Hydrogen storage via adsorption: A review of recent advances and challenges

Fuel, Journal Year: 2025, Volume and Issue: 387, P. 134273 - 134273

Published: Jan. 10, 2025

Language: Английский

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Predicting Adsorption Energies on MXene Surfaces Using Machine Learning to Enhance Catalyst Design for the Water–Gas Shift Reaction

The Journal of Physical Chemistry C, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Language: Английский

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Density Functional Theory-Based Indicators to Estimate the Corrosion Potentials of Zinc Alloys in Chlorine-, Oxidizing-, and Sulfur-Harsh Environments

Molecules, Journal Year: 2024, Volume and Issue: 29(16), P. 3790 - 3790

Published: Aug. 10, 2024

Nowadays, biodegradable metals and alloys, as well their corrosion behavior, are of particular interest. The process alloys under various harsh conditions can be studied via the investigation atom adsorption on metal surfaces. This performed using density functional theory-based simulations. Importantly, comprehensive analytical data obtained in simulations including parameters such energy, amount charge transferred, atomic coordinates, etc., utilized machine learning models to predict ability, catalytic activity, alloys. In this work, indicators Zn surfaces Cl-, S-, O-rich environments collected. A dataset containing height, partial states, work function values, electronic charges individual atoms is presented. addition, based these descriptors, it found that a Cl-rich environment less harmful for different compared an environment, more S-rich environment.

Language: Английский

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Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review

Journal of Environmental Science and Health Part A, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: Feb. 2, 2025

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 understand better current status, future possibilities, capabilities of ML supporting environmentally friendly development applications. Previous applications were classified into three categories according their objectives: material process performance prediction sustainability evaluation. helps optimize BDMs systems, predict properties performance, reverse engineering, data difficulties evaluations. