AIMC Topic: Gases

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Exhaled gas biomarkers: a non-invasive approach for distinguishing diabetes and its complications.

The Analyst Aug 4, 2025
Exhaled gas detection offers a safe, convenient, and non-invasive clinical diagnostic method for preventing the progression of diabetes to complications. In this study, gas chromatography-mass spectrometry (GC-MS) analysis and statistical methods wer...
Exhalation Diabetes Complications Gases Breath Tests Butadienes Hemiterpenes Middle Aged Diabetes Mellitus, Type 2 Gas Chromatography-Mass Spectrometry Volatile Organic Compounds Support Vector Machine Aged Male Biomarkers Female Humans Machine Learning
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State Ensemble Energy Recognition (SEER): A Hybrid Gas-Phase Molecular Charge State Predictor.

Journal of chemical information and modeling Jul 8, 2025
Accurately resolving a three-dimensional structure that corresponds to an experimental mass spectrometry (MS) result is valuable for outcomes such as improved analyte identification, determination of physiochemical properties relating to conformation...
Models, Molecular Machine Learning Thermodynamics Software Gases Mass Spectrometry
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Real-Time Gas Identification at Room Temperature Using UV-Modulated Sb-Doped SnO Sensors via Machine Learning.

ACS sensors Jul 2, 2025
This study presents a novel approach for real-time gas identification at room temperature. We use UV-modulated Sb-doped SnO sensors combined with machine learning. Our method exclusively employs the gas response () as the sole metric. This eliminates...
Machine Learning Temperature Hydrogen Tin Compounds Ammonia Ultraviolet Rays Gases Nitrogen Dioxide
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DNA-Mediated Bioinspired MXene Gas Sensor Array with Machine Learning for Noninvasive Cancer Recognition.

ACS nano Jun 28, 2025
Noninvasive odor sensing is important in environmental monitoring and medical diagnosis. The two-dimensional material MXene is widely used due to its unique sensing properties but has limitations in specifically recognizing a certain gas. This study ...
Humans Machine Learning Neoplasms Breath Tests Transition Elements Odorants Nitrites Gases Biosensing Techniques DNA
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A flow pattern recognition method for gas-liquid two-phase flow based on dilated convolutional channel attention mechanism.

PloS one Jun 24, 2025
Addressing the issue of insufficient key feature extraction leading to low recognition rates in existing deep learning-based flow pattern identification methods, this paper proposes a novel flow pattern image recognition model, Enhanced DenseNet with...
Gases Image Processing, Computer-Assisted Pattern Recognition, Automated Neural Networks, Computer Algorithms Deep Learning
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Modeling approaches for data-driven model predictive control of acid gases in waste-to-energy plants.

Waste management (New York, N.Y.) Jun 4, 2025
The economic and environmental sustainability of waste-to-energy (WtE) plants can be improved through advanced control techniques such as model predictive control (MPC), which enables stricter regulation by incorporating constraints, handling multipl...
Hydrochloric Acid Gases Refuse Disposal Models, Theoretical Waste Management
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Predicting anaerobic digestion stability in load-flexible operation using gas phase indicators and classification algorithms.

Bioresource technology Apr 8, 2025
This study investigates early warning indicators for process instabilities in anaerobic digestion caused by shock-loadings in biogas plants, focussing on gas-phase parameters to avoid substrate analyses. With the increasing use of renewable energy so...
Algorithms Anaerobiosis Biofuels Gases Classification Algorithms Methane Bioreactors
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Gas Sensor Drift Compensation Using Semi-Supervised Ensemble Classifiers with Multi-Level Features and Center Loss.

ACS sensors Apr 8, 2025
The drift compensation of gas sensors is a significant and challenging issue in the field of electronic noses (E-nose). Compensating sensor drift has a great benefit in improving the performance of E-nose systems. However, conventional methods often ...
Neural Networks, Computer Algorithms Gases Electronic Nose
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Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics.

Waste management (New York, N.Y.) Mar 18, 2025
A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR)...
Pyrolysis Plastics Machine Learning Gases Biomass
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Artificial Intelligence in Gas Sensing: A Review.

ACS sensors Mar 11, 2025
The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based i...
Environmental Monitoring Humans Gases Artificial Intelligence Machine Learning Deep Learning
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