Prediction of Individual Gas Yields of Supercritical Water Gasification of Lignocellulosic Biomass by Machine Learning Models.

Supercritical water gasification (SCWG) of lignocellulosic biomass is a promising pathway for the production of hydrogen. However, SCWG is a complex thermochemical process, the modeling of which is challenging via conventional methodologies. Therefore, eight machine learning models (linear regression (LR), Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical boosting regressor (CatBoost)) with particle swarm optimization (PSO) and a genetic algorithm (GA) optimizer were developed and evaluated for prediction of H2, CO, CO2, and CH4 gas yields from SCWG of lignocellulosic biomass. A total of 12 input features of SCWG process conditions (temperature, time, concentration, pressure) and biomass properties (C, H, N, S, VM, moisture, ash, real feed) were utilized for the prediction of gas yields using 166 data points. Among machine learning models, boosting ensemble tree models such as XGB and CatBoost demonstrated the highest power for the prediction of gas yields. PSO-optimized XGB was the best performing model for H2 yield with a test R2 of 0.84 and PSO-optimized CatBoost was best for prediction of yields of CH4, CO, and CO2, with test R2 values of 0.83, 0.94, and 0.92, respectively. The effectiveness of the PSO optimizer in improving the prediction ability of the unoptimized machine learning model was higher compared to the GA optimizer for all gas yields. Feature analysis using Shapley additive explanation (SHAP) based on best performing models showed that (21.93%) temperature, (24.85%) C, (16.93%) ash, and (29.73%) C were the most dominant features for the prediction of H2, CH4, CO, and CO2 gas yields, respectively. Even though temperature was the most dominant feature, the cumulative feature importance of biomass characteristics variables (C, H, N, S, VM, moisture, ash, real feed) as a group was higher than that of the SCWG process condition variables (temperature, time, concentration, pressure) for the prediction of all gas yields. SHAP two-way analysis confirmed the strong interactive behavior of input features on the prediction of gas yields.

Quantum-level investigation of air decomposed pollutants gas sensor (Pd-modified g-C3N4) influenced by micro-water content.

In the electrical industry, there are many hazardous gases that pollute the environment and even jeopardize human health, so timely detection and effective control of these hazardous gases is of great significance. In this work, the gas-sensitive properties of Pd-modified g-C3N4 interface for each hazardous gas molecule were investigated from a microscopic viewpoint, taking the hazardous gases (CO, NOx) that may be generated in the power industry as the detection target. Then, the performance of Pd-modifiedg-C3N4 was evaluated for practical applications as a gas sensor material. Novelly, an unconventional means was designed to briefly predict the effect of humidity on the adsorption properties of this sensor material. The final results found that Pd-modified g-C3N4 is most suitable as a potential gas-sensitizing material for NO2 gas sensors, followed by CO. Interestingly, Pd-modified g-C3N4 is less suitable as a potential gas-sensitizing material for NO gas sensors, but has the potential to be used as a NO cleaner (adsorbent). Unconventional simulation explorations of humidity effects show that in practical applications Pd-modified g-C3N4 remains a promising material for gas sensing in specific humidity environments. This work reveals the origin of the excellent properties of Pd-modified g-C3N4 as a gas sensor material and provides new ideas for the detection and treatment of these three hazardous gases.

Plastic waste gasification using oxygen-enriched air and steam: Experimental and model results from a large pilot-scale reactor.

Advanced thermochemical technologies for plastic waste valorization represent an interesting alternative to waste-to-energy options. They are particularly appealing for waste-to-hydrogen and waste-to-chemicals applications, with autothermal steam-oxygen gasification in fluidized bed reactors showing the greatest market potential. The study describes a series of experimental tests carried out on a large pilot-scale fluidized bed gasifier, using steam and O2-enriched air, with increasing fractions of oxygen. Different values of the main operating parameters are varied: equivalence ratio (0.22-0.25), steam-to-carbon ratio (0.7-1.13), and steam-to-oxygen ratio (up to 3.2). The fuel consists of real mixed plastic waste coming from separate collection of municipal solid wastes. The data obtained are used to investigate in depth the role of the main operating parameters and to improve and validate a recently developed one-dimensional kinetic model for waste gasification. The validation shows a good agreement between experimental data and model results, suggesting the reliability of the model to predict the reactor behavior under conditions of pure steam-oxygen gasification, relevant to many industrial applications. It has been found that the equivalence ratio is the parameter that most affects the syngas composition. At a constant equivalent ratio, the molar fraction of oxygen in the enriched air shows a limited influence on syngas composition while the steam is crucial in controlling the temperature along the reactor. Provided that the steam-to-carbon molar ratio is larger than 1.5, steam affects mainly the reactor temperature rather than the syngas composition, qualifying the steam-to-oxygen molar ratio as an instrumental parameter for smooth plant operation.

Janus Membrane-Based Wearable pH Sensor with Sweat Absorption, Gas Permeability, and Self-Adhesiveness.

Wearable biomedical sensors have enabled noninvasive and continuous physiological monitoring for daily health management and early detection of chronic diseases. Among biomedical sensors, wearable pH sensors attracted significant interest, as pH influences most biological reactions. However, conformable pH sensors that have sweat absorption ability, are self-adhesive to the skin, and are gas permeable remain largely unexplored. In this study, we present a pioneering approach to this problem by developing a Janus membrane-based pH sensor with self-adhesiveness on the skin. The sensor is composed of a hydrophobic polyurethane-polydimethylsiloxane porous hundreds nanometer-thick substrate and a hydrophilic poly(vinyl alcohol)-poly(acrylic acid) porous nanofiber layer. This Janus membrane exhibits a thickness of around 10 µm, providing a conformable adhesion to the skin. The simultaneous realization of solution absorption, gas permeability, and self-adhesiveness makes it suitable for long-term continuous monitoring without compromising the comfort of the wearer. The pH sensor was tested successfully for continuous monitoring for 7.5 h, demonstrating its potential for stable analysis of skin health conditions. The Janus membrane-based pH sensor holds significant promise for comprehensive skin health monitoring and wearable biomedical applications.

Research on high performance combustible gas concentration sensor based on pyramid beam splitter matrix.

Combustible gas concentration detection faces challenges of increasing accuracy, and sensitivity, as well as high reliability in harsh using environments. The special design of the optical path structure of the sensitive element provides an opportunity to improve combustible gas concentration detection. In this study, the optical path structure of the sensitive element was newly designed based on the Pyramidal beam splitter matrix. The infrared light source was modulated by multi-frequency point signal superimposed modulation technology. At the same time, concentration detection results and confidence levels were calculated using the 4-channel combustible gas concentration detection algorithm based on spectral refinement. Through experiment, it is found that the sensor enables full-range measurement of CH4, at the lower explosive limit (LEL, CH4 LEL of 5%), the reliability level is 0.01 parts-per-million (PPM), and the sensor sensitivity is up to 0.5PPM. The sensor is still capable of achieving PPM-level detections, under extreme conditions in which the sensor's optical window is covered by 2/3, and humidity is 85% or dust concentration is 100mg/m3. Those improve the sensitivity, robustness, reliability, and accuracy of the sensor.

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors.

Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Various combinations of them can be expected and gas-oil-water is one of the most common flows. Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utilized to ascertain the volumetric proportion of each phase. Sensors based on measuring capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for measuring in the process. Besides, at the present moment, Artificial intelligence (AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, stratified, and homogeneous. In this paper, volume fractions in a gas-oil-water three-phase homogeneous regime are measured. To accomplish this objective, an Artificial Neural Network (ANN) and a capacitance-based sensor are utilized. To train the presented network, an optimized sensor was implemented in the COMSOL Multiphysics software and after doing a lot of simulations, 231 different data are produced. Among all obtained results, 70 percent of them (161 data) are awarded to the train data, and the rest of them (70 data) are considered for the test data. This investigation proposes a new intelligent metering system based on the Multilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid's water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method's considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be considered for future works. Besides, temperature and pressure changes which highly temper relative permittivity and density of the liquid inside the pipe can be considered for another future idea.

Characterization and health risks of short- and medium-chain chlorinated paraffins in the gas and size-fractionated particulate phases in ambient air.

Short-chain and medium-chain chlorinated paraffins (SCCPs and MCCPs) have garnered significant attention because they have persistence and potential toxicity, and can undergo long-distance transport. Chlorinated paraffins (CPs) inhaled in the size-fractionated particulate phase and gas phase can carry different risks to human health due to their ability to accumulate in different regions of the respiratory tract and exhibit varying deposition efficiencies. In our study, large-volume ambient air samples in both the size-fractionated particulate phase (Dp < 1.0 µm, 1.0-2.5 µm, 2.5-10 µm, and Dp ≥ 10 µm) and gas phase were collected simultaneously in Beijing using an active sampler. The overall levels of SCCPs and MCCPs were relatively high, the ranges being 57-881 and 30-385 ng/m3, respectively. SCCPs tended to be partitioned in the gas phase (on average 75% of the ΣSCCP concentration), while MCCPs tended to be partitioned in the particulate phase (on average 62% of the ΣMCCP concentration). Significant correlations were discovered between the logarithm-transformed gas-particle partition coefficients (KP) and predicted subcooled vapor pressures (PL0) (p < 0.01 for SCCPs and MCCPs) and between the logarithm-transformed KP values and octanol-air partition coefficients (KOA) (p < 0.01 for SCCPs and MCCPs). Thus, the slopes indicated that organic matter absorption was the dominant process involved in gas-particle partitioning. We used the ICRP model to calculate deposition concentrations for particulate-associated CPs in head airways region (15.6-71.4 ng/m³), tracheobronchial region (0.8-4.8 ng/m³), and alveolar region (5.1-21.9 ng/m³), then combined these concentrations with the CP concentrations in the gas phase to calculate estimated daily intakes (EDIs) for inhalation. The EDIs for SCCPs and MCCPs through inhalation of ambient air for the all-ages group were 67.5-184.2 ng/kg/day and 19.7-53.7 ng/kg/day, respectively. The results indicated that SCCPs and MCCPs in ambient air do not currently pose strong risks to human health in the study area.

Ultrathin Boundary-Less SnO2 Films with Surface-Activated Two-Dimensional Nanograins Enable Fast and Sensitive Hydrogen Gas Sensing.

Fast and reliable semiconductor hydrogen sensors are crucially important for the large-scale utilization of hydrogen energy. One major challenge that hinders their practical application is the elevated temperature required, arising from undesirable surface passivation and grain-boundary-dominated electron transportation in the conventional nanocrystalline sensing layers. To address this long-standing issue, in the present work, we report a class of highly reactive and boundary-less ultrathin SnO2 films, which are fabricated by the topochemical transformation of 2D SnO transferred from liquid Sn-Bi droplets. The ultrathin SnO2 films are purposely made to consist of well-crystallized quasi-2D nanograins with in-plane grain sizes going beyond 30 nm, whereby the hydroxyl adsorption and grain boundary side-effects are effectively suppressed, giving rise to an activated (101)-dominating dangling-bond surface and a surface-controlled electrical transportation with an exceptional electron mobility of 209 cm2 V-1 s-1. Our work provides a new cost-effective strategy to disruptively improve the gas reception and transduction of SnO2. The proposed chemiresistive sensors exhibit fast, sensitive, and selective hydrogen sensing performance at a much-reduced working temperature of 60 °C. The remarkable sensing performance as well as the simple and scalable fabrication process of the ultrathin SnO2 films render the thus-developed sensors attractive for long awaited practical applications in hydrogen-related industries.

Unrecognized extensive charge of microbial gas in the Junggar basin.

Different from the Qaidam basin with about 320 billion m3 microbial gas, only limited microbial gases were found from the Junggar basin with similarly abundant type III kerogen. To determine whether microbial gases have not yet identified, natural gas samples from the Carboniferous to Cretaceous in the Junggar basin have been analyzed for chemical and stable isotope compositions. The results reveal some of the gases from the Mahu sag, Zhongguai, Luliang and Wu-Xia areas in the basin may have mixed with microbial gas leading to straight ethane to butane trends with a "dogleg" light methane in the Chung's plot. Primary microbial gas from degradation of immature sedimentary organic matter is found to occur in the Mahu sag and secondary microbial gas from biodegradation of oils and propane occurred in the Zhongguai, Luliang and Beisantai areas where the associated oils were biodegraded to produce calcites with δ13C values from + 22.10‰ to + 22.16‰ or propane was biodegraded leading to its 13C enrichment. Microbial CH4 in the Mahu sag is most likely to have migrated up from the Lower Wuerhe Formation coal-bearing strata by the end of the Triassic, and secondary microbial gas in Zhongguai and Beisantan uplifts may have generated after the reservoirs were uplifted during the period of the Middle and Late Jurassic. This study suggests widespread distribution of microbial gas and shows the potential to find large microbial gas accumulation in the basin.

Dimethyl sulfoxide as a gas phase charge-reducing agent for the determination of PEGylated proteins' intact mass.

Determination of PEGylated proteins' intact mass by mass spectrometry is challenging due to the molecules' large size, excessive charges, and instrument limitations. Previous efforts have been reported. However, signal variability, ion coalescence, and a generally low degree of robustness have been observed. In this work, we have explored the capabilities of post-column infusion of dimethyl sulfoxide (DMSO) following reversed-phase liquid chromatography-mass spectrometry (RP-LCMS) to determine PEG-filgrastim' intact mass, and to characterize its PEG moiety. The method was optimized around reproducibility (six preparations, and three injection replicates) with an in-house prepared PEG-filgrastim standard. The method showed a mass accuracy of ≤1.2 Da. The average molecular weight (MWEO=483) was 40 147.9 Da. The number average molecular weight (Mn) and the weight average molecular weight (Mw) were observed to be 40 101.1 and 40 113.9 Da, respectively, both with an RSD of 0.03%. The molecular weight distribution of ethylene oxide (EO), the polydispersity index (PDI), was 1.0003 for all preparations with a minimum and maximum number of EO units of 448 ± 2 and 516 ± 2, respectively. The method was finally applied to commercially available Neulasta® lots where the Mn and Mw were 39 995.8 and 40 008.8 Da, respectively, both with an RSD of 0.1%. The minimum and maximum EO units across the lots were observed to be 444.5 ± 1.5 and 514 ± 3, respectively. The PDI for all Neulasta® lots was 1.0003. This study provides an insightful characterization of Neulasta® and describes a robust LC-MS methodology for the characterization of the PEGylated proteins.

Modeling Nonlinear Dynamics of Functionalization Layers: Enhancing Gas Sensor Sensitivity for Piezoelectrically Driven Microcantilever.

This article presents a parametrized response model that enhances the limit of detection (LOD) of piezoelectrically driven microcantilever (PD-MC) based gas sensors by accounting for the adsorption-induced variations in elastic properties of the functionalization layer (binder) and the nonlinear motional dynamics of the PD-MC. The developed model is demonstrated for quantifying cadaverine, a volatile biogenic diamine whose concentration is used to assess the freshness of meat. At low concentrations of cadaverine, an increase in the resonance frequency is observed, contrary to the expected reduction due to mass added by adsorption. The study explores the variations in the elastic modulus vis-à-vis the adsorbed mass of cadaverine and derives the resonance frequency to the adsorbed mass response function. We advance a blended technique involving the analysis of atomic force microscopy (AFM) force-distance (f-d) curves and fitting of the quartz crystal microbalance (QCM) impedance response spectrum to deduce the adsorption-induced changes in the viscoelastic properties of the functionalization layer. The findings obtained are subsequently employed in modeling the response function for a structurally nonhomogenous PD-MC, highlighting the significance of the functionalization layer to the global elastic properties. The structural composition of the PD-MC beam adopted herein features a trapezoidal base hosting the actuating piezoelectric stratum and a rectangular free end with a functionalization layer. The Euler-Bernoulli beam theory coupled with Hamilton's principle is used to develop the equation of motion, which is subsequently discretized into a set of nonlinear ordinary differential equations via Galerkin expansion, and the solutions to the first fundamental mode of vibration are determined using the method of multiple scales. The obtained solutions provide a basis for deducing the nonlinear response function model to the adsorbed mass. The derived model is validated by recorded resonance frequency changes resulting from exposure to known concentrations of cadaverine. We demonstrate that the increase in resonance frequency for low concentrations of cadaverine is due to the dominance of the variation of the elastic modulus of the functionalization layer originating from the initial binder-analyte interactions over damping due to added mass. It is concluded that the developed nonlinear response function model can reliably be used to quantify the cadaverine concentration at low concentrations with an elevated Limit of Detection.

Fast-Response and Low-Power Self-Heating Gas Sensor Using Metal/Metal Oxide/Metal (MMOM) Structured Nanowires.

With the escalating global awareness of air quality management, the need for continuous and reliable monitoring of toxic gases by using low-power operating systems has become increasingly important. One of which, semiconductor metal oxide gas sensors have received great attention due to their high/fast response and simple working mechanism. More specifically, self-heating metal oxide gas sensors, wherein direct thermal activation in the sensing material, have been sought for their low power-consuming characteristics. However, previous works have neglected to address the temperature distribution within the sensing material, resulting in inefficient gas response and prolonged response/recovery times, particularly due to the low-temperature regions. Here, we present a unique metal/metal oxide/metal (MMOM) nanowire architecture that conductively confines heat to the sensing material, achieving high uniformity in the temperature distribution. The proposed structure enables uniform thermal activation within the sensing material, allowing the sensor to efficiently react with the toxic gas. As a result, the proposed MMOM gas sensor showed significantly enhanced gas response (from 6.7 to 20.1% at 30 ppm), response time (from 195 to 17 s at 30 ppm), and limit of detection (∼1 ppm) when compared to those of conventional single-material structures upon exposure to carbon monoxide. Furthermore, the proposed work demonstrated low power consumption (2.36 mW) and high thermal durability (1500 on/off cycles), demonstrating its potential for practical applications in reliable and low-power operating gas sensor systems. These results propose a new paradigm for power-efficient and robust self-heating metal oxide gas sensors with potential implications for other fields requiring thermal engineering.

MXene/Hydrogel-based bioelectronic nose for the direct evaluation of food spoilage in both liquid and gas-phase environments.

Various bioelectronic noses have been recently developed for mimicking human olfactory systems. However, achieving direct monitoring of gas-phase molecules remains a challenge for the development of bioelectronic noses due to the instability of receptor and the limitations of its surrounding microenvironment. Here, we report a MXene/hydrogel-based bioelectronic nose for the sensitive detection of liquid and gaseous hexanal, a signature odorant from spoiled food. In this study, a conducting MXene/hydrogel structure was formed on a sensor via physical adsorption. Then, canine olfactory receptor 5269-embedded nanodiscs (cfOR5269NDs) which could selectively recognize hexanal molecules were embedded in the three-dimensional (3D) MXene/hydrogel structures using glutaraldehyde as a linker. Our MXene/hydrogel-based bioelectronic nose exhibited a high selectivity and sensitivity for monitoring hexanal in both liquid and gas phases. The bioelectronic noses could sensitively detect liquid and gaseous hexanal down to 10-18 M and 6.9 ppm, and they had wide detection ranges of 10-18 - 10-6 M and 6.9-32.9 ppm, respectively. Moreover, our bioelectronic nose allowed us to monitor hexanal levels in fish and milk. In this respect, our MXene/hydrogel-based bioelectronic nose could be a practical strategy for versatile applications such as food spoilage assessments in both liquid and gaseous systems.

Thermodynamically and Kinetically Enhanced Benzene Vapor Sensor Based on the Cu-TCPP-Cu MOF with Extremely Low Limit of Detection.

As a carcinogenic and highly neurotoxic hazardous gas, benzene vapor is particularly difficult to be distinguished in BTEX (benzene, toluene, ethylbenzene, xylene) atmosphere and be detected in low concentrations due to its chemical inertness. Herein, we develop a depth-related pore structure in Cu-TCPP-Cu to thermodynamically and kinetically enhance the adsorption of benzene vapor and realize the detection of ultralow-temperature benzene gas. We find that the in-plane π electronic nature and proper pore sizes in Cu-TCPP-Cu can selectively induce the adsorption and diffusion of BTEX. Interestingly, the theoretical calculations (including density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations) exhibit that benzene molecules are preferred to adsorb and array as a consecutive arrangement mode in the Cu-TCPP-Cu pore, while the TEX (toluene, ethylbenzene, xylene) dominate the jumping arrangement model. The differences in distribution behaviors can allow adsorption and diffusion of more benzene molecules within limited room. Furthermore, the optimal pore-depth range (60-65 nm) of Cu-TCPP-Cu allows more exposure of active sites and hinders the gas-blocking process. The optimized sensor exhibits ultrahigh sensitivity to benzene vapor (155 Hz/µg@1 ppm), fast response time (less than 10 s), extremely low limit of detection (65 ppb), and excellent selectivity (83%). Our research thus provides a fundamental understanding to design and optimize two-dimensional metal-organic framework (MOF)-based gas sensors.

Chemiresistive Sensor for Enhanced CO2 Gas Monitoring.

Carbon dioxide (CO2) gas sensing and monitoring have gained prominence for applications such as smart food packaging, environmental monitoring of greenhouse gases, and medical diagnostic tests. Although CO2 sensors based on metal oxide semiconductors are readily available, they often suffer from limitations such as high operating temperatures (>250 °C), limited response at elevated humidity levels (>60% RH), bulkiness, and limited selectivity. In this study, we designed a chemiresistive sensor for CO2 detection to overcome these problems. The sensing material of this sensor consists of a CO2 switchable polymer based on N-3-(dimethylamino)propyl methacrylamide (DMAPMAm) and methoxyethyl methacrylate (MEMA) [P(D-co-M)], and diethylamine. The designed sensor has a detection range for CO2 between 103 and 106 ppm even at high humidity levels (>80% RH), and it is capable of differentiating ammonia at low concentrations (0.1-5 ppm) from CO2. The addition of diethylamine improved sensor performance such as selectivity, response/recovery time, and long-term stability. These data demonstrate the potential of using this sensor for the detection of food spoilage.

Up-bottom assessments of nutrient supply and gaseous pollutant from Chinese wheat straw field management.

To achieve resource efficiency, and carbon neutrality, it is vital to evaluate nutrient supply and gaseous pollutant emissions associated with field management of bio-straw resources. Previous straw yield estimates have typically relied on a constant grain-to-straw yield ratio without accounting for grain yield levels in a given region. Addressing this high-resolution data gap, our study introduces a novel empirical model for quantifying grain-to-straw yield, which has been used to gauge wheat straw field management practices at the city level during 2011-2015. Utilizing both statistical review and GIS-based methods, average nitrogen (N), phosphorus (P), and potassium (K) supplies from straw field management stood at 1510, 1229, and 61700 tons, respectively. Average emissions of PM2.5, SO2, NOx, NH3, CH4, and CO2 due to straw burning were 367, 41, 160, 18, 165, and 70,644 tons, respectively. We also reported uncertainty from Monte Carlo model as the 5th-95th percentiles of estimated nutrient supply and gaseous pollutant. These insights will provide foundational support for the sustainable and environmentally friendly management of wheat straw in China.

Wireless Gas Sensor Based on the Mesoporous ZnO-SnO2 Heterostructure Enables Ultrasensitive and Rapid Detection of 3-Methylbutyraldehyde.

Achieving ultrasensitive and rapid detection of 3-methylbutyraldehyde is crucial for monitoring chemical intermediate leakage in pharmaceutical and chemical industries as well as diagnosing ventilator-associated pneumonia by monitoring exhaled gas. However, developing a sensitive and rapid method for detecting 3-methylbutyraldehyde poses challenges. Herein, a wireless chemiresistive gas sensor based on a mesoporous ZnO-SnO2 heterostructure is fabricated to enable the ultrasensitive and rapid detection of 3-methylbutyraldehyde for the first time. The mesoporous ZnO-SnO2 heterostructure exhibits a uniform spherical shape (∼79 nm in diameter), a high specific surface area (54.8 m2 g-1), a small crystal size (∼4 nm), and a large pore size (6.7 nm). The gas sensor demonstrates high response (18.98@20 ppm), short response/recovery times (13/13 s), and a low detection limit (0.48 ppm) toward 3-methylbutyraldehyde. Furthermore, a real-time monitoring system is developed utilizing microelectromechanical systems gas sensors. The modification of amorphous ZnO on the mesoporous SnO2 pore wall can effectively increase the chemisorbed oxygen content and the thickness of the electron depletion layer at the gas-solid interface, which facilitates the interface redox reaction and enhances the sensing performance. This work presents an initial example of semiconductor metal oxide gas sensors for efficient detection of 3-methylbutyraldehyde that holds great potential for ensuring safety during chemical production and disease diagnosis.

General Model for Predicting Response of Gas-Sensitive Materials to Target Gas Based on Machine Learning.

Gas sensors play a crucial role in various industries and applications. In recent years, there has been an increasing demand for gas sensors in society. However, the current method for screening gas-sensitive materials is time-, energy-, and cost-consuming. Consequently, an imperative exists to enhance the screening efficiency. In this study, we proposed a collaborative screening strategy through integration of density functional theory and machine learning. Taking zinc oxide (ZnO) as an example, the responsiveness of ZnO to the target gas was determined quickly on the basis of the changes in the electronic state and structure before and after gas adsorption. In this work, the adsorption energy and electronic and structural characteristics of ZnO after adsorbing 24 kinds of gases were calculated. These computed features served as the basis for training a machine learning model. Subsequently, various machine learning and evaluation algorithms were utilized to train the fast screening model. The importance of feature values was evaluated by the AdaBoost, Random Forest, and Extra Trees models. Specifically, charge transfer was assigned importance values of 0.160, 0.127, and 0.122, respectively, ranking as the highest among the 11 features. Following closely was the d-band center, which was presumed to exert influence on electrical conductivity and, consequently, adsorption properties. With 5-fold cross-validation using the Extra Tree accuracy, the 24-sample data set achieved an accuracy of 88%. The 72-sample data set achieved an accuracy of 78% using multilayer perceptron after 5-fold cross-validation, with both data sets exhibiting low standard deviations. This verified the accuracy and reliability of the strategy, showcasing its potential for rapidly screening a material's responsiveness to the target gas.

Synthesis of lignin-based carbon/graphene oxide foam and its application as sensors for ammonia gas detection.

The present study highlights the integration of lignin with graphene oxide (GO) and its reduced form (rGO) as a significant advancement within the bio-based products industry. Lignin-phenol-formaldehyde (LPF) resin is used as a carbon source in polyurethane foams, with the addition of 1 %, 2 %, and 4 % of GO and rGO to produce carbon structures thus producing carbon foams (CFs). Two conversion routes are assessed: (i) direct addition with rGO solution, and (ii) GO reduction by heat treatment. Carbon foams are characterized by thermal, structural, and morphological analysis, alongside an assessment of their electrochemical behavior. The thermal decomposition of samples with GO is like those having rGO, indicating the effective removal of oxygen groups in GO by carbonization. The addition of GO and rGO significantly improved the electrochemical properties of CF, with the GO2% sensors displaying 39 % and 62 % larger electroactive area than control and rGO2% sensors, respectively. Furthermore, there is a significant electron transfer improvement in GO sensors, demonstrating a promising potential for ammonia detection. Detailed structural and performance analysis highlights the significant enhancement in electrochemical properties, paving the way for the development of advanced sensors for gas detection, particularly ammonia, with the prospective market demands for durable, simple, cost-effective, and efficient devices.