Target Gas Research Articles

Surface reaction mechanisms research of Cun and Pdn (n=1–3) clusters modified MoSTe for fault gases in oil-immersed transformers

Efficient detection and timely resolution of internal faults within synthetic ester oil transformers are imperative for ensuring the stability of power systems. This paper proposes the utilization of MoSTe monolayer modified with Cun and Pdn (n=1–3) clusters for detecting internal fault gases (C2H2, C2H4, and CH4) in transformers. The adsorption mechanism and electronic properties of target gases on the modified substrate surface are systematically studied through density functional theory analysis of adsorption energy, density of states, and deformation charge density. The results indicate that TMn clusters possess the capability to enhance the reactivity of the MoSTe surface, leading to varying degrees of improvement in conductivity and adsorption capacity. Different target gases exhibit distinct adsorption characteristics, with the modified monolayer showing strong adsorption capabilities for C2H2 and C2H4. The calculation of work function and recovery time further reveal the promising potential of Pdn-MoSTe monolayer as resistive gas sensing materials for detecting C2H2 and C2H4, while Cun-MoSTe monolayer are also identified as high-quality candidates for C2H4 sensing materials. This study is expected to facilitate the practical application of MoSTe monolayer in the field of transformer operational state monitoring, providing new insights for exploring novel gas sensing materials.

Non-thermal emission from microquasar jets: The case of GRS 1915+105

Microquasars (μQs) represent a prominent population of Galactic sources, from which broadband non-thermal emission was detected. At present, the potential of μQs as sources of gamma-ray emission is poorly understood and constrained. In most cases, even the power of μQ jets remains largely uncertain, and this hinders the study of gamma-ray generation in these sources. Using observational data obtained in the radio band, we derive an estimate of the jet power in one of the most famous Galactic μQ, in GRS 1915+105. Compared to other studies of this subject, we additionally consider constraints related to the energy dissipated at internal shocks in the jet, the conditions at which are obtained using properties of the radio blobs detected in the jets. We show that even if one subtracts the rest-energy of matter in the jet, the jet power in this source should be very high, 1039ergs−1, or even higher. The properties of the detected radio emission favor jets with a Gauss-strength magnetic field at parsec distances from the black hole. Such a strong magnetic field should suppress inverse Compton (IC) emission from relativistic electrons, at least from parsec-scale jet. On the other hand, this strong magnetic field makes μQ jets perfect proton accelerators. If present, protons should easily be accelerated in the ultra-high-energy regime. However, due to a diluted target, protons should not lose a notable fraction of their energy in the jet, injected into the interstellar medium at the jet termination. This should significantly enhance the density of cosmic rays in the vicinity of GRS 1915+105. This may manifest itself as an extended gamma-ray source or as several compact sources if the target gas has a highly inhomogeneous distribution. This analysis has direct implication for studying Galactic μQ with such gamma-ray observatories as LHAASO, CTA, and SWGA in the next decades.

Cellulose Membranes: Synthesis and Applications for Water and Gas Separation and Purification.

Membranes are a selective barrier that allows certain species (molecules and ions) to pass through while blocking others. Some rely on size exclusion, where larger molecules get stuck while smaller ones permeate through. Others use differences in charge or polarity to attract and repel specific species. Membranes can purify air and water by allowing only air and water molecules to pass through, while preventing contaminants such as microorganisms and particles, or to separate a target gas or vapor, such as H2 and CO2, from other gases. The higher the flux and selectivity, the better a material is for membranes. The desirable performance can be tuned through material type (polymers, ceramics, and biobased materials), microstructure (porosity and tortuosity), and surface chemistry. Most membranes are made from plastic from petroleum-based resources, contributing to global climate change and plastic pollution. Cellulose can be an alternative sustainable resource for making renewable membranes. Cellulose exists in plant cell walls as natural fibers, which can be broken down into smaller components such as cellulose fibrils, nanofibrils, nanocrystals, and cellulose macromolecules through mechanical and chemical processing. Membranes made from reassembling these particles and molecules have variable pore architecture, porosity, and separation properties and, therefore, have a wide range of applications in nano-, micro-, and ultrafiltration and forward osmosis. Despite their advantages, cellulose membranes face some challenges. Improving the selectivity of membranes for specific molecules often comes at the expense of permeability. The stability of cellulose membranes in harsh environments or under continuous operation needs further improvement. Research is ongoing to address these challenges and develop advanced cellulose membranes with enhanced performance. This article reviews the microstructures, fabrication methods, and potential applications of cellulose membranes, providing some critical insights into processing-structure-property relationships for current state-of-the-art cellulosic membranes that could be used to improve their performance.

