Visible Light-Activated Room Temperature NO2 Gas Sensing Based on the In2O3@ZnO Heterostructure with a Hollow Microtube Structure.

The persistent challenge of poor recovery characteristics of NO2 sensors operated at room temperature remains significant. However, the development of In2O3-based gas sensing materials provides a promising approach to accelerate response and recovery for sub-ppm of NO2 detection at room temperature. Herein, we propose a simple two-step method to synthesize a one-dimensional (1D) In2O3@ZnO heterostructure material with hollow microtubes, by coupling metal-organic frameworks (MOFs) (MIL-68 (In)) and zinc ions. Meanwhile, the In2O3@ZnO composite-based gas sensor exhibits superior sensitivity performance to NO2 under visible light activation. The response value to 5 ppm of NO2 at room temperature is as high as 1800, which is 35 times higher than that of the pure In2O3-based sensor. Additionally, the gas sensor based on the In2O3@ZnO heterostructure demonstrates a significantly reduced response/recovery time of 30 s/67 s compared to the sensor based on pure In2O3 (74 s/235 s). The outstanding gas sensing properties of the In2O3@ZnO heterostructure-based sensors can be attributed to the enhanced photogenerated charge separation efficiency resulting from the heterostructure effect, and the improved receptor function toward NO2, which can increase the reactive sites and gas adsorption capacity. In summary, this work proposes a low-cost and efficient method to synthesize a 1D heterostructure material with microtube structures, which can serve as a fundamental technique for developing high-performance room-temperature gas sensors.

Facile Fabrication of Lilac-Like Multiple Self-Supporting WO3 Nanoneedle Arrays with Cubic/Hexagonal Phase Junctions for Highly Sensitive Ethylene Glycol Gas Sensors.

Metal oxides with nanoarray structures have been demonstrated to be prospective materials for the design of gas sensors with high sensitivity. In this work, the WO3 nanoneedle array structures were synthesized by a one-step hydrothermal method and subsequent calcination. It was demonstrated that the calcination of the sample at 400 °C facilitated the construction of lilac-like multiple self-supporting WO3 arrays, with appropriate c/h-WO3 heterophase junction and highly oriented nanoneedles. Sensors with this structure exhibited the highest sensitivity (2305) to 100 ppm ethylene glycol at 160 °C and outstanding selectivity. The enhanced ethylene glycol gas sensing can be attributed to the abundant transport channels and active sites provided by this unique structure. In addition, the more oxygen adsorption caused by the heterophase junction and the aggregation of reaction medium induced by tip effect are both in favor of the improvement on the gas sensing performance.

Image Sensing of Gaseous Acetone Using Secondary Alcohol Dehydrogenase-Immobilized Mesh for Exhaled Air.

Human-borne acetone is a potent marker of lipid metabolism. Here, an enzyme immobilization method for secondary alcohol dehydrogenase (S-ADH), which is suitable for highly sensitive and selective biosensing of acetone, was developed, and then its applicability was demonstrated for spatiotemporal imaging of concentration distribution. After various investigations, S-ADH-immobilized meshes could be prepared with less than 5% variation by cross-linking S-ADH with glutaraldehyde on a cotton mesh at 40 °C for 15 min. Furthermore, high activity was obtained by adjusting the concentration of the coenzyme nicotinamide adenine dinucleotide (NADH) solution added to the S-ADH-immobilized mesh to 500 µM and the solvent to a potassium phosphate buffer solution at pH 6.5. The gas imaging system using the S-ADH-immobilized mesh was able to image the decrease in NADH fluorescence (ex 340 nm, fl 490 nm) caused by the catalytic reaction of S-ADH and the acetone distribution in the concentration range of 0.1-10 ppm-v, including the breath concentration of healthy people at rest. The exhaled breath of two healthy subjects at 6 h of fasting was quantified as 377 and 673 ppb-v, which were consistent with the values quantified by gas chromatography-mass spectrometry.

Synergistic Etching and Hydrogen Bonding-Induced Self-Assembly of MXene/MOF Hybrid Aerogel for Flexible Room-Temperature Gas Sensing.

