isomers in the ambient air background and raises the hope of innovating the future advanced machine olfactory system.

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.

Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity

Odors are one of the most diverse and complicated gaseous mixtures so that their discrimination is challenging yet attractive because of the rich information about their origin. The more similar the properties of odors are, the more difficult the discrimination becomes. The practical applications, however, often demand such discrimination, especially with a compact sensing platform. In this paper, we show that a nanomaterial designed for a specific type of odors can clearly discriminate them even with a single nanomechanical sensing channel. Fuel oils and their mixture are used as a model target that has similar chemical properties but different compositions mainly consisting of paraffinic, olefinic, naphthenic, and aromatic hydrocarbons. We demonstrate using octadecyl functionalized silica-titania nanoparticles that the difference in the compositions is successfully picked up based on their high affinity for the aliphatic hydrocarbons and alkyl chain length dependent nonlinear viscoelastic behavior. Such a properly designed material is proved to derive sufficient information from a series of analytes to discriminate them even with a single sensing element. This approach provides a guideline to prepare various sensors whose response properties are distinct and optimized depending on applications.

Development and Characterization of Integrated Nano-Sensors for Organic Residues and pH Field Detection

Meeting global water quality standards is a real challenge to ensure that food crops and livestock are fit for consumption, as well as for human health in general. A major hurdle affecting the detection of pollutants in water reservoirs is the lapse of time between the sampling moment and the availability of the laboratory-based results. Here, we report the preparation, characterization, and performance assessment of an innovative sensor for the rapid detection of organic residue levels and pH in water samples. The sensor is based on carbonaceous nanomaterials (CNMs) coated with an intrinsically conductive polymer, polyaniline (PANI). Inverse emulsion polymerizations of aniline in the presence of carbon nanotubes (CNTs) or graphene were prepared and confirmed by thermogravimetric analysis and high-resolution scanning electron microscopy. Aminophenol and phenol were used as proxies for organic residue detection. The PANI/CNM nanocomposites were used to fabricate thin-film sensors. Of all the CNMs, the smallest limit of detection (LOD) was achieved for multi-walled CNT (MWCNT) with a LOD of 9.6 ppb for aminophenol and a very high linearity of 0.997, with an average sensitivity of 2.3 kΩ/pH at an acid pH. This high sensor performance can be attributed to the high homogeneity of the PANI coating on the MWCNT surface.

A chemiluminescence sensor array for discrimination of seven toxicants

A chemiluminescence (CL) sensor array was developed based on 11 CL systems cross-combined by three luminescence reagents and four oxidants. Using the CL sensor array, we measured seven toxicants, including morphine, ketamine, diazepam, chlorpromazine, strychnine, paraquat, and fenpropathrin, which represent psychotropic drugs, sedatives and hypnotics, rodenticides, herbicides, and insecticides, respectively. The CL response pattern or 'fingerprints' were obtained for a given compound on the sensor array and then discriminated through principal component analysis. The established sensor array has been applied to real-life samples and the results showed that it possesses excellent discrimination.

Principles of odor coding in vertebrates and artificial chemosensory systems

The biological olfactory system is the sensory system responsible for the detection of the chemical composition of the environment. Several attempts to mimic biological olfactory systems have led to various artificial olfactory systems using different technical approaches. Here we provide a parallel description of biological olfactory systems and their technical counterparts. We start with a presentation of the input to the systems, the stimuli, and treat the interface between the external world and the environment where receptor neurons or artificial chemosensors reside. We then delineate the functions of receptor neurons and chemosensors as well as their overall I-O relationships. Up to this point, our account of the systems goes along similar lines. The next processing steps differ considerably: while in biology the processing step following the receptor neurons is the "integration" and "processing" of receptor neuron outputs in the olfactory bulb, this step has various realizations in electronic noses. For a long period of time, the signal processing stages beyond the olfactory bulb, i.e., the higher olfactory centers were little studied. Only recently there has been a marked growth of studies tackling the information processing in these centers. In electronic noses, a third stage of processing has virtually never been considered. In this review, we provide an up-to-date overview of the current knowledge of both fields and, for the first time, attempt to tie them together. We hope it will be a breeding ground for better information, communication, and data exchange between very related but so far little connected fields.