Dynamic Response and Swift Recovery of Filament Heater‐Integrated Low‐Power Transparent CNT Gas Sensor

AbstractDesigning a transparent carbon nanotube (CNT) gas sensor for nitrogen dioxide (NO2) detection at room temperature (RT), which is unaffected by humidity, is a critical challenge in various technologies. To solve this issue, a filament‐based memristor heater (MH) embedded low‐power CNT gas sensor is proposed to address humidity‐related issues, and dynamic response with a low power consumption of 6.15 µW and swift recovery of <1 ms/30.8 µW is successfully demonstrated, without any serious effects on humidity. This study reveals that the MH can effectively heat the surface of the active region due to the joule heating effect, which improves the absorption and desorption of the target gases and mitigates the influence on the humidity, which is in a response that is below 7.5%. As a result, it is believed that the proposed MH‐embedded transparent CNT gas sensors can address the humidity issues and slow response/recovery with ultralow‐power consumption and high‐accuracy gas sensing characteristics.

Determining the influence of relative humidity on the working parameters of a gas sensor based on zinc oxide

This paper investigates the dependence of operating parameters of a gas sensor based on zinc oxide obtained by the method of direct current magnetron sputtering. The production of gas sensor films was carried out using a VUP-5M vacuum unit with an original material-saving magnetron. The study of the dependence of the working parameters of the gas sensor was carried out under standard conditions. It was found that with increasing humidity, the electrical resistance of the gas sensor decreases and, accordingly, the sensitivity to the target gas decreases. A significant reaction of the gas sensor to an increase in humidity was observed in the range of 65–80 % relative humidity. The mechanism of influence of relative humidity on the sensitivity of a gas sensor based on ZnO was investigated. The change in resistance of the gas sensor is caused by trapped electrons on adsorbed oxygen molecules on the surface of the sensitive layer. The capture of electrons from the conduction zone leads to bending of the conduction zone and an increase in the space charge zone, respectively, to a change in the resistance of the sensitive layer of the gas sensor. In the atmosphere, when O2 molecules are adsorbed on the ZnO surface, they remove an electron from the conduction band. The reaction of oxygen adsorbed on the ZnO surface with reducing gases and the replacement of adsorbed oxygen with other molecules changes the bending of the conduction band and reduces the area of space charge. Adsorption of water on the surface of zinc oxide occurs according to the dissociation mechanism, which consists in the adsorption of steam molecules or hydroxyl groups with the subsequent displacement of previously adsorbed oxygen and free electrons and, accordingly, leads to a decrease in the sensitivity of the gas sensor. In addition, the adsorption of water vapor (H2O) molecules leads to less chemisorption of oxygen species on the ZnO surface due to the reduction of the surface area, which is responsible for the sensor response. Approaches to reduce the influence of relative humidity on the sensitivity of a gas sensor based on zinc oxide have been proposed

E-Nose: Time-Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction.

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time-frequency attention convolutional neural network (TFA-CNN). A time-frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model's robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time-frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.

An olfactory figure-ground segregation: The resistance fluctuation analysis of acetone gas for acetone/random gas mixtures recognition

