AIMC Topic: Hydrogen

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Durative Monitoring of Sulfur Hexafluoride Characteristic Gases under Hydrogen Interference Using a Time2Vec-Encoded CNN-Transformer-LSTM Model Based on a Heterogeneous Gas Sensor Array.

ACS sensors
Gas-insulated switchgear (GIS) systems extensively employ sulfur hexafluoride (SF) as an insulating medium and are widely deployed in modern power systems. Under partial discharge (PD) conditions, SF decomposes to generate hazardous byproducts such a...

A Resilient MEMS Sensor Array-AI System for DGA-Based Transformer Fault Monitoring in High-H Environments.

ACS sensors
MOS gas sensors offer significant potential for real-time dissolved gas analysis (DGA) in power transformer monitoring. However, their performance is often degraded in high-hydrogen (H) environments due to cross-interference, which impairs detection ...

Graph-Based Machine Learning Framework for Predicting Hydrogen Storage Capacity in Metal-Organic Frameworks.

Journal of chemical information and modeling
Hydrogen is a clean and high-energy fuel, yet its safe and efficient storage remains a key obstacle to widespread adoption. Metal-organic frameworks (MOFs), with their high surface area and tunable porosity, have emerged as promising candidates for s...

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...

Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process.

ACS sensors
Hydrogen detection plays a crucial role in various scenes of hydrogen energy such as hydrogen vehicles, hydrogen transportation and hydrogen storage. It is essential to develop a hydrogen detection system with ultrafast response times (<1 s) for the ...

PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.

Journal of chemical information and modeling
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. T...

Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning.

ACS sensors
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevan...

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

Journal of chemical information and modeling
The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and sele...

Recent advances in dark fermentative hydrogen production from vegetable waste: role of inoculum, consolidated bioprocessing, and machine learning.

Environmental science and pollution research international
Waste-centred-bioenergy generation have been garnering interest over the years due to environmental impact presented by fossil fuels. Waste generation is an unavoidable consequence of urbanization and population growth. Sustainable waste management t...

Production of high calorific value hydrogen-rich combustible gas by supercritical water gasification of straw assisted by machine learning.

Bioresource technology
This article reveals the basic laws of straw supercritical water gasification (SCWG) and provides basic experimental data for the effective utilization of straw. The paper studied the impact of three operational conditions on the production of high-c...