AIMC Topic: Gases

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Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics.

ACS sensors Mar 10, 2025
Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for real-time health monitoring. This review highlights recent advancements in breath-sensing technologies...
Nanostructures Volatile Organic Compounds Gases Humans Breath Tests Machine Learning
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Prediction of landfill gases concentration based on Grey Wolf Optimization - Support Vector Regression during landfill excavation process.

Waste management (New York, N.Y.) Mar 4, 2025
In some areas, there is a phenomenon that the landfill is full or even over-capacity with the extension of the service period. With the aging and damage of the protective facilities, this phenomenon may have a more serious impact on the surrounding e...
Support Vector Machine Air Pollutants Algorithms Environmental Monitoring Refuse Disposal Waste Disposal Facilities Odorants Gases
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Robotic-based Experimental Procedure for Colorimetric Gas Sensing Development.

Journal of visualized experiments : JoVE Feb 28, 2025
This paper presents a robot-based experimental program aimed at developing an efficient and fast colorimetric gas sensor. The program employs an automated Design-Build-Test-learning (DBTL) approach, which optimizes the search process iteratively whil...
Gases Algorithms Colorimetry Robotics
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Selective gas adsorption using graphitic carbon nitride: Exploring the role of molecular descriptors by artificial intelligence frameworks.

Journal of environmental management Feb 10, 2025
Artificial Intelligence (AI) frameworks estimate the adsorption energies of crucial pollutants like CO, O, NO, NO, SOF, HCHO, and CO on Graphitic Carbon Nitride (g-CN) surfaces. The predictive capabilities of two AI-based models, namely, Artificial N...
Artificial Intelligence Adsorption Neural Networks, Computer Gases Graphite Nitrogen Compounds
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Multifunctional Fiber Robotics with Low Mechanical Hysteresis for Magnetic Navigation and Inhaled Gas Sensing.

ACS sensors Feb 7, 2025
Recently, increasing research attention has been directed toward detecting the distribution of hazardous gases in the respiratory system for potential diagnosis and treatment of lung injury. Among various technologies, magnetic fiber robots exhibit g...
Robotics Titanium Gases
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Multigas Identification by Temperature-Modulated Operation of a Single Anodic Aluminum Oxide Gas Sensor Platform and Deep Learning Algorithm.

ACS sensors Jan 20, 2025
Semiconductor metal oxide (SMO) gas sensors are gaining prominence owing to their high sensitivity, rapid response, and cost-effectiveness. These sensors detect changes in resistance resulting from oxidation-reduction reactions with target gases, res...
Acetone Electrodes Semiconductors Ammonia Deep Learning Ethanol Algorithms Aluminum Oxide Gases Temperature Neural Networks, Computer Nitrogen Dioxide
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Recent advances in medical gas sensing with artificial intelligence-enabled technology.

Medical gas research Jan 18, 2025
Recent advancements in artificial intelligence-enabled medical gas sensing have led to enhanced accuracy, safety, and efficiency in healthcare. Medical gases, including oxygen, nitrous oxide, and carbon dioxide, are essential for various treatments b...
Artificial Intelligence Gases Humans
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Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.

ACS sensors Jan 6, 2025
Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In this study, we propose an ideal gas sensor design strategy with high screening efficiency and low c...
Greenhouse Gases Carbon Dioxide Gases Density Functional Theory Methane Machine Learning Adsorption
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Novel method for predicting concentrations of incineration flue gas based on waste composition and machine learning.

Journal of environmental management Dec 5, 2024
The complex composition of solid waste leads to the variability of flue gas emissions during its incineration, which poses a challenge to the stable operation of incineration and pollution control systems. To address this problem, the study explored ...
Machine Learning Air Pollutants Incineration Solid Waste Gases
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Feasibility of classification of drainage and river water quality using machine learning methods based on multidimensional data from a gas sensor array.

Annals of agricultural and environmental medicine : AAEM Dec 2, 2024
OBJECTIVE: The aim of the study is to verify whether the electronic nose system - an array of 17 gas sensors with a signal analysis system - is a useful tool for the classification and preliminary assessment of the quality of drainage water.
Gases Environmental Monitoring Machine Learning Electronic Nose Feasibility Studies Neural Networks, Computer Water Quality Poland Rivers
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