Recognizing the disease-specific odor in human breath is a feasible non-invasive disease detection strategy. The most critical issue of olfactory diagnosis lies in recognizing the target gas among complex respiration compounds accurately. This paper demonstrated a novel approach to binary gas mixture recognition, exploring nature-related features of the target “odor object” (acetone gas) regardless of its “noisy background” (ethanol or methanol gases). Those features were extracted from time-series resistance fluctuations collected with one conventional SnO2 thin film gas sensor (TF) or our sensitivity-controllable lines gas sensor (TL). 6 features (variance, root mean square, band power, relative band power, entropy, and the number of values crossing mean value) were selected and analyzed with k-Nearest Neighbor regressor (KNN) and Support Vector Regressor (SVR). 18 types of acetone/ethanol mixtures and 3 acetone/methanol mixtures were tested with the proposed models and further explained with SHAP (SHapley Additive exPlanations). The model of TL and KNN achieved the optimal performance of regressing acetone concentrations with the coefficient of determination (R2) of 0.97 and 0.95 in individual gas and binary gas mixtures, respectively. Entropy was found to be the key feature that was linked to sensor properties. Finally, acetone gas data mixed with simulated gas and random noise data were experimented to evaluate our models’ potential for acetone/random gas mixture recognition and robustness.

Influence of light source modulation on detection performance of trace-level SF6 fault gas using photoacoustic spectroscopy

Photoacoustic spectroscopy (PAS) is highly valued in gas detection for its outstanding sensitivity and rapid response. Addressing its limited precision in trace gas analysis, scholars have taken numerous measures to optimize structural and modify the size of acoustic sensors and photoacoustic cells (PACs). Nevertheless, advancements in laser modulation remain understudied due to technical difficulties. This paper theoretically examines the effects of intensity modulation with square wave signals and wavelength modulation involving sawtooth and sinusoidal. In this paper, a theoretical approach is proposed to examine the effects of intensity modulation with square wave signals and wavelength modulation involving sawtooth and sinusoidal signals. Experimental measurements were performed using parameters obtained in preliminary experiments. We constructed an experimental setup with varied-length PACs to bolster experimental dependability, focusing on H2S as the target gas to compare the two laser modulation techniques. The results exhibited that at low frequencies, intensity modulation outperforms wavelength modulation. At resonance frequencies, however, wavelength modulation could provide stronger signals’ intensity. Modulation frequency considerations are therefore crucial when choosing a modulation method for PAS gas detection. Without specific frequency requirements, a second harmonic modulation at resonance is recommended. This study provides guidance on selecting laser modulation methods for PAS, potentially refining the technique’s application in trace gas analysis.

Hierarchical porous spindle-shaped MIL-88B@Fe2O3 with core-shelled structure for conductometric trimethylamine detection

Hierarchical porous spindle-shaped MIL-88B (HPS-MIL-88B) with core-shelled structure was prepared by citric acid-assisted. The material exhibits open channels on its outer surface and a hierarchical internal cavity structure with meso-/macro-pores. The samples were then pretreated at 200 °C to form an oxide film, aiming to improve the conductivity of material. The as-prepared HPS-MIL-88B-6 h@Fe2O3 material with core-shelled structure exhibits superior selectivity and high sensitivity to trimethylamine (TMA) at 200 °C (detection limit as low as 1 ppm) with fast response and excellent repeatability. The outstanding sensing performance was owing to the open and hierarchical porous that enhances the reaction between the target gas and the active sites of the material. In addition, the metal unsaturated active sites of MIL-88B (Lewis’s acid) can selectively undergo acid-base reactions with TMA (Lewis’s base), enhancing the generation of oxygen species and improving the selective adsorption capacity towards the target gas. This work provides a new path route for the application of MOF materials in sensors.

Methane measurement method based on F-P angle-dependent correlation spectroscopy.

This study explores a gas measurement method based on Fabry-Perot (F-P) angle-dependent correlated spectroscopy, which achieves highly sensitive and selective gas measurements by adjusting the angle to match the F-P interference peak with the gas absorption peak. Methane (CH4) is the chosen target gas, and an F-P etalon is designed with parameters matching the CH4 absorption peak. An angle-scanning measurement system is established to enable correlated spectroscopic detection of CH4 gas. Angle-scanning measurements reveal distinct absorption signals at the angle where the F-P interference peak aligns with the CH4 absorption peak. Gas measurements of standard samples demonstrate a linear relationship between the apparent absorbance at the on/off positions and CH4 concentration, allowing for accurate CH4 concentration measurements. The study further investigates the detection limit of the experimental system, achieving a 3σ detection limit of 720 ppm under the on/off measurement mode. A conical incidence model is developed to analyze the impact of beam divergence angles on the transmittance of the F-P cavity. Simulations are conducted to assess absorption signals in the presence of extreme cross-interference, demonstrating the method's robust resistance to cross-interference. The F-P correlated spectroscopy method described in this paper, as a non-dispersive spectroscopic measurement technique, holds promise for designing high-sensitivity gas sensors and imaging applications.

