Highly selective detection of methanol over ethanol by a handheld gas sensor

Recent advancements in ubiquitous colorimetric and fluorometric sensors for the detection of methanol

Methanol, an aliphatic alcohol, is a widely recognized volatile organic compound (VOC) extensively used as a cost-effective solvent in various chemical, agricultural, and biological industries. However, the intentional adulteration of alcoholic beverages with inexpensive methanol for economic gain poses significant health hazards, including birth defects, respiratory illnesses, nervous system damage, and various chronic conditions. Prolonged exposure to methanol vapour can result in adverse effects such as eye inflammation, dizziness, headaches, insomnia, stomach problems, visual impairment, and even fatality. Notably, from 2017 to 2019, there were 7104 documented cases of methanol intoxication, resulting in 1888 fatalities across 306 recorded outbreaks of methanol poisoning. Notably, over 90% of these cases reported in Asia. Given the severe health risks associated with methanol, there is an urgent need for dependable detection methods to ensure public safety. One promising approach involves chemosensors capable of detecting methanol through changes in fluorescence or color. This review article provides a comprehensive overview of the latest advancements in methanol detection, categorizing sensors based on their materials, structural attributes, and detection mechanisms. In essence, the review highlights recent research progress in fluorometric and colorimetric methods for methanol detection.

Hybrid Series of Carbon-Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi-Task Model

Detecting individual gases with various sensors is a well-established field in gas sensing. However, substantial challenges and opportunities remain in the simultaneous detection and classification of multiple gases. Artificial intelligence (AI) integrated gas sensor systems effectively enable multi-gas detection using specialized algorithms. Nevertheless, these algorithms are prone to overfitting owing to their high model complexity; this study proposes a sensor array that engineers carbon vacancies in graphene oxide via metal ion doping and high-temperature reduction, enabling high-sensitivity, simultaneous detection of various gases at low temperatures (20 °C). By integrating an advanced artificial intelligence framework, the acquired electrical signals are transformed, and a multi-task learning (MTL) approach is applied to achieve instantaneous identification of four gas types and four-level concentrations. The proposed MTL framework demonstrates superior performance by effectively mitigating overfitting and improving generalization through feature sharing and mutual regularization between gas type classification and concentration estimation tasks. Experimental validation on vehicle exhaust gas fault diagnosis highlights the method's effectiveness and applicability in complex conditions, achieving 98.22% accuracy and 48% faster inference compared to traditional single-task models. This study provides a basis for developing more intelligent and adaptable sensor systems capable.

Specific Odor Coding Using a Single Thin-Film Transistor

The specificity of olfactory receptor cells (ORCs) in the human nose is the physiological basis for their powerful odor coding capacity. Ideally, artificial ORCs in bioinspired machine olfaction represented by electronic noses (e-noses) should have a desirable specificity. However, mapping gas-solid interactions at the ORC level for specific odor perception remains a key challenge because of the cross-sensitivity issues of gas sensors in e-noses. Here, we use a thin-film transistor (TFT) to generate a two-dimensional matrix code by resolving the electronic transduction process (chemical reception and electron transduction) arising from the gas-solid interactions. As a proof-of-concept experiment, we selected lead sulfide quantum dots and black phosphorus as sensitive materials and achieved specific discrimination between typical oxidizing gases. The developed coding method has physical interpretability, enabling its extension to other gas-sensitive materials and target analytes.

Multiple stimulus modulated organic crystal polymorphs with tunable luminescence behavior

Polymorphism is defined as the ability of a substance to exist in two or more crystalline forms, which provide a unique platform for revealing the relationship between its spatial structure and properties. However, organic crystal polymorphism can be commonly obtained by growing crystals in different solvents or at different temperatures. This study reports a compound named p-An-Br containing carbazole and anthracene chromophores with three multiple stimulus modulated crystal polymorphs with green, yellow and red fluorescence, respectively. Interestingly, switching of p-An-Br between crystal G, crystal Y and crystal R can be achieved through the uptake and release of methanol using different stimuli, which shows dynamically adjustable luminescent colors. Significantly, structure-property investigations via the in-depth analysis of molecular conformations and frameworks of the polymorphic crystals demonstrate that the diverse conformations and abundant noncovalent interactions have a predominant impact on emission behavior. Consequently, the crystals R can be used for the highly sensitive and specific sensing of methanol with a detection limit of 39.35 ppm. This study not only provides a new strategy for crystal polymorphism, but also develops an effective method for the detection of methanol.

Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition

Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.

Identification of Gas Mixture Components with Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data

Chemical vapor sensors are essential for various fields, including medical diagnostics and environmental monitoring. Notably, the identification of components in unknown gas mixtures has great potential for noninvasive diagnosis of diseases such as lung cancer. However, current gas identification techniques, despite the development of electronic nose-based sensor platforms, still lack sufficient classification accuracy for mixed gases. In our previous study, we introduced multichannel hierarchical analysis using a time-resolved hyperspectral system to address the spectral ambiguity of conventional RGB sensor-based colorimetric e-noses. Here, we demonstrate the identification of mixed gas components through time-resolved line hyperspectral measurements with an eight-colorimetric sensor array that uses genetically engineered M13 bacteriophages as gas-selective colorimetric sensors. The time-dependent spectral variations induced by mixed gas in the different colorimetric sensors are converted into a hyperspectral three-dimensional (3D) data cube. For efficient machine learning classification, the data cube was converted into a multichannel spectrogram by applying a novel data processing method, including dimensionality reduction and a block average filter to reduce high-dimensional complexity and improve the signal-to-noise ratio. A convolution filter was then used for hierarchical analysis of the multichannel spectrogram, effectively capturing the complex gas-induced spectral patterns and temporal dynamics. Our study demonstrates a classification accuracy of 93.9% for pure and mixed gases of acetone, ethanol, and xylene at a low concentration of 2 ppm.

Selectivity in Chemiresistive Gas Sensors: Strategies and Challenges

The demand for highly functional chemical gas sensors has surged due to the increasing awareness of human health to monitor metabolic disorders or noncommunicable diseases, safety measures against harmful greenhouse and/or explosive gases, and determination of food freshness. Over the years of dedicated research, several types of chemiresistive gas sensors have been realized with appreciable sensitivities toward various gases. However, critical issues such as poor selectivity and sluggish response/recovery speeds continue to impede their widespread commercialization. Specifically, the mechanisms behind the selective response of some chemiresistive materials toward specific gas analytes remain unclear. In this review, we discuss state-of-the-art strategies employed to attain gas-selective chemiresistive materials, with particular emphasis on materials design, surface modification or functionalization with catalysts, defect engineering, material structure control, and integration with physical/chemical gas filtration media. The nature of material surface-gas interactions and the supporting mechanisms are elucidated, opening opportunities for optimizing the materials design, fine-tuning the gas sensing performance, and guiding the selection of the most appropriate materials for the accurate detection of specific gases. This review concludes with recommendations for future research directions and potential opportunities for further selectivity improvements.

