Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications.

The escalating development and improvement of gas sensing ability in industrial equipment, or "machine olfactory", propels the evolution of gas sensors toward enhanced sensitivity, selectivity, stability, power efficiency, cost-effectiveness, and longevity. Two-dimensional (2D) materials, distinguished by their atomic-thin profile, expansive specific surface area, remarkable mechanical strength, and surface tunability, hold significant potential for addressing the intricate challenges in gas sensing. However, a comprehensive review of 2D materials-based gas sensors for specific industrial applications is absent. This review delves into the recent advances in this field and highlights the potential applications in industrial machine olfaction. The main content encompasses industrial scenario characteristics, fundamental classification, enhancement methods, underlying mechanisms, and diverse gas sensing applications. Additionally, the challenges associated with transitioning 2D material gas sensors from laboratory development to industrialization and commercialization are addressed, and future-looking viewpoints on the evolution of next-generation intelligent gas sensory systems in the industrial sector are prospected.

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

Ultra-Flexible, Breathable, and Robust PAN/MWCNTs/PANI Nanofiber Networks for High-Performance Wearable Gas Sensor Application.

Wearable gas sensors have drawn great attention for potential applications in health monitoring, minienvironment detection, and advanced soft electronic noses. However, it still remains a great challenge to simultaneously achieve excellent flexibility, high sensitivity, robustness, and gas permeability, because of the inherent limitation of widely used traditional organic flexible substrates. Herein, an electrospinning polyacrylonitrile (PAN) nanofiber network was designed as a flexible substrate, on which an ultraflexible wearable gas sensor was prepared with in situ assembled polyaniline (PANI) and multiwalled carbon nanotubes (MWCNTs) as a sensitive layer. The unique nanofiber network and strong binding force between substrate and sensing materials endow the wearable gas sensor with excellent robustness, flexibility, and gas permeability. The wearable sensor can maintain stable NH3 sensing performance while sustaining extreme bending and stretching (50% of strain). The Young's modulus of wearable PAN/MWCNTs/PANI sensor is as low as 18.9 MPa, which is several orders of magnitude smaller than those of reported flexible sensors. The water vapor transmission rate of the sensor is 0.38 g/(cm2 24 h), which enables the wearing comfort of the sensor. Most importantly, due to the effective exposure of sensing sites as well as the heterostructure effect between MWCNTs and PANI, the sensor shows high sensitivity to NH3 at room temperature, and the theoretical limit of detection is as low as 300 ppb. This work provides a new avenue for the realization of reliable and high-performance wearable gas sensors.

A Strategy for Multigas Identification Using Multielectrical Parameters Extracted from a Single Carbon-Based Field-Effect Transistor Sensor.

Given the widespread utilization of gas sensors across various industries, the detection of diverse and complex target gases presents a significant challenge in designing sensors with multigas detection capability. Although constructing a sensor array with widely used chemiresistive gas sensors is one solution, it is difficult for a single chemiresistive gas sensor to simultaneously detect different gases, as it can only detect a single target gas. The intrinsic reason for this bottleneck is that chemiresistive gas sensors rely entirely on the resistivity as the unique parameter to evaluate the diverse gas sensing properties of sensors, such as sensitivity, selectivity, etc. Herein, a field-effect transistor (FET) with abundant electrical parameters is employed to prepare a gas sensor for the detection of a variety of gases. Semiconducting carbon nanotubes (CNTs) are selected as the channel material, which is modified by Pd nanoparticles to enhance the gas sensing properties of the sensors. By extracting various electrical parameters such as transconductance, threshold voltage, etc. from the transfer characteristic curves of FET, a correlation between multielectrical parameters and various gas detection information is established for subsequent data analysis. Through the utilization of the principal component analysis algorithm, the identification of six gases can be finally achieved by relying solely on a single carbon-based FET-type gas sensor. We hope our work can solve the bottleneck of multigas identification by a single sensor in principle and is expected to reduce the system complexity and cost caused by the design of sensor arrays, offering a valuable guidance for multigas identification technology.

Si, O-Codoped Carbonized Polymer Dots with High Chemiresistive Gas Sensing Performance at Room Temperature.

