Unknown Gas Research Articles

Machine learning-motivated trace triethylamine identification by bismuth vanadate/tungsten oxide heterostructures.

Triethylamine, an extensively used material in industrial organic synthesis, is hazardous to the human respiratory and nervous systems, but its accurate detection and prediction has been a long-standing challenge. Herein, a machine learning-motivated chemiresistive sensor that can predict ppm-level triethylamine is designed. The zero-dimensional (0D) bismuth vanadate (BiVO4) nanoparticles were anchored on the surface of three-dimensional (3D) tungsten oxide (WO3) architectures to form hierarchical BiVO4/WO3 heterostructures, which demonstrates remarkable triethylamine-sensing performance such as high response of 21 (4 times higher than pristine WO3) at optimal temperature of 190°C, low detection limit of 57ppb, long-term stability, reproducibility and good anti-interference property. Furthermore, an intelligent framework with good visibility was developed to identify ppm-level triethylamine and predict its definite concentration. Using feature parameters extracted from the sensor responses, the machine learning-based classifier provides a decision boundary with 92.3% accuracy, and the prediction of unknown gas concentration was successfully achieved by linear regression model after training a series of as-known concentrations. This work not only provides a fundamental understanding of BiVO4-based heterostructures in gas sensors but also offers an intelligent strategy to identify and predict trace triethylamine under an interfering atmosphere.

Gas classification and concentration prediction in open environments using class anchor clustering-initialized temporal convolutional network

Identifying target gases and predicting their concentrations in open environments are critical tasks, particularly due to fluctuating environmental conditions and the presence of unknown gases. This study aims to address these challenges by developing a model that improves both gas classification and concentration prediction. We introduce two key components: Quantile-Dynamic Associated Features (Q-DAF) and the Class Anchor Clustering-Initialized Temporal Convolutional Network (CAC-InitTCN). Q-DAF dynamically enhances gas signal feature extraction, optimizing the ability of CAC-InitTCN to handle complex and changing gas environments. CAC-InitTCN is designed to efficiently process time-series data, allowing it to manage gas concentration fluctuations while mitigating the interference of unknown gases. CAC-InitTCN was evaluated on two distinct datasets, achieving classification accuracy rates of 93.62 % and 98.05 %, respectively. In gas concentration prediction tasks, the model demonstrated RMSE values of 0.2102 and 0.0392, and R2 scores of 0.9085 and 0.9784, after data normalization. These results demonstrate the capability of CAC-InitTCN in handling real-time gas fluctuations and recognizing unknown gases, making it a promising approach for open-environment gas detection.

Multiarray Gas Sensors Using Ternary Combined Ti3C2Tx MXene-Based Nanocomposites.

This paper reports chemiresistive multiarray gas sensors through the synthesized ternary nanocomposites, using a one-pot method to integrate two-dimensional MXene (Ti3C2Tx) with Ti-doped WO3 (Ti-WO3/Ti3C2Tx) and Ti3C2Tx with Pd-doped SnO2 (Pd-SnO2/Ti3C2Tx). The gas sensors based on Ti-WO3/Ti3C2Tx and Pd-SnO2/Ti3C2Tx exhibit exceptional sensitivity, particularly in detecting 70% at 1 ppm acetone and 91.1% at 1 ppm of H2S. Notably, our sensors demonstrate a remarkable sensing performance in the low-ppb range for acetone and H2S. Specifically, the Ti-WO3/Ti3C2Tx sensor demonstrates a detection limit of 0.035 ppb for acetone, and the Pd-SnO2/Ti3C2Tx sensor shows 0.116 ppb for H2S. Simultaneous measurements with Ti-WO3/Ti3C2Tx- and Pd-SnO2/Ti3C2Tx-based sensors enable the evaluation of both the concentration and type of unknown target gases, such as acetone or H2S. Furthermore, density functional theory calculations are performed to clarify the role of Ti and Pd doping in enhancing the performance of Ti-WO3/Ti3C2Tx and Pd-SnO2/Ti3C2Tx nanocomposites. Theoretical simulations contribute to a deeper understanding of the doping effects, providing essential insights into the mechanisms underlying the enhanced gas response of the gas sensors. Overall, this work provides valuable insights into the gas-sensing mechanisms and introduces a novel approach for high-performance multiarray gas sensing.