Sensor array and gas chromatographic detection of the blood serum volatolomic signature of COVID-19

Volatolomics is gaining consideration as a viable approach to diagnose several diseases, and it also shows promising results to discriminate COVID-19 patients via breath analysis. This paper extends the study of the relationship between volatile compounds (VOCs) and COVID-19 to blood serum. Blood samples were collected from subjects recruited at the emergency department of a large public hospital. The VOCs were analyzed with a gas chromatography mass spectrometer (GC/MS). GC/MS data show that in more than 100 different VOCs, the pattern of abundances of 17 compounds identifies COVID-19 from non-COVID with an accuracy of 89% (sensitivity 94% and specificity 83%). GC/MS analysis was complemented by an array of gas sensors whose data achieved an accuracy of 89% (sensitivity 94% and specificity 80%).

Multicomponent SF6 decomposition product sensing with a gas-sensing microchip

A difficult issue restricting the development of gas sensors is multicomponent recognition. Herein, a gas-sensing (GS) microchip loaded with three gas-sensitive materials was fabricated via a micromachining technique. Then, a portable gas detection system was built to collect the signals of the chip under various decomposition products of sulfur hexafluoride (SF₆). Through a stacked denoising autoencoder (SDAE), a total of five high-level features could be extracted from the original signals. Combined with machine learning algorithms, the accurate classification of 47 simulants was realized, and 5-fold cross-validation proved the reliability. To investigate the generalization ability, 30 sets of examinations for testing unknown gases were performed. The results indicated that SDAE-based models exhibit better generalization performance than PCA-based models, regardless of the magnitude of noise. In addition, hypothesis testing was introduced to check the significant differences of various models, and the bagging-based back propagation neural network with SDAE exhibits superior performance at 95% confidence.

Highly selective gas sensing enabled by filters

Portable and inexpensive gas sensors are essential for the next generation of non-invasive medical diagnostics, smart air quality monitoring & control, human search & rescue and food quality assessment to name a few of their immediate applications. Therein, analyte selectivity in complex gas mixtures like breath or indoor air remains the major challenge. Filters are an effective and versatile, though often unrecognized, route to overcome selectivity issues by exploiting additional properties of target analytes (e.g., molecular size and surface affinity) besides reactivity with the sensing material. This review provides a tutorial for the material engineering of sorption, size-selective and catalytic filters. Of specific interest are high surface area sorbents (e.g., activated carbon, silica gels and porous polymers) with tunable properties, microporous materials (e.g., zeolites and metal-organic frameworks) and heterogeneous catalysts, respectively. Emphasis is placed on material design for targeted gas separation, portable device integration and performance. Finally, research frontiers and opportunities for low-cost gas sensing systems in emerging applications are highlighted.

Microplotter-Printed On-Chip Combinatorial Library of Ink-Derived Multiple Metal Oxides as an "Electronic Olfaction" Unit

Information about the surrounding atmosphere at a real timescale significantly relies on available gas sensors to be efficiently combined into multisensor arrays as electronic olfaction units. However, the array's performance is challenged by the ability to provide orthogonal responses from the employed sensors at a reasonable cost. This issue becomes more demanded when the arrays are designed under an on-chip paradigm to meet a number of emerging calls either in the internet-of-things industry or in situ noninvasive diagnostics of human breath, to name a few, for small-sized low-powered detectors. The recent advances in additive manufacturing provide a solid top-down background to develop such chip-based gas-analytical systems under low-cost technology protocols. Here, we employ hydrolytically active heteroligand complexes of metals as ink components for microplotter patterning a multioxide combinatorial library of chemiresistive type at a single chip equipped with multiple electrodes. To primarily test the performance of such a multisensor array, various semiconducting oxides of the p- and n-conductance origins based on pristine and mixed nanocrystalline MnO_x, TiO₂, ZrO₂, CeO₂, ZnO, Cr₂O₃, Co₃O₄, and SnO₂ thin films, of up to 70 nm thick, have been printed over hundred μm areas and their micronanostructure and fabrication conditions are thoroughly assessed. The developed multioxide library is shown to deliver at a range of operating temperatures, up to 400 °C, highly sensitive and highly selective vector signals to different, but chemically akin, alcohol vapors (methanol, ethanol, isopropanol, and n-butanol) as examples at low ppm concentrations when mixed with air. The suggested approach provides us a promising way to achieve cost-effective and well-performed electronic olfaction devices matured from the diverse chemiresistive responses of the printed nanocrystalline oxides.