Pulse-driven MEMS gas sensor combined with machine learning for selective gas identification

The sensing and identification of trace gases are essential for ensuring chemical safety and protecting human health. This study introduces a low-power electronic nose system that utilizes a single sensor driven by repeated pulsed power inputs, offering a viable alternative to conventional sensor array-based methods. The sensor's compact design and suspended architecture facilitate a rapid thermal response, effectively decoupling the influences of temperature, physisorption, and charge exchange on the conductivity of the sensing material. This mechanism generates distinct gas sensing responses, characterized by alternating dual responses within a single time period. The unique dynamics of the dual signals, which vary with gas type and concentration, enable precise identification of multiple gas species using machine learning (ML) algorithms. Microfabricated through wafer-level batch processing, our innovative electronic nose system holds significant potential for battery-powered mobile devices and IoT-based monitoring applications.

Rare Earth-Driven Photogenerated Charge Separation in SnO2@Y2O3 Heterojunctions for Enhanced H2S Sensing at Room Temperature

Although conventional heat-activated gas sensors effectively detect air pollutants such as highly toxic H2S and NO2, their high operating temperatures increase the risk of explosions in flammable gas environments, limiting their applications. In this study, we report a novel composite material of SnO2@Y2O3 synthesized using an electrospinning-solvothermal method. This is the first demonstration of gas sensing at room temperature using SnO2@Y2O3 heterostructures under low-power ultraviolet light. This heterostructure has excellent H2S sensing performance, thanks to its rich adsorbed oxygen species [21.01%, significantly higher than pure SnO2 (11.55%)] and enhanced interfacial charge transfer. The introduction of the Y element greatly reduces the photogenerated electron-hole recombination efficiency of the composite material under UV light. Furthermore, the SnO2@Y2O3 sensor's response to 10 ppm of H2S under UV irradiation is 318.3% higher than that of the pure SnO2 sensor. In addition, the sensor has excellent selectivity to H2S due to the interface potential barrier between the two, and the response is at least 6.8 times that of other interfering gases. DFT calculations further show that the adsorption energy of H2S on the SnO2@Y2O3 heterojunction is significantly higher than that on the SnO2 (110) surface. In addition, the electron transfer amount of H2S on the SnO2@Y2O3 heterojunction (0.385 e) is 218.2% higher than that on the SnO2 (110) surface (0.121 e). This work proposes a new strategy for improving the performance of gas sensors by introducing element Y and light excitation, providing a low-temperature, safe, and highly selective sensing platform for toxic gases such as H2S.

Enhanced Oxygen Vacancy Formation in Pt-WO3 via W-OH Bond Cleavage Using Water-Based One-Step Electrospinning for High-Performance Gas Sensors

Oxygen vacancies play a crucial role in charge transport and surface states in semiconductor metal oxides, significantly influencing various research fields, such as photocatalysis and gas sensor. Developing effective strategies to generate oxygen vacancies and thereby enhance device performance is highly desirable. In this study, we proposed a water-based one-step electrospinning method to introduce hydroxyl groups, leading to the synthesis of Pt-decorated WO3 nanofibers (Pt-WO3(H2O)) with increased oxygen vacancies. Density functional theory calculations revealed that the dissociation energy of W-OH is lower than that of the W-O bonds, promoting the formation of oxygen vacancies via W-OH bond cleavage. These vacancies reduced the adsorption energy of acetone on the WO3 surface, enhancing surface interactions. Consequently, the Pt-WO3(H2O) sensor exhibited an ultrahigh response of 82 to 1.8 ppm acetone at 300 °C, which was about 1 order of magnitude higher than the one fabricated by conventional electrospinning. These findings indicate that water-based electrospinning is an effective technique for generating oxygen vacancies in metal oxide nanofibers. Our high-performance acetone sensor, capable of detecting low concentrations, holds great potential for applications in noninvasive health screening.

Smart filtering facepiece respirator with self-adaptive fit and wireless humidity monitoring

The widespread emergence of airborne diseases has transformed our lifestyle, and respirators have become an essential part of daily life. Nevertheless, finding respirators that fit well can be challenging due to the variety of human facial sizes and shapes, potentially compromising protection. In addition, the current respirators do not inform the user of the air quality in case of continuous long-term use. Here, we introduce a smart filtering facepiece respirator incorporating a humidity sensor and pressure sensory feedback for self-fit adjusting and maintaining an adequate fit. The humidity detection sensor uses laser-induced graphene, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the user's face, providing real-time closed-loop feedback and the wearer's fitting status. Those membrane sensors show outstanding performance, such as a low humidity hysteresis of 0.131 % and a precise pressure detection limit of 0.23 ± 0.02 kPa. As a result of the self-fit adjusting mode, the overall fit factor is increased by 10 % on average compared to the commercial respirator. This significant improvement in fit factor, coupled with the innovative design, has the potential to develop next-generation facepiece respirators as essential personal protective equipment.

Single cobalt atoms with unconventional dynamic coordination mechanism for selective ammonia sensor

Developing gas sensors that can simultaneously achieve high sensitivity and selectivity for the detection of a single-type gas remains a significant challenge. Herein we demonstrate cobalt (Co) single atoms with an unconventional dynamically changing coordination structure that could be used as NH3-sensing material with superior sensitivity and selectivity. According to the steric effect of 2-methylimidazole (2MI) molecules and carbonyl groups on graphene, the Co single atom is evolved into a bidentate coordinated structure (Co-2MI-G). In-situ characterization and theoretical simulation reveal that the sensing mechanism of Co-2MI-G is the specific chemical adsorption between unsaturated coordinated Co single atoms and NH3 molecules, causing a reversible switching of coordination number from 2 to 4, a valence state transfer from Co2+ to Co3+ of Co single atoms, and a band-gap width from 0.14 eV to 0.50 eV. Consequently, the Co-2MI-G-based gas sensor presents a sensing response of 67.598% for 1 ppm NH3 and a limit of detection of 2.67 ppb, at least 1.8 times higher than that of state-of-the-art NH3 sensors, together with robust stability and reproducibility. This work provides an innovative perspective on utilizing single atoms for ultra-selective gas sensing by coordination regulation.

Enhanced room temperature ammonia gas sensing based on a multichannel PSS-functionalized graphene/PANI network

Disordered polymerization of polymers widens the polymerization degree distribution, which leads to uncontrollable thickness and significantly weakens their sensing performance. Herein, poly(sodium p-styrenesulfonate)-functionalized reduced graphene oxide (PSS-rGO) with multichannel chain structures coated with thin polyaniline layer (PSS-rGO/PANI) nanocomposites was synthesized via a facile interfacial polymerization route. The morphology and microstructure of the PSS-rGO/PANI nanocomposites were characterized using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). The flexible PSS-rGO/PANI-2 sensor exhibits excellent room temperature NH3 sensing performance, including a higher sensitivity of 362% and a faster response/recovery time of 23/158 s towards 100 ppm NH3 than other PSS-rGO/PANI nanocomposites. In addition, the flexible PSS-rGO/PANI-2 sensor has a low detection limit of 10 ppb, superior selectivity, repeatability, and long-term stability over 75 days. Remarkably, the flexible PSS-rGO/PANI-2 sensor shows excellent humidity resistance (196 ± 3%, 50 ppm) even at a high relative humidity of 80%. The gas sensing mechanism was systematically investigated through high protonation states and strong π-π conjugation of PSS-rGO/PANI. This work provides a convenient method to construct multichannel thin polyaniline layer-coated graphene nanocomposites and promotes their application in flexible wearable electronics.