Limited by insufficient active sites and restricted mechanical strength, designing reliable and wearable gas sensors with high activity and ductility remains a challenge for detecting hazardous gases. In this work, a thermally induced and solvent-assisted oxyanion etching strategy was implemented for selective pore opening in a rigid microporous Cu-based metal-organic framework (referred to as CuM). A conductive CuM/MXene aerogel was then self-assembled through cooperative hydrogen bonding interactions between the carbonyl oxygen atom in PVP grafted on the surface of defect-rich Cu-BTC and the surface functional hydroxyl group on MXene. A flexible NO2 sensing performance using the CuM/MXene aerogel hybridized sodium alginate hydrogel is finally achieved, demonstrating extraordinary sensitivity (S = 52.47 toward 50 ppm of NO2), good selectivity, and rapid response/recovery time (0.9/4.5 s) at room temperature. Compared with commercial sensors, the relative error is less than 7.7%, thereby exhibiting significant potential for application in monitoring toxic and harmful gases. This work not only provides insights for guiding rational synthesis of ideal structure models from MOF composites but also inspires the development of high-performance flexible gas sensors for potential multiscenario applications.

Ambipolar Heterojunction Sensors: Another Way to Detect Polluting Gases.

Gas sensors based on ambipolar materials offer significant advantages in reducing the size of the analytical system and enhancing its efficiency. Here, bilayer heterojunction devices are constructed using different octafluorinated phthalocyanine complexes, with Zn and Co as metal centers, combined with a lutetium bisphthalocyanine complex (LuPc2). Stable p-type behavior is observed for the ZnF8Pc/LuPc2 device under both electron-donating (NH3) and -oxidizing (NO2 and O3) gaseous species, while the CoF8Pc/LuPc2 device exhibits n-type behavior under reducing gases and p-type behavior under oxidizing gases. The nature of majority of the charge carriers of Co-based devices varies depending on the nature of target gases, displaying an ambipolar behavior. Both heterojunction devices demonstrate stable and observable response toward all three toxic gases in the sub-ppm range. Remarkably, the Co-based device is highly sensitive toward ammonia with a limit of detection (LOD) of 200 ppb, whereas the Zn-based device demonstrates exceptional sensitivity toward oxidizing gases, with excellent LOD values of 4.9 and 0.75 ppb toward NO2 and O3, respectively, which makes it one of the most effective organic heterojunction sensors reported so far for oxidizing gases.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses.

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

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.

Internal Standard Addition System for Online Breath Analysis.

Breath analysis with secondary electrospray ionization (SESI) coupled to mass spectrometry (MS) is a sensitive method for breath metabolomics. To enable quantitative assessments using SESI-MS, a system was developed to introduce controlled amounts of gases into breath samples and carry out standard addition experiments. The system combines gas standard generation through controlled evaporation, humidification, breath dilution, and standard injection with the help of mass-flow controllers. The system can also dilute breath, which affects the signal of the detected components. This response can be used to filter out contaminating compounds in an untargeted metabolomics workflow. The system's quantitative capabilities have been shown through standard addition of pyridine and butyric acid into breath in real time. This system can improve the quality and robustness of breath data.

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.

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

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

Portable hydrogel kit based on Michael addition reaction for (E)-2-hexenal gas detection.

Plants exhibit rapid responses to biotic and abiotic stresses by releasing a range of volatile organic compounds (VOCs). Monitoring changes in these VOCs holds the potential for the early detection of plant diseases. This study proposes a method for identifying late blight in potatoes based on the detection of (E)-2-hexenal, one of the major VOC markers released during plant infection by Phytophthora infestans. By combining the Michael addition reaction with cysteine-mediated etching of aggregation-induced emission gold nanoclusters (Au NCs), we have developed a portable hydrogel kit for on-site detection of (E)-2-hexenal. The Michael addition reaction between (E)-2-hexenal and cysteine effectively alleviates the etching of cysteine-mediated Au NCs, leading to a distinct fluorescence color change in the Au NCs, enabling a detection limit of 0.61 ppm. Utilizing the superior loading and diffusion characteristics of the three-dimensional structure of agarose hydrogel, our sensor demonstrated exceptional performance in terms of sensitivity, selectivity, reaction time, and ease of use. Moreover, quantitative measurement of (E)-2-hexenal was made easier by using ImageJ software to transform fluorescent images from the hydrogel kit into digital data. Such method was effectively used for the early detection of potato late blight. This study presents a low-cost, portable fluorescent analytical tool, offering a new avenue for on-site detection of plant diseases.

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.

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.

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.

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.