A new type of carbonized polymer dot was prepared by the one-step hydrothermal method of triethoxylsilane (TEOS) and citric acid (CA). The sensor made from carbonized polymer dots (CPDs) showed superior gas sensing performance toward ammonia at room temperature. The Si, O-codoped CPDs exhibited superior ammonia sensing performance at room temperature, including a low practical limit of detection (pLOD) of 1 ppm (Ra/Rg: 1.10, 1 ppm), short response/recovery time (30/36 s, 1 ppm), high humidity resistance (less than 5% undulation when changing relative humidity to 80 from 30%), high stability (less than 5% initial response undulation after 120 days), reliable repeatability, and high selectivity against other interferential gases. The gas sensing mechanism was investigated through control experiments and in situ FTIR, indicating that Si, O-codoping essentially improves the electron transfer capability of CPDs and synergistically dominates the superior ammonia sensing properties of the CPDs. This work presents a facile strategy for constructing novel high-performance, single-component carbonized polymer dots for gas sensing.

A cellulose nanocrystal-based dual response of photonic colors and fluorescence for sensitive benzene gas detection.

Benzene, as a common volatile organic compound, represents serious risk to human health and environment even at low level concentration. There is an urgent concern on visualized, sensitive and real time detection of benzene gases. Herein, by doping Fe3+ and graphene quantum dots (GQDs), a cellulose nanocrystal (CNC) chiral nematic film was designed with dual response of photonic colors and fluorescence to benzene gas. The chiral nematic CNC/Fe/GQDs film could respond to benzene gas changes by reversible motion. Moreover, chiral nematic film also displays reversible responsive to humidity changes. The resulting CNC/Fe/GQDs chiral nematic film showed excellent response performance at benzene gas concentrations of 0-250 mg/m3. The maximal reflection wavelength film red shifted from 576 to 625 nm. Furthermore, structural color of CNC/Fe/GQDs chiral nematic film change at 44 %, 54 %, 76 %, 87 %, and 99 % relative humidity. Interestingly, due to the stability of GQDs to water molecules, CNC/Fe/GQDs chiral nematic film exhibit fluorescence response to benzene gas even in high humidity (RH = 99 %) environment. Besides, we further developed a smartphone-based response network system for quantitively determinization and signal transformation. This work provides a promising routine to realize a new benzene gas response regime and promotes the development of real-time benzene gas detection.

Agri-food waste biosorbents for volatile organic compounds removal from air and industrial gases - A review.

Approximately 1.3 billion metric tons of agricultural and food waste is produced annually, highlighting the need for appropriate processing and management strategies. This paper provides an exhaustive overview of the utilization of agri-food waste as a biosorbents for the elimination of volatile organic compounds (VOCs) from gaseous streams. The review paper underscores the critical role of waste management in the context of a circular economy, wherein waste is not viewed as a final product, but rather as a valuable resource for innovative processes. This perspective is consistent with the principles of resource efficiency and sustainability. Various types of waste have been described as effective biosorbents, and methods for biosorbents preparation have been discussed, including thermal treatment, surface activation, and doping with nitrogen, phosphorus, and sulfur atoms. This review further investigates the applications of these biosorbents in adsorbing VOCs from gaseous streams and elucidates the primary mechanisms governing the adsorption process. Additionally, this study sheds light on methods of biosorbents regeneration, which is a key aspect of practical applications. The paper concludes with a critical commentary and discussion of future perspectives in this field, emphasizing the need for more research and innovation in waste management to fully realize the potential of a circular economy. This review serves as a valuable resource for researchers and practitioners interested in the potential use of agri-food waste biosorbents for VOCs removal, marking a significant first step toward considering these aspects together.

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Occurrence of organic ultraviolet absorbers in the particle and gas samples from plastic greenhouses: Human inhalation intake risk assessment.

The environmental pollution of organic ultraviolet absorbers (UVAs) has attracted global attention. However, the distribution, sources and risk assessment of UVAs in air from plastic greenhouses are rarely reported. This study was the first to investigate the concentrations of ten UVAs in the air samples from plastic greenhouses. The total concentrations of ten UVAs (∑10UVAs) in the air samples ranged from 5.7 × 103 ng/m3 to 6.3 × 103 ng/m3 (median 5.7 × 103 ng/m3) in greenhouses covered with biodegradable mulch film, 288.2 ng/m3 to 376.4 ng/m3 (median 333.9 ng/m3) in greenhouses covered with PE mulch film, and 97.9 ng/m3 to 142.6 ng/m3 (median 114.9 ng/m3) in greenhouses covered without mulch film. The concentrations of ten UVAs in 65 commercial agricultural films were simultaneously analyzed. Additionally, the potential health risks for greenhouse workers exposed to UVAs were estimated. And the migration simulations showed that the health risk in greenhouses may be higher even if only one UVA is added to the biodegradable mulch film. Therefore, the exposure risk of UVAs in plastic greenhouses needs to be highly prioritized.

Development of a Smartwatch with Gas and Environmental Sensors for Air Quality Monitoring.