Nanomaterials in Electrochemical Sensing Area: Applications and Challenges in Food Analysis

Recently, nanomaterials have received increasing attention due to their unique physical and chemical properties, which make them of considerable interest for applications in many fields, such as biotechnology, optics, electronics, and catalysis. The development of nanomaterials has proven fundamental for the development of smart electrochemical sensors to be used in different application fields such, as biomedical, environmental, and food analysis. In fact, they showed high performances in terms of sensitivity and selectivity. In this report, we present a survey of the application of different nanomaterials and nanocomposites with tailored morphological properties as sensing platforms for food analysis. Particular attention has been devoted to the sensors developed with nanomaterials such as carbon-based nanomaterials, metallic nanomaterials, and related nanocomposites. Finally, several examples of sensors for the detection of some analytes present in food and beverages, such as some hydroxycinnamic acids (caffeic acid, chlorogenic acid, and rosmarinic acid), caffeine (CAF), ascorbic acid (AA), and nitrite are reported and evidenced.

Detecting cooking state of grilled chicken by electronic nose and computer vision techniques

Determination of food doneness remains a challenge for automation in the cooking industry. The complex physicochemical processes that occur during cooking require a combination of several methods for their control. Herein, we utilized an electronic nose and computer vision to check the cooking state of grilled chicken. Thermogravimetry, differential mobility analysis, and mass spectrometry were employed to deepen the fundamental insights towards the grilling process. The results indicated that an electronic nose could distinguish the odor profile of the grilled chicken, whereas computer vision could identify discoloration of the chicken. The integration of these two methods yields greater selectivity towards the qualitative determination of chicken doneness. The odor profile is matched with detected water loss, and the release of aromatic and sulfur-containing compounds during cooking. This work demonstrates the practicability of the developed technique, which we compared with a sensory evaluation, for better deconvolution of food state during cooking.

Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning"

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

Gas sensors based on mass-sensitive transducers. Part 2: Improving the sensors towards practical application

Within the framework outlined in the first part of the review, the second part addresses attempts to increase receptor material performance through the use of sensor systems and chemometric methods, in conjunction with receptor preparation methods and sensor-specific tasks. Conclusions are then drawn, and development perspectives for gravimetric sensors are discussed.

Detecting varroosis using a gas sensor system as a way to face the environmental threat

Colony Collapse Disorder (CCD) is an environmental threat on a global scale due to the irreplaceable role of bees in crop pollination. Varroa destructor (V.d.), a parasite that attacks honeybee colonies, is one of the primary causes of honey bee population decline and the most serious threat to the beekeeping sector. This work demonstrates the possibility of quantitatively determining bee colony infestation by V.d. using gas sensing. The results are based on analysing the experimental data acquired for eighteen bee colonies in field conditions. Their infestation rate was in the 0 to 24.76% range. The experimental data consisted of measurements of beehive air with a semiconductor gas sensor array and the results of bee colony V.d. infestation assessment using a flotation method. The two kinds of data were collected in parallel. Partial Least Square regression was applied to identify the relationship between the highly multivariate measurement data provided by the gas sensor array and the V.d. infestation rate. The quality of the developed quantitative models was very high, as demonstrated by the coefficient of determination exceeding R² = 0.99. Moreover, the prediction error was <0.6% for V.d. infestation rate predictions based on the measurement data that was unknown to the model. The presented work has considerable novelty. To our knowledge, the ability to determine the V.d. infestation rate of bee colony quantitatively based on beehive air measurements using a semiconductor gas sensor array has not been previously demonstrated.