Adsorption of dissolved gases on the TM (Pt, Pd) doped XP(X=In, Ga): A DFT study

Using 2D materials to detect dissolved gases in transformer oil can effectively evaluate the operating status of power systems. In this study, the Pt and Pd atoms are used to modify InP and GaP monolayers for detecting the target gases (C2H2, C2H4, CH4). The band gaps of modified structures are reduced and became more compact compared to the original substrates. The adsorption characteristics of the target gases on the modified substrates are systematically analyzed through adsorption parameters, DCD and DOS. The adsorption effect of the modified InP monolayers on the target gases is ranked as: C2H2>C2H4>CH4. The adsorption capacity of the modified GaP monolayers to the target gases is: C2H4>C2H2>CH4, however the adsorption capacity of the Pd-GaP/gases system is consistent with that of the modified InP monolayers. The sensitivity and recovery time of every gas adsorption systems are further analyzed. In addition, this study provides a theoretical direction for exploring the application prospects of InP and GaP monolayers in the evaluation of operating conditions of oil-immersed transformers.

Computational investigation of pure antimonene and antimonene/bismuthene heterostructure for detecting thermal runaway gases

The widespread use of lithium-ion batteries in everyday life has emphasized the critical need for safety precautions, particularly in monitoring the release of thermal runaway gases to ensure human well-being. This study employs density functional theory (DFT) to construct adsorption models of pure antimonene, bismuthene, below and above of the heterostructure of Sb/Bi. Pure Sb and Bi exhibited semiconducting behavior, whereas the heterostructure of antimonene with bismuthene transformed the semiconducting behavior to metallic. Target gases, including C2H4, CH4, H2, CO, and CO2, were adsorbed onto these models. All gases displayed physiosorption with the studied materials. By analyzing adsorption energy, charge transfer, energy band structure, density of states, work function, sensing response, electron localization function (ELF), and recovery time, the research reveals that the heterostructure displays strong adsorption properties, a robust sensing response, and quicker desorption times, suggesting its potential as a resistive gas sensor. This theoretical exploration offers valuable insights for experimentalists aiming to design and synthesize novel, sensitive materials for detecting thermal runaway gases.

Micro quartz crystal tuning fork based photothermal spectroscopy for trace gas detection

This paper describes a micro-quartz crystal tuning fork (M-QCTF) hermetically sealed in a vacuum environment for the development of low-cost, high-sensitivity photothermal detectors. Compared with conventional QCTF-based photothermal detectors exposed to air, the M-QCTF detector with the approximate stable characteristic resonance frequency (∼32763 Hz) has a small size and high quality factor of 19840. Utilizing the M-QCTF detector and direct absorption spectroscopy (DAS) technology, a gas detection system has been successfully implemented. NO2 was selected as the target gas for evaluating the performance of the M-QCTF based gas sensor. The results indicate a strong linear relationship between the normalized absorbance amplitude of the M-QCTF and the gas concentration, achieving an optimal measurement precision of 117 ppb with an integration time of 230 s. Furthermore, the vacuum packaging structure of the M-QCTF provides advantages such as easy system integration, low cost, and robust anti-interference capability.

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.

Flexible NH3 gas sensors based on ZnO nanostructures deposited on kevlar substrates via hydrothermal method