In recent years, there has been a growing interest in developing portable and personal devices for measuring air quality and surrounding pollutants, partly due to the need for ventilation in the aftermath of COVID-19 situation. Moreover, the monitoring of hazardous chemical agents is a focus for ensuring compliance with safety standards and is an indispensable component in safeguarding human welfare. Air quality measurement is conducted by public institutions with high precision but costly equipment, which requires constant calibration and maintenance by highly qualified personnel for its proper operation. Such devices, used as reference stations, have a low spatial resolution since, due to their high cost, they are usually located in a few fixed places in the city or region to be studied. However, they also have a low temporal resolution, providing few samples per hour. To overcome these drawbacks and to provide people with personalized and up-to-date air quality information, a personal device (smartwatch) based on MEMS gas sensors has been developed. The methodology followed to validate the performance of the prototype was as follows: firstly, the detection capability was tested by measuring carbon dioxide and methane at different concentrations, resulting in low detection limits; secondly, several experiments were performed to test the discrimination capability against gases such as toluene, xylene, and ethylbenzene. principal component analysis of the data showed good separation and discrimination between the gases measured.

High-Performance Trimethylamine Sensor Based on an Imine Covalent Organic Framework.

As trimethylamine (TMA) is widely used in agriculture and industry, inhalation of TMA can cause very serious negative effects on human health. However, most of the current gas sensors for detecting TMA are commonly performed at high temperatures and cannot meet market needs. Inspired by this, we prepared imine covalent organic frameworks (TB-COF) synthesized from two monomers, 1,3,5-tris(4-aminophenyl)benzene (TAPB) and 1,3,5-benzotricarboxaldehyde (BTCA), using acetic acid as a catalyst at room temperature. Based on this, three sensors were prepared for gas sensitivity testing, namely, TA, BT, and TB-COF sensors. The three sensors were tested for 15 different gases at room temperature. From the whole gas sensitivity data, the TB-COF sensor made by compositing TA and BT has a higher sensitivity (6845.9%) to TMA at 500 ppm, which is 6.1 and 5.4 times higher than the response of TA and BT sensors, respectively. The TB-COF sensor adsorbs and desorbs TMA in a controlled 23 s cycle with a low detection limit of 28.6 ppb. This result indicates that TB-COF prepared at room temperature can be used as a gas-sensitive sensing material for real-time monitoring of TMA. The gas sensing results demonstrate the great potential of COFs for sensor development and application and provide ideas for further development of COFs-based gas sensors.

High-performance peptide biosensor based on unified structure of lotus silk.

The sensitive materials of current gas sensors are fabricated on planar substrates, significantly limiting the quantity of sensitive material available on the sensor and the complete exposure of the sensitive material to the target gas. In this work, we harnessed the finest, resilient, naturally degradable, and low-cost lotus silk derived from plant fibers, to fabricate a high-performance bio-sensor for toxic and harmful gas detection, employing peptides with full surface connectivity. The proposed approach to fabricate gas sensors eliminated the need for substrates and electrodes. To ascertain the effectiveness and versatility of the sensors created via this method, sensors for three distinct representative gases (isoamyl alcohol, 4-vinylanisole, and benzene) were prepared and characterized. These sensors surpassed reported detection limits by at least one order of magnitude. The inherent pliancy of lotus silk imparts adaptability to the sensor architecture, facilitating the realization of 1D, 2D, or 3D configurations, all while upholding consistent performance characteristics. This innovative sensor paradigm, grounded in lotus silk, represents great potential toward the advancement of highly proficient bio gas sensors and associated applications.

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.

High versatility of polyethylene terephthalate (PET) waste for the development of batteries, biosensing and gas sensing devices.

Continuously growing adoption of electronic devices in energy storage, human health and environmental monitoring systems increases demand for cost-effective, lightweight, comfortable, and highly efficient functional structures. In this regard, the recycling and reuse of polyethylene terephthalate (PET) waste in the aforementioned fields due to its excellent mechanical properties and chemical resistance is an effective solution to reduce plastic waste. Herein, we review recent advances in synthesis procedures and research studies on the integration of PET into energy storage (Li-ion batteries) and the detection of gaseous and biological species. The operating principles of such systems are described and the role of recycled PET for various types of architectures is discussed. Modifying the composition, crystallinity, surface porosity, and polar surface functional groups of PET are important factors for tuning its features as the active or substrate material in biological and gas sensors. The findings indicate that conceptually new pathways to the study are opened up for the effective application of recycled PET in the design of Li-ion batteries, as well as biochemical and catalytic detection systems. The current challenges in these fields are also presented with perspectives on the opportunities that may enable a circular economy in PET use.

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.

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.

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.

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.