Multimodal Imaging Iridium(III) Complex for Hypochlorous Acid in Living Systems

Biomolecule tracing with different imaging methods is of great significance for more accurately unravelling the fundamental processes in living systems. However, considering the different principles of each imaging method for probe design, it is still a great challenge to apply one molecular probe to achieve two or even more imaging analyses for biomarkers. In general, traditional oxime was reported as a recognition group for fluorescence imaging of HOCl. Herein, for the first time, we designed the oxime decorated iridium(III) complex, which can be directly used for chemiluminescence as well as two-photon luminescence and photoluminescence lifetime imaging of HOCl in living systems. Moreover, the novel chemiluminescence mechanism of Ir-CLFLPLIM for HOCl was also proposed and explored by continuously monitoring chemiluminescence peak shapes and mass spectra, inferring the reaction intermediate and calculating the chemical reaction energy range of the reaction process. This strategy could lead us to expand the chemiluminescence application of transition metal complexes and develop more multimodal imaging probes.

The Effectiveness of Varroa destructor Infestation Classification Using an E-Nose Depending on the Time of Day

Honey bees are subject to a number of stressors. In recent years, there has been a worldwide decline in the population of these insects. Losses raise a serious concern, because bees have an indispensable role in the food supply of humankind. This work is focused on the method of assessment of honey bee colony infestation by Varroa destructor. The approach allows to detect several categories of infestation: "Low", "Medium" and "High". The method of detection consists of two components: (1) the measurements of beehive air using a gas sensor array and (2) classification, which is based on the measurement data. In this work, we indicate the sensitivity of the bee colony infestation assessment to the timing of measurement data collection. It was observed that the semiconductor gas sensor responses to the atmosphere of a defined beehive, collected during 24 h, displayed temporal variation. We demonstrated that the success rate of the bee colony infestation assessment also altered depending on the time of day when the gas sensor array measurement was done. Moreover, it was found that different times of day were the most favorable to detect the particular infestation category. This result could indicate that the representation of the disease in the beehive air may be confounded during the day, due to some interferences. More studies are needed to explain this fact and determine the best measurement periods. The problem addressed in this work is very important for scheduling the beekeeping practices aimed at Varroa destructor infestation assessment, using the proposed method.

Vapor detection through dynamic process of molecule desorption from butterfly wings

This work explores an alternative vapor sensing mechanism through analyzing dynamic desorption process from butterfly wings for the differentiation of both individual and mixed vapors quantitatively. Morpho butterfly wings have been used in differentiating individual vapors, but it is challenging to use them for the differentiation of mixed vapor quantitatively. This paper demonstrates the use of Morpho butterfly wings for the sensitive and selective detection of closely related vapors in mixtures. Principal components analysis (PCA) is used to process the reflectance spectra of the wing scales during dynamic desorption of different vapors. With the desorption-based detection mechanism, individual vapors with different concentrations and mixed vapors with different mixing ratios can be differentiated using the butterfly wing based sensors. Both the original butterfly wings and butterfly wings with surface modification show the capability in distinguishing vapors in mixtures, which may offer a guideline for further improving selectivity and sensitivity of bioinspired sensors.

In-Plane Vibration of Hammerhead Resonators for Chemical Sensing Applications

Thermally excited and piezoresistively detected in-plane cantilever resonators have been previously demonstrated for gas- and liquid-phase chemical and biosensing applications. In this work, the hammerhead resonator geometry, consisting of a cantilever beam supporting a wider semicircular "head", vibrating in an in-plane vibration mode, is shown to be particularly effective for gas-phase sensing with estimated limits of detection in the sub-ppm range for volatile organic compounds. This paper discusses the hammerhead resonator design and the particular advantages of the hammerhead geometry, while also presenting mechanical characterization, optical characterization, and chemical sensing results. These data highlight the distinct advantages of the hammerhead geometry over other cantilever designs.