Owing to their flexibility and high thermal resistance, para-aramid based Kevlar™ fabric substrates are an ideal candidate for obtaining flexible gas sensors for new-generation wearable technologies. The morphology of the surface of the substrate where the target gas comes into contact directly affects the important features of the sensor such as detection limit, selectivity, and response. In this context, a feasible technique is introduced to enhance the efficiency of flexible gas sensors utilizing ZnO/Kevlar™, entailing the modification of ZnO morphology. ZnO based gas sensing layers were deposited on Kevlar™ fabric substrates by a two-stage method. Initially, seed layer was produced on Kevlar™ fabric using the ultrasonic bath method, with five different seed layer processing times ranging from 1 min to 20 min. In the nucleation, all nanostructured layers were deposited by hydrothermal method with constant parameters and under the same conditions. As the seeding time was increased, nanorods (1D) formed on the surface became flakes (2D), leading to considerable improvement in NH3 gas sensing properties. The sensor with a 5-min seeding time showed a sensitivity of 49 % at an optimal operating temperature of 190 °C, while the sensor produced in 20 min showed the best response performance with 169 % at 130 °C for 50 ppm NH3 gas. The increase in seeding time not only reduced the operating temperature of the gas sensor through the conversion from 1D nanostructure to 2D, but also significantly increased the sensor response. Under constant hydrothermal conditions and solution concentration, the density per unit area increases. However, after 10 min of seeding, the length of nanorods shortens and turn into a rod-flake hybrid morphology. A superior sensor performance was demonstrated for the samples examined. The obtained results also showed that flexible Kevlar™ fabric could be a feasible and interesting substrate for nanostructured ZnO-based NH3 gas sensors.

Bayesian linearized amplitude variation with offset and azimuth inversion and uncertainty analysis in horizontal transversely isotropic media

AbstractThe stratum can be modelled as a horizontal transversely isotropic medium when a single set of vertically parallel fractures embedded in an isotropic background medium, which facilitates efficient study for fractured reservoirs. Elastic parameters and fracture weaknesses are important parameters to describe the characteristics of fractured reservoirs, and seismic inversion plays a significant role in parameters estimation. The commonly used deterministic inversion methods do not fully utilize the prior information and fails to present the uncertainty analysis of inversion results. To address these shortcomings, we propose a Bayesian linearized amplitude variation with offset and azimuth inversion method tailored for horizontal transversely isotropic media, enabling a more robust analysis of uncertainty. Within the framework of Bayesian inversion, the proposed method successfully derives analytical expressions for the posterior mean and covariance of both elastic parameters and fracture weaknesses. The response characteristics of the anisotropic reflection coefficient are analysed, and it is found that the perturbations of elastic parameters have a greater effect on reflection coefficient compared to fracture weaknesses. Synthetic data examples confirm that the accuracy of estimated P‐ and S‐wave velocities and density surpasses that of fracture weaknesses, and the proposed method still performs well for the case of moderate noise. A field data example demonstrates that the inverted profiles agree well with the logging curve, and the estimated fracture weaknesses display significantly high values in the reservoir area. The estimated reservoir parameters not only contribute to a more accurate representation of the fractured gas‐bearing reservoir but also provide insights into the target gas reservoir through its posterior distribution. Both synthetic and field data examples demonstrate the stability and reliability of the proposed method in characterizing fractured reservoirs. We determine that the proposed method provides an available tool for nuanced evaluation of uncertainty for the inversion results, and it is helpful for the fine description of fractured hydrocarbon‐bearing reservoirs.

Adsorption and sensing behavior of Cr-doped WS2 monolayer for hazardous gases in agricultural greenhouses: A DFT study

This study used Density functional theory (DFT) to analyze the adsorption behavior of the Cr-doped WS2 monolayer on five hazardous gases (NH3, SO2, NO, NO2, and CO) in agricultural greenhouses. The optimal stable doped site for the Cr atom was identified, and various adsorption parameters such as adsorption energy, adsorption distance, charge transfer and desorption time were calculated. Results indicated that intrinsic WS2 had limited adsorption capacity for five target gases. Moreover, Cr doped significantly enhanced WS2 monolayer conductivity, shifting NH3, SO2, NO and NO2 adsorption from physisorption to chemisorption with Eads <0.6 eV, while CO remained physisorbed. The conductivity of the five gases underwent significant changes after adsorption on Cr-WS2. Desorption times at four temperatures (298 K, 398 K, 498 K and 598 K) indicate that Cr-WS2 demonstrates ideal gas sensor behavior for NH3 gas. However, the physisorbed and short desorption at room temperature suggested unsuitability for CO detection in practical applications. Conversely, the extended desorption time at 598 K made Cr-WS2 more appropriate as a gas scavenger for SO2, NO and NO2 gases. These findings underscore the potential of Cr-WS2 monolayers as novel gas warning or adsorbent materials, offering crucial applications in detecting and removing hazardous gases in agricultural greenhouses.

Real-Time Monitoring of Toxic Gases using Artificial Neural Networks with SnO2-based Thick Film Gas Sensors

The present study aims to develop an advanced system for real-time identification and measurement of harmful gases using Pd-doped SnO2-based thick film gas sensors integrated with Artificial Neural Networks. These sensors, renowned for their exceptional sensitivity and selectivity, undergo testing in a meticulously controlled gas chamber environment. Here, gas concentrations, temperature, and humidity are meticulously controlled. The gas chamber setup allows for mixing gases like carbon monoxide, nitrogen dioxide, and sulfur dioxide with nitrogen gas to achieve desired concentrations ranging from to . Temperature is kept at and relative humidity is maintained between to . The sensitivity analysis of the sensor demonstrates its adeptness in detecting low concentrations of target gases, with sensitivity increasing as gas concentrations rise, also the selectivity assessments highlight their ability to accurately differentiate between target gases and common interferents, ensuring precise detection even in complex gas mixtures. Response time testing indicates rapid detection capabilities, crucial and useful for emergencies. The Artificial Neural Network model, trained via the backpropagation algorithm, demonstrates remarkable accuracy, precision, recall, and F1 scores in predicting gas concentrations. The findings indicate that this integrated system offers a reliable and efficient solution for toxic gas detection, with potential applications in industrial safety and environmental monitoring.

Molecular engineering of a MOF with an ionic liquid sheath for direct air separation at ambient conditions

Combining ionic liquids (ILs) with metal–organic frameworks (MOFs) offers broad prospects for gas separation applications. However, identifying the best IL-MOF pair for a target gas separation solely based on experimental techniques is impractical. Herein, the promising IL and MOF combination was selected among 35,672 distinct ILs and 5,629 different MOFs by fusing computational methods at different molecular scales, density functional theory, conductor-like screening model for real solvents calculations, and grand canonical Monte Carlo simulations to engineer a MOF with an IL sheath (MOFILS) for direct air separation. The new MOFILS composed of [P6,6,6,14][DCA] and PCN-250(Fe) designed at the atomic level was then experimentally synthesized after fine-tuning the MOF to achieve high-performance air separation. An exceptionally high ideal O2/N2 selectivity of 26 was reached at 1 bar and 25 °C, boosting the benchmark value by four-times. The results of equilibrium and dynamic gas adsorption measurements further indicated a remarkably high selectivity of 382 for the separation of O2/N2:21/79 mixture, demonstrating the broad potential of the MOFILS designed in this work for direct air separation.

Research on intelligent sensor for industrial hazardous gases monitoring based on Tc doped C3N using density functional theory

With the ongoing global industrialization, a considerable amount of hazardous gases is emitted into the atmospheric environment. Achieving intelligent monitoring of industrial hazardous gases is a crucial pathway toward realizing the modernization of electronic information. In this study, the use of Tc doped C3N nanosheet for the intelligent online monitoring of industrial hazardous gases (CO, NO2, O3) is firstly proposed. The doping and modification mechanism of Tc nanoparticle on the C3N surface are revealed. Simultaneously, the adsorption and sensing mechanisms of the three target gases on the surface of pure C3N and Tc doped C3N nanosheet is deeply elucidated through density of states, band structure, and differential charge density analysis. The results indicate that Tc particle can stably bind to the C3N surface, providing numerous active sites for the adsorption of target gases, enabling selective capture and scavenging of hazardous gases. Furthermore, the adsorption type for all three gases shifts from physical adsorption to physicochemical adsorption, with significant changes in the electronic signals during surface reactions, manifested by drastic alterations in the band structure and enhanced electron mobility. The adsorption capacity order for the three gases is O3 > NO2 > CO. The achievements of this work not only provide a theoretical foundation for the design and preparation of Tc doped C3N nanosensors, but also offer a new direction for environmental protection and intelligent detection in industrial applications.