AIMC Topic: Gases

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Enhanced Room Temperature Sensing Properties of Tin Oxide Gas Sensors Exploiting Carbon Nanotubes: High-Accuracy Ammonia Gas Classification via Supervised Learning Regression Algorithms.

ACS sensors
The sensing properties of tin oxide (SnO) gas sensors, enhanced by the exploitation of carbon nanotubes (CNTs), were explored at room temperature. The CNT/tin oxide hybrid sensors demonstrated superior performance at room temperature compared to sing...

Active Learning of Atomic Size Gas/Solid Potential Energy Surfaces via Physics Aware Models.

Journal of chemical information and modeling
We propose an active learning (AL) framework to develop classical force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, computationally e...

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

The Analyst
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...

QSAR-based prediction of acute inhalation toxicity and SHAP interpretability analysis of fluorocarbon environmental-friendly insulating gases.

Environmental research
Environmental-friendly fluorocarbon insulating gases have emerged as the preferred substitutes for SF due to their low global warming potential (GWP) and excellent insulation properties. However, current gases face challenges in meeting multiple perf...

State Ensemble Energy Recognition (SEER): A Hybrid Gas-Phase Molecular Charge State Predictor.

Journal of chemical information and modeling
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...

Real-Time Gas Identification at Room Temperature Using UV-Modulated Sb-Doped SnO Sensors via Machine Learning.

ACS sensors
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...

DNA-Mediated Bioinspired MXene Gas Sensor Array with Machine Learning for Noninvasive Cancer Recognition.

ACS nano
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 ...

A flow pattern recognition method for gas-liquid two-phase flow based on dilated convolutional channel attention mechanism.

PloS one
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...

Modeling approaches for data-driven model predictive control of acid gases in waste-to-energy plants.

Waste management (New York, N.Y.)
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...

Predicting anaerobic digestion stability in load-flexible operation using gas phase indicators and classification algorithms.

Bioresource technology
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...