Novel Selectivity: Target of Gas Sensing Defined by Behavior

Traditional selectivity of gas sensors determined by the magnitude of the response value has significant limitations. The distinctive inversion sensing behavior not only defies the traditional sensing theory but also provides insight into defining selectivity. Herein, the novel definition of selectivity is established in a study with VO2(M1). The sensing behavior of VO2(M1) is investigated after its synthesis conditions optimization by machine learning. In gases of the same nature, VO2(M1) shows remarkably selective for NH3 marked with unique resistance increase behavior. Such anomalous behavior is attributed to the formation of the Schottky junction between VO2(M1) and the electrode. The "work function-electron affinity" relation is summarized as the selectivity coefficient, a parameter for predicting the selectivity of the sensing material effectively.

Bi2Sn2O7 Overlayer Assists Bilayer Chemiresistor in Humidity-Independent and Highly Selective Detection of Expiratory Acetone

Constructing a bilayer structure has not been reported as a method to mitigate the adverse effect of water poisoning on oxide chemiresistors while simultaneously enhancing gas selectivity and sensitivity. To address this challenge, pyrochlore-Bi2Sn2O7 has been first utilized as an overlayer on a ZnO sensing layer for constructing a bilayer acetone chemiresistor, leading to remarkable improvement in the performance for trace-level (500 p-p-b) acetone detection under high humidity (80% relative humidity). In addition, owing to the catalytic predecompositions of ethanol across the overlayer, an outstanding acetone gas selectivity (Sacetone/Sethanol = 2.9) has been achieved, with a more than 4-fold improvement compared with monolayer ZnO chemiresistor (Sacetone/Sethanol = 0.7). More significantly, comprehensive experiments coupled with in situ characterizations have verified the generation of hydroxyl radicals (•OH) on the Bi2Sn2O7 overlayer. These radicals are capable of enhancing the kinetics between •OH and acetone, reducing the activation energy required for the gas sensing reaction, and thus leading to an unexpected phenomenon of enhanced acetone sensitivity under high humid conditions (Sacetone at 80% RH > Sacetone at 5% RH). These demonstrations offer crucial insight into the precise design of highly efficient overlayers for breath sensing.

Electrophilic-Attack Doped Organic Field-Effect Transistors for Ultrasensitive and Selective Hydrogen Sulfide Detection

Doping of organic semiconductors (OSCs) has been developed as an effective means of modulating the density and transfer efficiency of charge carriers; however, realization of effective doping to tailor the chemical sensing performance of OSC-based sensors still remains not explored extensively. In addition, the application of OSCs in chemical sensors is usually limited by the poor stability and low selectivity. Herein, flexible donor-acceptor copolymer-based organic field-effect transistor (OFET) chemical sensors are designed via an electrophilic attack doping strategy. The p-dopant trityl tetrakis(pentafluorophenyl) borate (TrTPFB) can be effectively doped into the host molecular N-alkyl-diketopyrrolo-pyrroledithienylthieno[3,2-b]thiophene (DPPDTT). It is simple to alter the doping efficiency and film thickness (ca. 5.5-17.7 nm) by adjusting the proportion and concentration of guest-host molecules, which endows facile carrier mobility and enhanced sensing sensitivity modulation toward reducing gases at room temperature. Particularly, 1.0 mol% TrTPFB-doped DPPDTT achieved the highest response to H2S gas, including ultralow detection concentration (0.5 ppb), excellent selectivity, high humidity stability, and long-term storage stability. This work can provide a new strategy for the potential applications of the organic electronic sensing devices.

Comprehensive analysis of risk factors (methanol, acetaldehyde and higher alcohols) in alcoholic beverages and their reduction strategies: GC-MS analysis and modified activated carbon adsorption and characterization

This study endeavors to examine the levels of risk factors in alcoholic beverages and propose mitigation strategies. GC-MS analysis was utilized to assess risk factors in various distilled-spirits. The content of such risk factors in spirits ranked as follows: vodka ≈ gin < baijiu < whiskey < brandy, and all were adhering to the Chinese national standard. Additionally, a method was refined to alleviate these risks, employing various reagents for activated carbon modification and evaluating their adsorption efficiency for risk factors reduction. Oxalic acid-modified activated carbon exhibited promising adsorption rates for risk factors with acceptable flavor compounds loss, rendering it a prospective solution for health hazard reduction. Characterization via SEM and nitrogen-adsorption-desorption was conducted on the optimal material, complemented by sensory experiments to optimize its application. This study offers valuable insights into the content of risk factors in alcoholic beverages, aiding in improving quality and safety of alcoholic beverages.

How Vibrational Notations Can Spoil Infrared Spectroscopy: A Case Study on Isolated Methanol

Unraveling methanol's infrared spectrum has challenged spectroscopists for a century, with numerous loose ends still to be explored. We engage in this exploration based on experiments of isolating single methanol molecules in solid argon and neon matrices. We report infrared spectra of methanol in its natural isotopic composition and with partial and full deuteration. These experiments are accompanied by calculating wavenumbers involving anharmonicity and mode-coupling based on the vibrational configuration interaction approach. This allows for an unambiguous assignment of all fundamentals and resonances in the mid-infrared spectrum. An increasing degree of deuteration lifts resonances and aids in assigning bands uniquely. It also becomes evident that different notations typically used in chemistry or physics to describe molecular vibration from spectroscopy fail to describe the spectra appropriately. We highlight the shortcomings and suggest a more elaborate analysis using Sankey diagrams to unambiguously identify spectral features. Consequently, we demystify debated resonances occurring from various stretches and deformations of the methyl group.

Metastable CoCu2O3 Nanocrystals from Combustion-Aerosols for Molecular Sensing and Catalysis

Metastable nanostructures are kinetically trapped in local energy minima featuring intriguing surface and material properties. To unleash their potential, there is a need for non-equilibrium processes capable of stabilizing a large range of crystal phases outside thermodynamic equilibrium conditions by closely and flexibly controlling atomic reactant composition, spatial temperature distribution and residence time. Here, the capture of metastable pseudo-binary metal oxides at room temperature is demonstrated with scalable combustion-aerosol processes. By a combination of X-ray diffraction, electron microscopy and on-line flame characterization, the occurrence of metastable CoCu2O3 is investigated with controlled crystal size (4-16 nm) over thermodynamically stable CuO and Co3O4. Immediate practical impact is demonstrated by exceptional sensing and stable catalytic performance for air pollutant detection (e.g., 15 parts-per-billion benzene) shown for, at least, 21 days. This approach can be extended to various binary, ternary and high entropy oxides with even more components. Also, secondary phases can be loaded on such metastable nanocrystals to access novel materials promising for actuators, energy storage or solar cells.