Open-set adversarial domain match for electronic nose drift compensation and unknown gas recognition

In practical applications, electronic noses (EN) must tackle not only sensor drift but also resist interference from unknown gases. In prolonged open environments, electronic noses often encounter unknown gases that cannot be predicted in advance. Current gas detection methods typically rely on prior knowledge of target analytes but struggle to comprehensively address sensor drift and interference from unknown gases. Such as indoor air quality monitoring, long-term stability is crucial, and not all relevant analytes can be predetermined. However, the recent research cannot solve both the sensor drift and unknown gas intrusion problem simultaneously well. In this work, we unify above problems in to the open-set risk boundary. We propose an open-set adversarial domain match (OSADM) model and introduce the considers of open-set domain adaptation (OSDA). OSDA trains a target classifier through matching the domain distribution to recognize the known and unknown gases. First, a binary adversarial loss divides the class boundary. Secondly, adversarial domain adaptation unifies the distribution of different domains. Compared with the metric methods, it avoids complex distribution computation and parameter adjustment to reduce negative transfer. Extensive experimental results on two benchmark datasets, Gas Sensor Array Drift and Twin gas sensor arrays Dataset show that OSADM outperformance of the existing open-set models.

Pulmonary function testing in preoperative high-risk patients

BackgroundPostoperative respiratory failure is the most frequent complication in postsurgical patients. The purpose of this study is to assess whether pulmonary function testing in high-risk patients during preoperative assessment detects previously unknown respiratory impairments which may influence patient outcomes.MethodsA targeted patient screening by spirometry and the measurement of the diffusing capacity of the lung for carbon monoxide (DLCO) was implemented in the anesthesia department of a tertiary university hospital. Patients of all surgical disciplines who were at least 75 years old or exhibited reduced exercise tolerance with the metabolic equivalent of task less than four (MET < 4) were examined. Clinical characteristics, history of lung diseases, and smoking status were also recorded. The statistical analysis entailed t-tests, one-way ANOVA, and multiple linear regression with backward elimination for group comparisons.ResultsAmong 256 included patients, 230 fulfilled the test quality criteria. Eighty-one (35.2%) patients presented obstructive ventilatory disorders, out of which 65 were previously unknown. 38 of the newly diagnosed obstructive disorders were mild, 18 moderate, and 9 severe. One hundred forty-five DLCO measurements revealed 40 (27.6%) previously unknown gas exchange impairments; 21 were mild, 17 moderate, and 2 severe. The pulmonary function parameters of forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and DLCO were significantly lower than the international reference values of a healthy population. Patients with a lower ASA class and no history of smoking exhibited higher FVC, FEV1, and DLCO values. Reduced exercise tolerance with MET < 4 was strongly associated with lower spirometry values.ConclusionsOur screening program detected a relevant number of patients with previously unknown obstructive ventilatory disorders and impaired pulmonary gas exchange. This newly discovered sickness is associated with low metabolic equivalents and may influence perioperative outcomes. Whether optimized management of patients with previously unknown impaired lung function leads to a better outcome should be evaluated in multicenter studies.Trial registrationGerman Registry of Clinical Studies (DRKS00029337), registered on: June 22nd, 2022.