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol

The poor gas selectivity problem has been a long-standing issue for miniaturized chemical-resistor gas sensors. The electronic nose (e-nose) was proposed in the 1980s to tackle the selectivity issue, but it required top-down chemical functionalization processes to deposit multiple functional materials. Here, we report a novel gas-sensing scheme using a single graphene field-effect transistor (GFET) and machine learning to realize gas selectivity under particular conditions by combining the unique properties of the GFET and e-nose concept. Instead of using multiple functional materials, the gas-sensing conductivity profiles of a GFET are recorded and decoupled into four distinctive physical properties and projected onto a feature space as 4D output vectors and classified to differentiated target gases by using machine-learning analyses. Our single-GFET approach coupled with trained pattern recognition algorithms was able to classify water, methanol, and ethanol vapors with high accuracy quantitatively when they were tested individually. Furthermore, the gas-sensing patterns of methanol were qualitatively distinguished from those of water vapor in a binary mixture condition, suggesting that the proposed scheme is capable of differentiating a gas from the realistic scenario of an ambient environment with background humidity. As such, this work offers a new class of gas-sensing schemes using a single GFET without multiple functional materials toward miniaturized e-noses.

Gas Sensor Array and Classifiers as a Means of Varroosis Detection

The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

Bare Iron Oxide Nanoparticles: Surface Tunability for Biomedical, Sensing and Environmental Applications

Surface modification is widely assumed as a mandatory prerequisite for the real applicability of iron oxide nanoparticles. This is aimed to endow prolonged stability, electrolyte and pH tolerance as well as a desired specific surface chemistry for further functionalization to these materials. Nevertheless, coating processes have negative consequences on the sustainability of nanomaterial production contributing to high costs, heavy environmental impact and difficult scalability. In this view, bare iron oxide nanoparticles (BIONs) are arousing an increasing interest and the properties and advantages of pristine surface chemistry of iron oxide are becoming popular among the scientific community. In the authors’ knowledge, rare efforts were dedicated to the use of BIONs in biomedicine, biotechnology, food industry and environmental remediation. Furthermore, literature lacks examples highlighting the potential of BIONs as platforms for the creation of more complex nanostructured architectures, and emerging properties achievable by the direct manipulation of pristine iron oxide surfaces have been little studied. Based on authors’ background on BIONs, the present review is aimed at providing hints on the future expansion of these nanomaterials emphasizing the opportunities achievable by tuning their pristine surfaces.

Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006-2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.

Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated March 15, 2017.

The Multisensor Array Based on Grown-On-Chip Zinc Oxide Nanorod Network for Selective Discrimination of Alcohol Vapors at Sub-ppm Range

We discuss the fabrication of gas-analytical multisensor arrays based on ZnO nanorods grown via a hydrothermal route directly on a multielectrode chip. The protocol to deposit the nanorods over the chip includes the primary formation of ZnO nano-clusters over the surface and secondly the oxide hydrothermal growth in a solution that facilitates the appearance of ZnO nanorods in the high aspect ratio which comprise a network. We have tested the proof-of-concept prototype of the ZnO nanorod network-based chip heated up to 400 °C versus three alcohol vapors, ethanol, isopropanol and butanol, at approx. 0.2-5 ppm concentrations when mixed with dry air. The results indicate that the developed chip is highly sensitive to these analytes with a detection limit down to the sub-ppm range. Due to the pristine differences in ZnO nanorod network density the chip yields a vector signal which enables the discrimination of various alcohols at a reasonable degree via processing by linear discriminant analysis even at a sub-ppm concentration range suitable for practical applications.

Gas Identification by a Single Metal-Oxide-Semiconductor Sensor Assisted by Ultrasound

The gas identification technology has huge potential applications in medical diagnoses, food industries, early warning of poisonous gas leakage, fire prevention, antiterrorism, military, etc. Although electronic noses may be used to identify different gases, it has been a big challenge to identify gases by a single sensor. In this work, we demonstrate a novel gas identification strategy based on a single metal-oxide-semiconductor (MOS) sensor assisted by an ultrasound. The identification is based on different ultrasonic effects on the steady sensing responses of an ultrasonically radiated MOS gas sensor to different target gases. It does not need a complicated feature extraction computation. Our experiments show that the success rate of identification can be up to 100% if strong enough ultrasound is employed. The identification process can also give the concentration of the gas to be identified. The identification result is immune to the interference of impurity gases to some extent. The anti-interference capability may be strengthened by increasing the vibration velocity and choosing proper sensing materials.

