Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm.

An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH4) emissions. Employing a LiTaO3-based pyroelectric detector integrated with micro-electro-mechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH4 concentrations with a low limit of detection of about 5.6 ppmv and showed rapid response times with t90 consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH4 levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH4 levels below 10 ppmv. Postdenoising, nonlinear regression analyses based on the modified Beer-Lambert equation returned R2 values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH4 leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO2 injection.

Visualizing research trends and identifying hotspots of herbal components for treating cardiovascular diseases: A bibliometric analysis from 2000 to 2023.

OBJECTIVE: The objective of this study was to investigate the global research trends in herbal medicine for the treatment of cardiovascular disease (CVD) from 2000 to 2023. A bibliometric approach was employed to analyze international collaborations, knowledge structures, emerging trends, and research frontiers. METHOD: The Web of Science (WOS) core collection was utilized as the database, employing the search formula (((TS = (traditional Chinese medicine)) OR TS = (Chinese herbal medicinal ingredient)) OR TS = (Chinese herbal medicinal constituent)) AND TS = (cardiovascular disease) to conduct the search. The search period spanned from January 1, 2000, to February 14, 2023, and the literature type included articles and reviews. RESULTS: A total of 1478 papers were included in the analysis after searching the WOS database and excluding conference proceedings, news articles, retractions, editorials, and letters. China demonstrated the highest number of publications, followed by the United States and Taiwan (China). The institution with the highest publications was the Chinese Academy of Medical Sciences. China, the United States, and India were the main countries involved in research in this field, and there was significant collaboration among them. The hotspots related to herbal components for treating cardiovascular diseases from 2000 to 2023 included systematic reviews, ischemic reperfusion injury, global burden, type 2 diabetes, and protection. CONCLUSION: This paper provides a reference for the future development of herbal research in cardiovascular aspects by revealing the current status, hotspots, and trends of global herbal research in cardiovascular factors over more than 20 years. Identification of potential collaborators and institutions can assist researchers in exploring new directions for future research and discovering new perspectives for potential collaborations in this field.

Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage.

The utilization of carbon capture utilization and storage (CCUS) in unconventional formations is a promising way for improving hydrocarbon production and combating climate change. Shale wettability plays a crucial factor for successful CCUS projects. In this study, multiple machine learning (ML) techniques, including multilayer perceptron (MLP) and radial basis function neural networks (RBFNN), were used to evaluate shale wettability based on five key features, including formation pressure, temperature, salinity, total organic carbon (TOC), and theta zero. The data were collected from 229 datasets of contact angle in three states of shale/oil/brine, shale/CO2/brine, and shale/CH4/brine systems. Five algorithms were used to tune MLP, while three optimization algorithms were used to optimize the RBFNN computing framework. The results indicate that the RBFNN-MVO model achieved the best predictive accuracy, with a root mean square error (RMSE) value of 0.113 and an R2 of 0.999993. The sensitivity analysis showed that theta zero, TOC, pressure, temperature, and salinity were the most sensitive features. This research demonstrates the effectiveness of RBFNN-MVO model in evaluating shale wettability for CCUS initiatives and cleaner production.

Effects of temperature gradient and particle size on self-ignition temperature of low-rank coal excavated from inner Mongolia, China.

This study investigates the effects of temperature gradient and coal particle size on the critical self-ignition temperature T CSIT of a coal pile packed with low-rank coal using the wire-mesh basket test to estimate T CSIT based on the Frank-Kamenetskii equation. The values of T CSIT, the temperature gradient and the apparent activation energy of different coal pile volumes packed with coal particles of different sizes are measured. The supercriticality or subcriticality of the coal is assessed using a non-dimensional index IHR based on the temperature gradient at the temperature cross-point between coal and ambient temperatures for coal piles with various volumes and particle sizes. The critical value IHRC at the boundary between supercriticality and subcriticality is determined as a function of pile volume. The coal status of supercritical or subcritical can be separated by critical value of IHR as a function of pile volume. Quantitative effects of coal particle size on T CSIT of coal piles are measured for constant pile volume. It can be concluded that a pile packed with smaller coal particles is more likely to undergo spontaneous combustion, while the chemical activation energy is not sensitive to coal particle size. Finally, the effect of coal particle size on T CSIT is represented by the inclusion of an extra term in the equation giving T CSIT for a coal pile.

Field Study on Correlation between CO2 Concentration and Surface Soil CO2 Flux in Closed Coal Mine Goaf.

Self-heating of coal mine goaf or shallow coal seams can release an outbreak of unimaginable pollution disaster under suitable circumstances. As an indicator gas, CO2 is always used to determine the coal spontaneous combustion state during the self-heating process. Based on this, the paper investigated the influence of abandoned coal mine goaf CO2 on the surface environment by measuring the CO2 concentration in the borehole connected to the goaf and CO2 flux on the soil surface. Furthermore, rainfall and atmospheric temperature effects are discussed to illustrate the correlation between the CO2 concentration and surface soil CO2 flux in the closed mine goaf. Subsequently, the tracer gas experimental method is employed to analyze the effect of air leakage from an open-pit slope on CO2 flux. The experimental results demonstrated that the distribution of CO2 concentration in the borehole confirms the continuous diffusion of goaf CO2 onto the surface. The value of CO2 flux in the goaf is significantly higher than that of a normal area. Temperature is one of the primary factors that affect the CO2 flux on the field. Air leakage from the slope promotes the surface soil-overlying goaf CO2 diffusion. The study provides important reference data for the assessment of the mining area field environment and the determination of the spontaneous combustion risk of the residual coal in the goaf.