Enhanced hydrogen gas sensing characteristics of CuO/ZnO heterojunction for thickness-dependence and its application

In this study, we experimentally verified the synthesis of ZnO and CuO thin films using a droplet-based hydrothermal method and their hydrogen sensing performance. The hydrogen gas detection performance of the fabricated heterostructured gas sensor was evaluated based on the results of an experiment conducted while controlling the temperature and hydrogen concentration in the gas chamber. According to the responses of single metal oxide sensors made of each ZnO and CuO and their heterojunction sensors, CuO deposited with 7 cycles on 5 cycles of ZnO had a resistance change rate of about 164 % and the change of the resistance took 3 s in the environment of 4 % hydrogen at 300 °C. This sensor showed the highest response rate and fastest reaction time out of all. CuO/ZnO showed the highest reaction at 300 °C, confirming its selective reaction to hydrogen. To solve the problem of metal oxide-based gas sensors with low selectivity, the operating principle of the heterojunction between metal oxide that has selective gas reactivity was analyzed and experimentally verified. Additionally, the fabricated sensor was confirmed to have selective responsiveness to different gases at various temperatures. Using these characteristics, it was confirmed that this heterostructure based sensor can be used as a sensor array for electronic nose that could classify unknown gases using the same sensor at various temperatures.

Regression-enhanced Entrotaxis as an autonomous search algorithm for seeking an unknown gas leakage source

Rapid localization of hazardous gas leak sources is a vital task regarding the security of human society. Over the past few decades, autonomous search using mobile sensing devices equipped with gas sensors has become a promising field. Cognitive search strategies, such as Infotaxis and Entrotaxis, have been successfully used to search an unknown source autonomously in turbulence. However, they are time-consuming and may not be efficient because all source parameters (locations and strength) are estimated via particle filtering, especially when the prior information of source strength is rarely known. To address this inefficiency, this paper proposes a hybrid autonomous source searching approach, named as regression-enhanced Entrotaxis, which incorporates the Entrotaxis algorithm with the regression methods. Only the position of leak source is inferred by particle filtering, while the strength is estimated by the least squares based on the historical measurements. Through this way, the searching space in particle filtering is converted from 3-dimensions (source position in x–y plane and source strength) to 2-dimensions (source position in x–y plane), speeding up the computation significantly. Then, the reward function according to maximum entropy sampling principles is utilized to guide the robot’s next move. The performance of the proposed method is compared to that of the Entrotaxis algorithm. The results of Monte Carlo simulations demonstrate that fewer particles will be used to reach a specific success rate, saving time in the search process. Besides, the proposed strategy relies on less prior information regarding source strength, which is more in line with the actual scenario. In addition, the proposed approach is adaptable to a broader search region.

A volatile fatty acids adaptive observer-based hierarchical optimal controller design to maximum gas production of two-stage anaerobic digestion process

Volatile fatty acids (VFA), which influence the stability and gas production, are important state variables in anaerobic digestion (AD) process. Three main components of VFA can indicate the metabolic state of AD internal environment and be considered as a better basis for AD monitoring strategies. A VFA adaptive observer (VFA-AO) is proposed to estimate unknown gas production kinetic parameters and unmeasurable states simultaneously, including the concentration of three main components of VFA. Otherwise, using the estimated information, a VFA-AO-based hierarchical optimal controller (VFA-AO-based HOC) is designed to maximize gas production rates of AD. The proposed controller is compared with two existing control algorithms. Simulation results show that, despite the concentration of pollutants in the inlet fluctuates, VFA-AO achieves a reliable states and kinetic parameters estimation, and VFA-AO-based HOC maximizes gas production.

Investigation on the combined model of sensor drift compensation and open-set gas recognition based on electronic nose datasets

Electronic nose (EN) is widely used to identify and detect gases. However, ENs are inevitably subject to sensor drift and the intrusion of unknown gases during prolonged exposure to the environment. A combined model of sensor drift compensation and open-set gas recognition, class anchoring and contrasting semantic alignment (CACSA) was proposed and tested on two commonly used EN drift datasets. Firstly, the model learns an embedding subspace that is discriminative, where mapped domains are semantically aligned and maximally separated. In addition, the method requires a very small number of labeled target training samples, even if one per class can be effective. Then, our model learns a fixed anchor for each known class. Known classes form tight clusters around the centers of class dependencies anchored in the feature space. CACSA can be degenerated into a closed-set drift compensation model without sacrificing accuracy and not require the additional target domain sample labeling and repeated training for open-set recognition. According to the experimental result, our model showed better drift compensation performance, and achieved the highest open-set recognition accuracy.