Free-hand gas identification based on transfer function ratios without gas flow control

Gas identification is one of the most important functions of a gas sensor system. To identify gas species from sensing signals without gas flow control such as pumps or mass flow controllers, it is necessary to extract decisive dynamic features from complex sensing signals due to uncontrolled airflow. For that purpose, various analysis methods using system identification techniques have been proposed, whereas a method that is not affected by a gas input pattern has been demanded to enhance the robustness of gas identification. Here we develop a novel gas identification protocol based on a transfer function ratio (TFR) that is intrinsically independent of a gas input pattern. By combining the protocol with MEMS-based sensors-Membrane-type Surface stress Sensors (MSS), we have realized gas identification with a free-hand measurement, in which one can simply hold a small sensor chip near samples. From sensing signals obtained through the free-hand measurement, we have developed highly accurate machine learning models that can identify odors of spices and herbs as well as solvent vapors. Since no bulky gas flow control units are required, this protocol will expand the applicability of gas sensors to portable electronics, leading to practical artificial olfaction.

A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors

A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the i-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.

Nanomaterial based electrochemical sensors for the safety and quality control of food and beverages

The issue of foodborne related illnesses due to additives and contaminants poses a significant challenge to food processing industries. The efficient, economical and rapid analysis of food additives and contaminants is therefore necessary in order to minimize the risk of public health issues. Electrochemistry offers facile and robust analytical methods, which are desirable for food safety and quality assessment over conventional analytical techniques. The development of a wide array of nanomaterials has paved the way for their applicability in the design of high-performance electrochemical sensing devices for medical diagnostics and environment and food safety. The design of nanomaterial based electrochemical sensors has garnered enormous attention due to their high sensitivity and selectivity, real-time monitoring and ease of use. This review article focuses predominantly on the synthesis and applications of different nanomaterials for the electrochemical determination of some common additives and contaminants, including hydrazine (N2H4), malachite green (MG), bisphenol A (BPA), ascorbic acid (AA), caffeine, caffeic acid (CA), sulfite (SO32-) and nitrite (NO2-), which are widely found in food and beverages. Important aspects, such as the design, fabrication and characterization of graphene-based materials, gold nanoparticles, mono- and bimetallic nanoparticles and metal nanocomposites, sensitivity and selectivity for electrochemical sensor development are addressed. High-performance nanomaterial based electrochemical sensors have and will continue to have myriad prospects in the research and development of advanced analytical devices for the safety and quality control of food and beverages.

Electrospinning of Polystyrene/Polyhydroxybutyrate Nanofibers Doped with Porphyrin and Graphene for Chemiresistor Gas Sensors

Structural and functional properties of polymer composites based on carbon nanomaterials are so attractive that they have become a big challenge in chemical sensors investigation. In the present study, a thin nanofibrous layer, comprising two insulating polymers (polystyrene (PS) and polyhydroxibutyrate (PHB)), a known percentage of nanofillers of mesoporous graphitized carbon (MGC) and a free-base tetraphenylporphyrin, was deposited onto an Interdigitated Electrode (IDE) by electrospinning technology. The potentials of the working temperature to drive both the sensitivity and the selectivity of the chemical sensor were studied and described. The effects of the porphyrin combination with the composite graphene⁻polymer system appeared evident when nanofibrous layers, with and without porphyrin, were compared for their morphology and electrical and sensing parameters. Porphyrin fibers appeared smoother and thinner and were more resistive at lower temperature, but became much more conductive when temperature increased to 60⁻70 °C. Both adsorption and diffusion of chemicals seemed ruled by porphyrin according its combination inside the composite fiber, since the response rates dramatically increased (toluene and acetic acid). Finally, the opposite effect of the working temperature on the sensitivity of the porphyrin-doped fibers (i.e., increasing) and the porphyrin-free fibers (i.e., decreasing) seemed further confirmation of the key role of such a macromolecule in the VOC (volatile organic compound) adsorption.