Optimizing Low-Cost Gas Analysis with a 3D Printed Column and MiCS-6814 Sensor for Volatile Compound Detection

This paper explores an application of 3D printing technology on the food industry. Since its inception in the 1980s, 3D printing has experienced a huge rise in popularity. This study uses cost-effective, flexible, and sustainable components that enable specific features of certain gas chromatography. This study aims to optimize the process of gas detection using a 3D printed separation column and the MiCS-6814 sensor. The principle of the entire device is based on the idea of utilizing a simple capillary chromatographic column manufactured by 3D printing for the separation of samples into components prior to their measurement using inexpensive chemiresistive sensors. An optimization of a system with a 3D printed PLA block containing a capillary, a mixing chamber, and a measuring chamber with a MiCS-6814 sensor was performed. The optimization distributed the sensor output signal in the time domain so that it was possible to distinguish the peak for the two most common alcohols, ethanol and methanol. The paper further describes some optimization types and their possibilities.

Electrochemical Analysis of Methanol with Nafion-Coated Copper Oxide Nanoparticles

This work introduces a Nafion-coated copper(I) oxide nanoparticle electrode (Nafion/Cu2O/GC) designed for the electrochemical detection of methanol (CH3OH). The responses of the composite material toward CH3OH were enhanced by the selective permeation of CH3OH through the hydrophilic channels of the Nafion membrane in combination with the electroactivity of Cu2O nanoparticles. The sensor displayed a linear detection range of 0.33-100 mM CH3OH with a sensitivity of 0.17 μA·mM-1 and a detection limit (3 s/m) of 0.10 mM. It exhibited excellent reproducibility with a relative standard deviation of <5%. The sensor's practical applicability was demonstrated through recovery studies on hand sanitizer samples, achieving ca. 100% recovery. The sensor was further used to elucidate CH3OH adsorption on activated carbon, revealing that the process conforms to the Langmuir isotherm model.

Surface Defect-Induced Specific Catalysis Activates 100% Selective Sensing toward Amine Gases at Room Temperature

Achieving selective sensing toward target volatile organic compound gases is of vital importance in the fields of air quality assessment, food freshness evaluation, and diagnosis of patients via exhaled breath. However, chemiresistive sensors that exhibit specificity like biological enzymes in a complex environment are rare. Herein, we developed a strategy of optimizing oxygen vacancy structures in tin oxides to induce specific catalysis, activating 100% selective sensing toward amine gases at room temperature. In situ technologies and theoretical calculations reveal that the "donor-receptor" coordination between nitrogen atoms from amine molecules and bridging oxygen vacancies (OVBri)-induced electron-deficient center is the essence of specific catalysis and provides the bridge from the surface oxidation reaction to electrophysical characteristics evolution, which allows the sensor to exhibit amine-specific sensing behavior, even in gas mixtures. Moreover, OVBri enhances the selectivity by enabling a room-temperature sensing pathway where lattice oxygens participate in catalytic oxidation for amine molecules, resulting in record-high sensing values: 19,938.92 toward 100 ppm of triethylamine, 15,236.78 toward trimethylamine, and 123.41 toward diethylamine. Our findings illustrate the feasibility of designing specific active sites through defect engineering and can contribute to the advancement of highly selective sensors based on catalytic processes.

Wide-range and high-accuracy wireless sensor with self-humidity compensation for real-time ammonia monitoring

Real-time and accurate biomarker detection is highly desired in point-of-care diagnosis, food freshness monitoring, and hazardous leakage warning. However, achieving such an objective with existing technologies is still challenging. Herein, we demonstrate a wireless inductor-capacitor (LC) chemical sensor based on platinum-doped partially deprotonated-polypyrrole (Pt-PPy+ and PPy0) for real-time and accurate ammonia (NH3) detection. With the chemically wide-range tunability of PPy in conductivity to modulate the impedance, the LC sensor exhibits an up-to-180% improvement in return loss (S11). The Pt-PPy+ and PPy0 shows the p-type semiconductor nature with greatly-manifested adsorption-charge transfer dynamics toward NH3, leading to an unprecedented NH3 sensing range. The S11 and frequency of the Pt-PPy+ and PPy0-based sensor exhibit discriminative response behaviors to humidity and NH3, enabling the without-external-calibration compensation and accurate NH3 detection. A portable system combining the proposed wireless chemical sensor and a handheld instrument is validated, which aids in rationalizing strategies for individuals toward various scenarios.

Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses

This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.

Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspended 1D nanoheater, driven by duty cycling─characterized by repeated pulsed power inputs. The sensor's ultrafast thermal response, enabled by its small size, effectively decouples the effects of temperature and surface charge exchange on the MOS nanomaterial's conductivity. This provides distinct sensing signals that alternate between responses coupled with and decoupled from the thermally enhanced conductivity, all within a single time domain during duty cycling. The magnitude and ratio of these dual responses vary depending on the gas type and concentration, facilitating the early stage gas identification of five gas types within 30 s via a convolutional neural network (classification accuracy = 93.9%, concentration regression error = 19.8%). Additionally, the duty-cycling mode significantly reduces power consumption by up to 90%, lowering it to 160 μW to heat the sensor to 250 °C. Manufactured using only wafer-level batch microfabrication processes, this innovative e-nose system promises the facile implementation of battery-driven, long-term, and cost-effective IoT monitoring systems.

How the Interplay between SnO2 and Zn(II) Porphyrins Impacts on the Electronic Features of Gaseous Acetone Chemiresistors

Herein, the integration of SnO2 nanoparticles with two Zn(II) porphyrins─Zn(II) 5,10,15,20-tetraphenylporphyrin (ZnTPP) and its perfluorinated counterpart, Zn(II) 5,10,15,20-tetrakis(pentafluorophenyl)porphyrin (ZnTPPF20)─was investigated for the sensing of gaseous acetone at 120 °C, adopting three Zn-porphyrin/SnO2 weight ratios (1:4, 1:32, and 1:64). For the first time, we were able to provide evidence of the correlation between the materials' conductivity and these nanocomposites' sensing performances, obtaining optimal results with a 1:32 ratio for ZnTPPF20/SnO2 and showcasing a remarkable detection limit of 200 ppb together with a boosted sensing signal with respect to bare SnO2. To delve deeper, the combination of experimental data with density functional theory calculations unveiled an electron-donating behavior of both porphyrins when interacting with tin dioxide semiconductor, especially for the nonfluorinated one. The study suggested that the interplay between electrons injected, from the porphyrins' highest occupied molecular orbital to SnO2 conduction band, and the latter's available electronic states has a dramatic impact to boost the chemiresistive sensing. Indeed, we highlighted that the key lies in preventing the full saturation of SnO2 electronic states concomitantly increasing the materials' conductivity: in this respect, the best compromise turned out to be the perfluorinated porphyrin. A further corroboration of our findings was obtained by illuminating the sensors during measurements with light-emitting diode (LED) light. Actually, we demonstrated that it does not have any impact on improving the sensing behavior, most probably due to the electronic oversaturation and scattering caused by LED excitation in porphyrins. Lastly, the most effective hybrids (1:32 ratio) were physicochemically characterized, confirming the physisorption of the macrocycles onto the SnO2 surface. In conclusion, herein, we underscore the feasibility of customizing the porphyrin chemistry and porphyrin-to-SnO2 ratio to enhance the gaseous sensing of bare metal oxides, providing valuable insights for the engineering of highly performing light-free chemiresistors.

Compact 3D-Printed Unit for Separation of Simple Gas Mixtures Combined with Chemiresistive Sensors

Inexpensive chemiresistive sensors are often insufficiently selective as they are sensitive to multiple components of the gas mixture at the same time. One solution would be to insert a device in front of the sensor that separates the measured gas mixture and possibly isolates the unwanted components. This study focused on the fabrication and characterization of a compact unit, which was fabricated by 3D printing, for the separation and detection of simple gas mixtures. The capillary, the basic part of the compact unit, was 4.689 m long and had a diameter of 0.7 mm. The compact unit also contained a mixing chamber on the inlet side and a measuring chamber with a MiCS-6814 sensor on the outlet side. Mixtures of ethanol and water at different concentrations were chosen for characterization. The measured calibration curve was found to have a reliability of R2 = 0.9941. The study further addressed the elements of environmental friendliness of the materials used and their sustainability.

Handheld methanol detector for beverage analysis: interlaboratory validation

Methanol is a toxic alcohol contained in alcoholic beverages as a natural byproduct of fermentation or added intentionally to counterfeits to increase profit. To ensure consumer safety, many countries and the EU have established strict legislation limits for methanol content. Methanol concentration is mostly detected by laboratory instrumentation since mobile devices for routine on-site testing of beverages in distilleries, at border stations or even at home are not available. Here, we validated a handheld methanol detector for beverage analysis in an ISO 5725 interlaboratory trial: a total of 119 measurements were performed by 17 independent participants (distilleries, universities, authorities, and competence centers) from six countries on samples with relevant methanol concentrations (0.1, 1.5 vol%). The detector was based on a microporous separation filter and a nanostructured gas sensor allowing on-site measurement of methanol down to 0.01 vol% (in the liquid) within only 2 min by laymen. The detector showed excellent repeatability (<5.4%), reproducibility (<9.5%) and small bias (<0.012 vol%). Additional measurements on various methanol-spiked alcoholic beverages (whisky, rum, gin, vodka, tequila, port, sherry, liqueur) indicated that the detector is not interfered by environmental temperature and spirit composition, featuring excellent linearity (R2 > 0.99) down to methanol concentrations of 0.01 vol%. This device has been recently commercialized (Alivion Spark M-20) with comparable accuracy to the gold-standard gas chromatography and can be readily applied for final product inspection, intake control of raw materials or to identify toxic counterfeit products.

Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation

Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.

The Challenges and Opportunities for TiO2 Nanostructures in Gas Sensing

Chemiresistive gas sensors based on metal oxides have been widely applied in industrial monitoring, medical diagnosis, environmental pollutant detection, and food safety. To further enhance the gas sensing performance, researchers have worked to modify the structure and function of the material so that it can adapt to different gas types and environmental conditions. Among the numerous gas-sensitive materials, n-type TiO2 semiconductors are a focus of attention for their high stability, excellent biosafety, controllable carrier concentration, and low manufacturing cost. This Perspective first introduces the sensing mechanism of TiO2 nanostructures and composite TiO2-based nanomaterials and then analyzes the relationship between their gas-sensitive properties and their structure and composition, focusing also on technical issues such as doping, heterojunctions, and functional applications. The applications and challenges of TiO2-based nanostructured gas sensors in food safety, medical diagnosis, environmental detection, and other fields are also summarized in detail. Finally, in the context of their practical application challenges, future development technologies and new sensing concepts are explored, providing new ideas and directions for the development of multifunctional intelligent gas sensors in various application fields.

MOF-derived xPd-NPs@ZnO porous nanocomposites for ultrasensitive ppb-level gas detection with photoexcitation: Design, diverse-scenario characterization, and mechanism

Metal-organic frameworks (MOFs) have been regarded as a potential candidate with great application prospects in the field of gas sensing. Although plenty of previous efforts have been made to improve the sensitivity of MOF-based nanocomposites, it is still a great challenge to realize ultrafast and high selectivity to typical flammable gases in a wide range. Herein, porous xPd-NPs@ZnO were prepared by optimized heat treatment, which maintained the controllable morphology and high specific surface area of 471.08 m2g-1. The coupling effects of photoexcitation and thermal excitation on the gas-sensing properties of nanocomposites were systematically studied. An ultrafast high response of 88.37 % towards 200 ppm H2 was realized within 1.2 s by 5.0Pd-NPs@ZnO under UV photoexcitation. All xPd-NPs@ZnO exhibited favorable linearity over an extremely wide range (0.2-4000 ppm H2) of experimental tests, indicating the great potential in quantitative detection. The photoexcited carriers enabled the nanocomposites a considerable response at lower operating temperatures, which made diverse applications of the sensors. The mechanisms of high sensing performances and the photoexcitation enhancement were systematically explained by DFT calculations. This work provides a solid experimental foundation and theoretical basis for the design of controllable porous materials and novel photoexcited gas detection.

Molecularly imprinted polymer nanoparticle-carbon nanotube composite electrochemical gas sensor for highly selective and sensitive detection of methanol vapour

An electrochemical gas sensor has been fabricated using molecularly imprinted polymer nanoparticles (nanoMIPs) and multiwalled carbon nanotubes on screen-printed electrodes. Methanol vapour was chosen as the target due to its toxicity as its suitability as a model for more harmful pollutant gases. The sensor functions under ambient conditions and in the required concentration range, in contrast to all previous MIP-based gas sensors for methanol. The sensitivity of the sensor was greatly improved by the addition of multiwall carbon nanotubes, resulting in a limit of detection of approximately 10 ppm. The nanoMIPs provide an inherent selectivity for the target inherent in its design. Selectivity studies were performed with structurally analogous alcohols at various concentrations, demonstrating selectivity for methanol 12.1 times that for ethanol at 2 mmol dm-3 and 4.2 times that for ethanol at 1 mmol dm-3. Interactions with isopropanol and n-propanol were found to be non-specific, and the response to water was negligible. This demonstrates an improvement over previous methanol gas sensors based on molecularly imprinted polymers. No response was observed with carbon nanotubes alone, and no selectivity was observed with non-imprinted equivalents of the nanoMIP sensor. The resulting device is by far the most practical MIP-based instrument for methanol gas sensing thus far described in the literature, being the only example capable of functioning at the necessary methanol vapour concentrations and at the required temperature and humidity. With the selectivity and sensitivity described and the simple design, the developed device provides a substantial advance in the field of molecularly imprinted gas sensors.

Essential role of lattice oxygen in hydrogen sensing reaction

Understanding the sensing mechanism of metal oxide semiconductors is imperative to the development of high-performance sensors. The traditional sensing mechanism only recognizes the effect of surface chemisorbed oxygen from the air but ignores surface lattice oxygen. Herein, using in-situ characterizations, we provide direct experimental evidence that the surface chemisorbed oxygen participated in the sensing process can come from lattice oxygen of the oxides. Further density functional theory (DFT) calculations prove that the p-band center of O serves as a state of art for regulating the participation of lattice oxygen in gas-sensing reactions. Based on our experimental data and theoretical calculations, we discuss mechanisms that are fundamentally different from the conventional mechanism and show that the easily participation of lattice oxygen is helpful for the high response value of the materials.

Dispersion and Dosimetric Challenges of Hydrophobic Carbon-Based Nanoparticles in In Vitro Cellular Studies

Methodologies across the dispersion preparation, characterization, and cellular dosimetry of hydrophilic nanoparticles (NPs) have been developed and used extensively in the field of nanotoxicology. However, hydrophobic NPs pose a challenge for dispersion in aqueous culture media using conventional methods that include sonication followed by mixing in the culture medium of interest and cellular dosimetry. In this study, a robust methodology for the preparation of stable dispersions of hydrophobic NPs for cellular studies is developed by introducing continuous energy over time via stirring in the culture medium followed by dispersion characterization and cellular dosimetry. The stirring energy and the presence of proteins in the culture medium result in the formation of a protein corona around the NPs, stabilizing their dispersion, which can be used for in vitro cellular studies. The identification of the optimal stirring time is crucial for achieving dispersion and stability. This is assessed through a comprehensive stability testing protocol employing dynamic light scattering to evaluate the particle size distribution stability and polydispersity. Additionally, the effective density of the NPs is obtained for the stable NP dispersions using the volumetric centrifugation method, while cellular dosimetry calculations are done using available cellular computational modeling, mirroring approaches used for hydrophilic NPs. The robustness of the proposed dispersion approach is showcased using a highly hydrophobic NP model (black carbon NPs) and two culture media, RPMI medium and SABM, that are widely used in cellular studies. The proposed approach for the dispersion of hydrophobic NPs results in stable dispersions in both culture media used here. The NP effective density of 1.03-1.07 g/cm3 measured here for black carbon NPs is close to the culture media density, resulting in slow deposition on the cells over time. So, the present methodology for dispersion and dosimetry of hydrophobic NPs is essential for the design of dose-response studies and overcoming the challenges imposed by slow particle deposition.

Oxygen vacancy-enriched Zn2SnO4 for aliphatic alcohol sensing and enhanced selectivity towards n-butanol

The sensitive detection of toxic flammable volatile organics using low cost efficient sensors is important for ensuring both indoor and outdoor safety. It is essential for chemical sensors to exhibit a significantly stronger response to target analytes compared to equivalent amounts of analogous competing chemicals. In line with this importance, current work evaluated the performance of Zn2SnO4, a n-type semiconducting metal oxide, for sensing n-butanol in comparison to methanol, ethanol, and propanol vapours. These vapours fall within the category of aliphatic alcohols but vary in characteristics such as molecular weight, vapour pressure, volatility, and diffusivity. In this work we have explored the sensor's performance by adjusting the operating temperature over the range of 225-300 °C while detecting 1000 ppm of each of these vapours. Efforts were made to establish a correlation between the sensor's responses with the interactions of these vapours on the sensor's surface. Prior to assessing the sensing characteristics of the solid-state-route-derived Zn2SnO4, its structural characteristics, including phase purity, crystalline structure, bonding patterns, morphology, and defect characteristics, were studied. This comprehensive analysis sheds light on the potential of Zn2SnO4 as an effective sensor for detecting n-butanol.

Achieving Molecular-Level Selective Detection of Volatile Organic Compounds through a Strong Coupling Effect of Ultrathin Nanosheets and Au Nanoparticles

The high density of surface active sites, high efficiency of interfacial carrier transport, and molecular diffusion path determine the efficiency of the electrochemical sensors. The ultrathin structures have atomic-level thickness, carrier migration and heat diffusion are limited in the two-dimensional plane, resulting in excellent conductivity and high carrier concentration. A one-step chemical method is applied to synthesize defect-rich Au-SnO2 in an ultrathin nanosheet form (thickness of 2-3 nm). The strong interaction between Au and SnO2 via the Au-O-Sn bonding and the catalytic effect of Au can prolong the service life via decreasing the optimal operating temperature (55 °C) and promote the Au-SnO2 sensor to exclusively detect formaldehyde at the ppb level (300 ppb). The experimental findings along with theoretical study reveal that Au nanoparticles have a different effect on the competitive adsorption and chemical reaction over the surface of the Au-SnO2 with formaldehyde and other interfering VOC gases, such as methanol, ethanol, and acetone. This study provides mechanistic insights into the correlation between operating temperature and the performance of the Au-SnO2 chemiresistive sensor. This work allows the development of highly efficient and stable electrochemical sensors to detect VOC gases at room temperature in the future.

One Droplet toward Efficient Alcohol Detection Using Femtosecond Laser Textured Micro/Nanostructured Surface with Superwettability

Alcohol with different concentrations is commonly used in food, industry, and medicine fields all over the world. However, current methods for detecting alcohol concentration are restricted to large sample consumption, additional senergy consuming, or complex operations. Here, inspired by superwettability of lotus leaves, a superhydrophobic and superorganophilic surface is designed on the polydimethylsiloxane (PDMS) for one droplet efficient alcohol detection, which is prepared via femtosecond laser direct writing technology. Meanwhile, the contact angles of droplets with various alcohol concentrations on the laser-treated PDMS (LTP) surface are different. Based on the above characteristic, alcohol concentration through contact angle measurement without any external energy is directly detected, which is simple and efficient. Furthermore, it is worth noting that the LTP surface remains stable wettability after 1000 water-ethanol cycles and 300 days tests in air, indicating strong surface repeatability and stability. Significantly, the LTP surface has a broad potential application in one droplet detecting alcohol concentration, fake or genuine wine, and alcohol molecules. This work provides a new strategy to fabricate a superwetting surface for efficient one droplet alcohol detection.

d-Band Center Optimization of Ti3C2Tx MXene Nanosheets for Ultrahigh NO2 Gas Sensitivity at Room Temperature

MXene exhibits numerous advantageous properties such as high electronic conductivity, high surface area, and ease of surface modification via tailoring of functional groups. However, the mechanism by which MXene functionalization enhances gas sensing performance has not yet been well understood, let alone the development of a rational sensor design optimization strategy. This work presents a functionalization methodology for MXene based on d-band center modulation, which can be implemented by introducing Fe onto the surface of Ti3C2Tx nanosheets, for significantly improved gas sensing response and selectivity. The strategy is demonstrated in the design of gas sensors. The optimized gas sensor shows a response of 50% toward 10 ppm of NO2 at room temperature, which is over 6-fold improvement from its pristine counterpart, an unprecedented performance level among all reported MXene gas sensors. XPS characterizations, valence band analyses, and density functional theory (DFT) calculations all indicate that the underlying enhancement mechanism can be attributed to the tuning of the d-band center energy toward the Fermi level. This work provides a new design strategy based on the optimization of the d-band center energy and adds a much needed systematic and quantitative method to the design of two-dimensional materials based semiconducting gas sensors.

Alkaloid Precipitant Reaction Inspired Controllable Synthesis of Mesoporous Tungsten Oxide Spheres for Biomarker Sensing

Highly porous sensitive materials with well-defined structures and morphologies are extremely desirable for developing high-performance chemiresistive gas sensors. Herein, inspired by the classical alkaloid precipitant reaction, a robust and reliable active mesoporous nitrogen polymer sphere-directed synthesis method was demonstrated for the controllable construction of heteroatom-doped mesoporous tungsten oxide spheres. In the typical synthesis, P-doped mesoporous WO3 monodisperse spheres with radially oriented channels (P-mWO3-R) were obtained with a diameter of ∼180 nm, high specific surface area, and crystalline skeleton. The in situ-introduced P atoms could effectively adjust the coordination environment of W atoms (Wδ+-Ov), giving rise to dramatically enhanced active surface-adsorbed oxygen species and unusual metastable ε-WO3 crystallites. The P-mWO3-R spheres were applied for the sensing of 3-hydroxy-2-butanone (3H2B), a biomarker of foodborne pathogenic bacteria Listeria monocytogenes (LM). The sensor exhibited high sensitivity (Ra/Rg = 29 to 3 ppm), fast response dynamics (26/7 s), outstanding selectivity, and good long-term stability. Furthermore, the device was integrated into a wireless sensing module to realize remote real-time and precise detection of LM in practical applications, making it possible to evaluate food quality using gas sensors conveniently.

Road Map of Semiconductor Metal-Oxide-Based Sensors: A Review

Identifying disease biomarkers and detecting hazardous, explosive, flammable, and polluting gases and chemicals with extremely sensitive and selective sensor devices remains a challenging and time-consuming research challenge. Due to their exceptional characteristics, semiconducting metal oxides (SMOxs) have received a lot of attention in terms of the development of various types of sensors in recent years. The key performance indicators of SMOx-based sensors are their sensitivity, selectivity, recovery time, and steady response over time. SMOx-based sensors are discussed in this review based on their different properties. Surface properties of the functional material, such as its (nano)structure, morphology, and crystallinity, greatly influence sensor performance. A few examples of the complicated and poorly understood processes involved in SMOx sensing systems are adsorption and chemisorption, charge transfers, and oxygen migration. The future prospects of SMOx-based gas sensors, chemical sensors, and biological sensors are also discussed.

Handheld device quantifies breath acetone for real-life metabolic health monitoring

Non-invasive breath analysis with mobile health devices bears tremendous potential to guide therapeutic treatment and personalize lifestyle changes. Of particular interest is the breath volatile acetone, a biomarker for fat burning, that could help in understanding and treating metabolic diseases. Here, we report a hand-held (6 × 10 × 19.5 cm³), light-weight (490 g), and simple device for rapid acetone detection in breath. It comprises a tailor-made end-tidal breath sampling unit, connected to a sensor and a pump for on-demand breath sampling, all operated using a Raspberry Pi microcontroller connected with a HDMI touchscreen. Accurate acetone detection is enabled by introducing a catalytic filter and a separation column, which remove and separate undesired interferents from acetone upstream of the sensor. This way, acetone is detected selectively even in complex gas mixtures containing highly concentrated interferents. This device accurately tracks breath acetone concentrations in the exhaled breath of five volunteers during a ketogenic diet, being as high as 26.3 ppm. Most importantly, it can differentiate small acetone changes during a baseline visit as well as before and after an exercise stimulus, being as low as 0.5 ppm. It is stable for at least four months (122 days), and features excellent bias and precision of 0.03 and 0.6 ppm at concentrations below 5 ppm, as validated by proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). Hence, this detector is highly promising for simple-in-use, non-invasive, and routine monitoring of acetone to guide therapeutic treatment and track lifestyle changes.

Awareness raising and dealing with methanol poisoning based on effective strategies

Intoxication with methanol most commonly occurs as a consequence of ingesting, inhaling, or coming into contact with formulations that include methanol as a base. Clinical manifestations of methanol poisoning include suppression of the central nervous system, gastrointestinal symptoms, and decompensated metabolic acidosis, which is associated with impaired vision and either early or late blindness within 0.5-4 h after ingestion. After ingestion, methanol concentrations in the blood that are greater than 50 mg/dl should raise some concern. Ingested methanol is typically digested by alcohol dehydrogenase (ADH), and it is subsequently redistributed to the body's water to attain a volume distribution that is about equivalent to 0.77 L/kg. Moreover, it is removed from the body as its natural, unchanged parent molecules. Due to the fact that methanol poisoning is relatively uncommon but frequently involves a large number of victims at the same time, this type of incident occupies a special position in the field of clinical toxicology. The beginning of the COVID-19 pandemic has resulted in an increase in erroneous assumptions regarding the preventative capability of methanol in comparison to viral infection. More than 1000 Iranians fell ill, and more than 300 of them passed away in March of this year after they consumed methanol in the expectation that it would protect them from a new coronavirus. The Atlanta epidemic, which involved 323 individuals and resulted in the deaths of 41, is one example of mass poisoning. Another example is the Kristiansand outbreak, which involved 70 people and resulted in the deaths of three. In 2003, the AAPCC received reports of more than one thousand pediatric exposures. Since methanol poisoning is associated with high mortality rates, it is vital that the condition be addressed seriously and managed as quickly as feasible. The objective of this review was to raise awareness about the mechanism and metabolism of methanol toxicity, the introduction of therapeutic interventions such as gastrointestinal decontamination and methanol metabolism inhibition, the correction of metabolic disturbances, and the establishment of novel diagnostic/screening nanoparticle-based strategies for methanol poisoning such as the discovery of ADH inhibitors as well as the detection of the adulteration of alcoholic drinks by nanoparticles in order to prevent methanol poisoning. In conclusion, increasing warnings and knowledge about clinical manifestations, medical interventions, and novel strategies for methanol poisoning probably results in a decrease in the death load.

Dynamic Breath Limonene Sensing at High Selectivity

Liver diseases (e.g., cirrhosis, cancer) cause more than two million deaths per year worldwide. This is partly attributed to late diagnosis and insufficient screening techniques. A promising biomarker for noninvasive and inexpensive liver disease screening is breath limonene that can indicate a deficiency of the cytochrome P450 liver enzymes. Here, we introduce a compact and low-cost detector for dynamic and selective breath limonene sensing. It comprises a chemoresistive sensor based on Si/WO₃ nanoparticles pre-screened by a packed bed Tenax separation column at room temperature. We demonstrate selective limonene detection down to 20 parts per billion over up to three orders of magnitude higher concentrated acetone, ethanol, hydrogen, methanol, and 2-propanol in gas mixtures, as well as robustness to 10-90% relative humidity. Most importantly, this detector recognizes the individual breath limonene dynamics of four healthy volunteers following the ingestion (swallowing or chewing) of a limonene capsule. Limonene release and subsequent metabolization are monitored from breath measurements in real time and in excellent agreement (R² = 0.98) with high-resolution proton transfer reaction mass spectrometry. This study demonstrates the potential of the detector as a simple-to-use and noninvasive device for the routine monitoring of limonene levels in exhaled breath to facilitate early diagnosis of liver dysfunction.

Metal-Phenolic Film Coated Quartz Crystal Microbalance as a Selective Sensor for Methanol Detection in Alcoholic Beverages

The facile real-time monitoring of methyl content in fermented beverages is of fundamental significance in the alcohol and restaurant industry, since as little as 4 mL of methanol entering the blood may cause intoxication or blindness. So far, the practical applicability of available methanol sensors, including the piezoresonance analogs, is somewhat limited to laboratory use due to the complexity and bulkiness of the measuring equipment involving multistep procedures. This article introduces a hydrophobic metal-phenolic film-coated quartz crystal microbalance (MPF-QCM) as a novel streamlined detector of methanol in alcoholic drinks. Unlike other QCM-based alcohol sensors, our device operates under saturated vapor pressure conditions, permitting rapid detection of methyl fractions up to seven times below the tolerable levels in spirits (e.g., whisky) while effectively suppressing the cross-sensitivity to interfering chemical compounds such as water, petroleum ether or ammonium hydroxide. Furthermore, the good surface adhesion of metal-phenolic complexes endows the MPF-QCM with superior long-term stability, contributing to the repeatable and reversible physical sorption of the target analytes. These features, combined with the lack of mass flow controllers, valves and connecting pipes delivering the gas mixture, outline the likelihood for future design of a portable MPF-QCM prototype suitable to point-of-use analysis in drinking establishments.

Self-Assembling Nanoarchitectonics of Twisted Nanofibers of Fluorescent Amphiphiles as Chemo-Resistive Sensor for Methanol Detection

The inhalation, ingestion, and body absorption of noxious gases lead to severe tissue damage, ophthalmological issues, and neurodegenerative disorders; death may even occur when recognized too late. In particular, methanol gas present in traces can cause blindness, non-reversible organ failure, and even death. Even though ample materials are available for the detection of methanol in other alcoholic analogs at ppm level, their scope is very limited because of the use of either toxic or expensive raw materials or tedious fabrication procedures. In this paper, we report on a simple synthesis of fluorescent amphiphiles achieved using a starting material derived from renewable resources, this material being methyl ricinoleate in good yields. The newly synthesized bio-based amphiphiles were prone to form a gel in a broad range of solvents. The morphology of the gel and the molecular-level interaction involved in the self-assembly process were thoroughly investigated. Rheological studies were carried out to probe the stability, thermal processability, and thixotropic behavior. In order to evaluate the potential application of the self-assembled gel in the field of sensors, we performed sensor measurements. Interestingly, the twisted fibers derived from the molecular assembly could be able to display a stable and selective response towards methanol. We believe that the bottom-up assembled system holds great promise in the environmental, healthcare, medicine, and biological fields.

Soft, miniaturized, wireless olfactory interface for virtual reality

Recent advances in virtual reality (VR) technologies accelerate the creation of a flawless 3D virtual world to provide frontier social platform for human. Equally important to traditional visual, auditory and tactile sensations, olfaction exerts both physiological and psychological influences on humans. Here, we report a concept of skin-interfaced olfactory feedback systems with wirelessly, programmable capabilities based on arrays of flexible and miniaturized odor generators (OGs) for olfactory VR applications. By optimizing the materials selection, design layout, and power management, the OGs exhibit outstanding device performance in various aspects, from response rate, to odor concentration control, to long-term continuous operation, to high mechanical/electrical stability and to low power consumption. Representative demonstrations in 4D movie watching, smell message delivery, medical treatment, human emotion control and VR/AR based online teaching prove the great potential of the soft olfaction interface in various practical applications, including entertainment, education, human machine interfaces and so on.

A conductometric enzymatic methanol sensor based on polystyrene - PAMAM dendritic polymer electrospun nanofibers

Methanol (MeOH) is a solvent and cleaning agent used in industry, but it is poisonous when ingested. The recommended release threshold for MeOH vapor is 200 ppm. We present a novel sensitive micro-conductometric MeOH biosensor created by grafting alcohol oxidase (AOX) onto electrospun polystyrene-poly(amidoamine) dendritic polymer blend nanofibers (PS-PAMAM-ESNFs) on interdigitated electrodes (IDEs). The analytical performance of the MeOH microsensor was evaluated using gaseous MeOH, ethanol, and acetone samples collected from the headspace above aqueous solution with known concentration. The sensor's response time (t_Res) fluctuates from 13 s to 35 s from lower to higher concentrations. The conductometric sensor has a sensitivity of 150.53 μS.cm-1 (v/v) for MeOH and a detection limit of 100 ppm in the gas phase. The MeOH sensor is 7.3 times less sensitive to ethanol and 136.8 times less sensitive to acetone. The sensor was verified for detecting MeOH in commercial rubbing alcohol samples.

In-vehicle wireless driver breath alcohol detection system using a microheater integrated gas sensor based on Sn-doped CuO nanostructures

In this paper, we have developed an in-vehicle wireless driver breath alcohol detection (IDBAD) system based on Sn-doped CuO nanostructures. When the proposed system detects the ethanol trace in the driver`s exhaled breath, it can alarm and then prevents the car to be started and also sends the location of the car to the mobile phone. The sensor used in this system is a two-sided micro-heater integrated resistive ethanol gas sensor fabricated based on Sn-doped CuO nanostructures. Pristine and Sn-doped CuO nanostructures were synthesized as the sensing materials. The micro-heater is calibrated to provide the desired temperature by applying voltage. The results showed that by Sn-doping in CuO nanostructures, the sensor performance can be significantly improved. The proposed gas sensor has a fast response, good repeatability along with good selectivity that makes it suitable for being used in practical applications such as the proposed system.

Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

Quartz Crystal Microbalance Coated with Polyacrylonitrile/Nickel Nanofibers for High-Performance Methanol Gas Detection

This study describes a sensor based on quartz crystal microbalance (QCM) coated by polyacrylonitrile (PAN) nanofibers containing nickel nanoparticles for methanol gas detection. The PAN/nickel nanofibers composites were made via electrospinning and electrospray methods. The QCM sensors coated with the PAN/nickel nanofiber composite were evaluated for their sensitivities, selectivities, and stabilities. The morphologies and elemental compositions of the sensors were examined using a scanning electron microscope-energy dispersive X-ray. A Fourier Transform Infrared spectrometer was used to investigate the elemental bonds within the nanofiber composites. The QCM sensors coated with PAN/nickel nanofibers offered a high specific surface area to enhance the QCM sensing performance. They exhibited excellent sensing characteristics, including a high sensitivity of 389.8 ± 3.8 Hz/SCCM, response and recovery times of 288 and 251 s, respectively, high selectivity for methanol compared to other gases, a limit of detection (LOD) of about 1.347 SCCM, and good long-term stability. The mechanism of methanol gas adsorption by the PAN/nickel nanofibers can be attributed to intermolecular interactions, such as the Lewis acid-base reaction by PAN nanofibers and hydrogen bonding by nickel nanoparticles. The results suggest that QCM-coated PAN/nickel nanofiber composites show great potential for the design of highly sensitive and selective methanol gas sensors.