Machine Learning-Assisted Sensor Based on CsPbBr3@ZnO Nanocrystals for Identifying Methanol in Mixed Environments.

Methanol is a respiratory biomarker for pulmonary diseases, including COVID-19, and is a common chemical that may harm people if they are accidentally exposed to it. It is significant to effectively identify methanol in complex environments, yet few sensors can do so. In this work, the strategy of coating perovskites with metal oxides is proposed to synthesize core-shell CsPbBr3@ZnO nanocrystals. The CsPbBr3@ZnO sensor displays a response/recovery time of 3.27/3.11 s to 10 ppm methanol at room temperature, with a detection limit of 1 ppm. Using machine learning algorithms, the sensor can effectively identify methanol from an unknown gas mixture with 94% accuracy. Meanwhile, density functional theory is used to reveal the formation process of the core-shell structure and the target gas identification mechanism. The strong adsorption between CsPbBr3 and the ligand zinc acetylacetonate lays the foundation for the formation of the core-shell structure. The crystal structure, density of states, and band structure were influenced by different gases, which results in different response/recovery behaviors and makes it possible to identify methanol from mixed environments. Furthermore, due to the formation of type II band alignment, the gas response performance of the sensor is further improved under UV light irradiation.

A Chemiresistor Sensor Array Based on Graphene Nanostructures: From the Detection of Ammonia and Possible Interfering VOCs to Chemometric Analysis.

Sensor arrays are currently attracting the interest of researchers due to their potential of overcoming the limitations of single sensors regarding selectivity, required by specific applications. Among the materials used to develop sensor arrays, graphene has not been so far extensively exploited, despite its remarkable sensing capability. Here we present the development of a graphene-based sensor array prepared by dropcasting nanostructure and nanocomposite graphene solution on interdigitated substrates, with the aim to investigate the capability of the array to discriminate several gases related to specific applications, including environmental monitoring, food quality tracking, and breathomics. This goal is achieved in two steps: at first the sensing properties of the array have been assessed through ammonia exposures, drawing the calibration curves, estimating the limit of detection, which has been found in the ppb range for all sensors, and investigating stability and sensitivity; then, after performing exposures to acetone, ethanol, 2-propanol, sodium hypochlorite, and water vapour, chemometric tools have been exploited to investigate the discrimination capability of the array, including principal component analysis (PCA), linear discriminant analysis (LDA), and Mahalanobis distance. PCA shows that the array was able to discriminate all the tested gases with an explained variance around 95%, while with an LDA approach the array can be trained to accurately recognize unknown gas contribution, with an accuracy higher than 94%.

Probing natural gas components with Raman integrating sphere technology.

Raman spectroscopy is a powerful method of probing natural gas components, but higher sensitivity, greater miniaturization, and lower cost techniques are required. Therefore, we designed a Raman integrating sphere-enhanced spectroscopy technology in a volume of 40 × 40 × 20 cm3 based on the principle of integrating sphere reflection. This technology consists of two parts: the first is an integrating sphere model to collect scattered signals, and the second is a right-angle light-boosting system to increase the optical path of the pump light in the sample. Raman integrating sphere technology has a detection limit of 0.5 ppm in the air with an exposure time of 600 s under room temperature and ambient pressure conditions. Experiments of natural gas detection display that the detection limits of ethane, propane, n-butane, isobutane, n-pentane, and isopentane are 28, 28, 95, 28, 189, and 95 ppm, respectively. In addition, there is a linear relationship between the relative Raman intensity and the concentration of each component in natural gas, which can be used as a probe for detecting unknown natural gas components in gas wells.

Simultaneous determination of the position, release time and mass release rate of an unknown gas emission source in short-term emissions by inverse problem

This article proposes a protocol for the simultaneous determination of the position, release time and mass release rate of an unknown gas emission source from experimental data in the form of an inverse problem, since these are the main variables in the short duration release of pollutants.Records of pollutant concentration and of measurement time (with their inherent errors) carried out at two measuring stations are the input data for the inverse problem. The actions of the protocol are divided into two well-differentiated stages. In the first, the simulations are started for each iteration, the functionals are calculated to program the next iteration, setting the new values of measurement time and the distance of the emission source in downwind direction with respect to the measuring stations by comparing the simulated and experimental values, and so on until reaching the final solution. In the second, an analogous procedure is followed until the mass rate and the emission source position is obtained. In addition, it has been necessary to define a new coefficient that relates the effect of dispersion in the measurement time, the distance in the downwind direction and the atmospheric stability categories.The reliability of the proposed protocol is checked by means of a problem whose parameters are known a priori. First, the direct problem is solved to obtain the values of contaminant concentration and the measurement time of the stations. These variables are then affected by random errors of up to 2% to provide the input data for the inverse problem. In all the examples shown in this work, solutions have been obtained that can be considered very successful in this field of engineering.

Study of nonlinear analysis methods for spectral detection of mixed gases

Data analytic methods are required in detecting gas concentrations using spectroscopic techniques, especially for absorption spectra of mixed gases. However the common analytic methods, such as multivariate linear regression, principal component regression etc. are limited to deal with non-linear and uncorrelated variables. In the case of multi-component gases, these methods are able to meet expected accuracy and reliability. In this paper, a simple and effective method is proposed.This method is based on spectral cases of single-component gases, it determines the concentration of each component using linear superposition principle of the spectra in Beer’s law. The experimental results show that the detection error of this method is less than 1% in the known gas mixture case and 3% in the unknown gas mixture case.

Quantification of gas concentrations in NO/NO2/C3H8/NH3 mixtures using machine learning

We employ machine learning to decode the composition of unknown gas mixtures from the output of an array of four electrochemical sensors. The sensors use metal oxide electrodes paired with a ceramic electrolyte, yttria-stabilized zirconia (YSZ), to produce voltage responses to the presence of gases in complex mixtures. The voltages from the sensor array serve as inputs to a machine learning pipeline which first carries out multi-class classification of mixtures into types based on which gases are present at non-zero concentrations, and subsequently predicts gas concentrations given the mixture type. Thus, our model is able to take a single reading from the sensor array in response to gas mixtures involving NO, NO2, C3H8, and NH3, and output a highly accurate prediction of which gases are present in the mixture, along with the concentrations of each constituent gas. Our computational framework can be easily expanded to include additional gases and additional mixture types, allowing it to be used in numerous automotive, industrial and environmental monitoring settings.

Low-cost prediction of molecular and transition state partition functions via machine learning.

We have generated an open-source dataset of over 30 000 organic chemistry gas phase partition functions. With this data, a machine learning deep neural network estimator was trained to predict partition functions of unknown organic chemistry gas phase transition states. This estimator only relies on reactant and product geometries and partition functions. A second machine learning deep neural network was trained to predict partition functions of chemical species from their geometry. Our models accurately predict the logarithm of test set partition functions with a maximum mean absolute error of 2.7%. Thus, this approach provides a means to reduce the cost of computing reaction rate constants ab initio. The models were also used to compute transition state theory reaction rate constant prefactors and the results were in quantitative agreement with the corresponding ab initio calculations with an accuracy of 98.3% on the log scale.

A Novel Gas Recognition and Concentration Estimation Model for an Artificial Olfactory System With a Gas Sensor Array

Traditional algorithms cannot readily address the fact that artificial olfaction in a dynamic ambient environment requires continuous selection and execution of the optimal algorithm to detect different gases. This paper presents a deep learning WCCNN-BiLSTM-many-to-many GRU (wavelet coefficient convolutional neural network–bidirectional long short-term memory–many-to-many-gated recurrent unit) model for qualitative and quantitative artificial olfaction of gas based on the automatic extraction of time-frequency domain dynamic features and time domain steady-state features. The model consists of two submodels. One submodel recognizes a gas by the WCCNN-BiLSTM model, and the experiments based on actual data from our fabricated artificial olfactory system demonstrate that the gas recognition accuracy is nearly 100%. The other submodel quantifies the gas by the many-to-many GRU model with less labeled data; this submodel is comparable to conventional algorithms such as DT (decision tree), SVMs (support vector machines), KNN (k-nearest neighbor), RF (random forest), AdaBoost, GBDT (gradient-boosting decision tree), bagging, and ET (extra tree) according to PCA (principal component analysis) dimensionality reduction. The experimental results of 10-fold cross-validations show that the proposed many-to-many GRU outperforms the aforementioned conventional algorithms with remarkable metrics and can maintain higher concentration estimation accuracy for different unknown gases with less labeled data.

ADAP-KDB: A Spectral Knowledgebase for Tracking and Prioritizing Unknown GC–MS Spectra in the NIH’s Metabolomics Data Repository

We report the development of a spectral knowledgebase named ADAP-KDB for tracking and prioritizing unknown gas chromatography-mass spectrometry (GC-MS) spectra in the NIH's Metabolomics Data Repository-a national and international repository for metabolomics data. ADAP-KDB consists of two parts. One part is a computational workflow that preprocesses raw mass spectrometry data and derives consensus mass spectra. The other part is a web portal for users to browse the consensus spectra and match query spectra against them. For each consensus spectrum, the Gini-Simpson diversity index and the p-value from χ2 goodness-of-fit test are calculated to measure its statistical significance, which enables prioritization of unknown mass spectra for subsequent costly compound identification.

Federated learning based atmospheric source term estimation in urban environments

Establishing an accurate and efficient source term estimation (STE) system is of great significance for identifying unknown gas leakage sources in urban environments. Many successful STE methods have been proposed, while most of them assume there is only one point source. However, the complicated urban atmospheric dispersion and the massive sensor data in distributed edge devices pose new challenges. To address these issues, this paper proposes a method to convert measured concentrations into visual features, which retains the characteristics of diffusion and the layout of obstacles. Then, a federated STE (FL-STE) framework is proposed to extract knowledge from local models collaboratively without collecting all privacy data, in which a deep neural network is used to recognize the relationship between visual features and source terms. Furthermore, we construct an urban dispersion dataset with multiple obstacles and sources by FDS simulation. Various empirical studies prove the efficiency of the proposed method.

Emission source tracing based on bionic algorithm mobile sensors with artificial olfactory system

AbstractThe leakage of hazardous chemicals and toxic volatile substances in the atmosphere may cause serious consequences such as explosion and poisoning. To identify the unknown leakage locations and gas compositions, a mobile robot system to trace the leak source in the outdoor was investigated. First, two bionic searching algorithms, Zigzag and Silkworm algorithms, were tested with outdoor experiments for locating the leak source. The results showed that the locating errors of these two algorithms were within 0.5 m in 10 by 20 m search space, but the failing ratio of Zigzag and Silkworm algorithm was still high (about 40–50%). Therefore, an improved tracing algorithm combining the Silkworm and Zigzag algorithm, called as zigzag–Silkworm algorithm, was proposed. Compared with Silkworm and Zigzag algorithms, zigzag–Silkworm algorithm had a higher success ratio of 80% in outdoor source tracing tests, and the searching efficiency was enhanced, the efficiency parameter L: L0 has improved from 2.58 for Silkworm and 2.66 for Zigzag to 2.17 for zigzag–Silkworm. Then, in order to identify the composition of the leaked gases during the source tracing, an artificial olfaction system (AOS) based on the gas sensor array and support vector machine was set on the mobile robot. The test results in the source tracing experiments with ammonia and ethanol emissions indicated that the recognition accuracy of emission gases reached to 99% with AOS equipped on the robot. Therefore, the mobile robot system equipped with the zigzag–Silkworm algorithm and the AOS is feasible to trace the leakage source and identify the emission composition in the outdoor leakage event with good performance in efficiency and accuracy although some underlying problems still need to be addressed in future work.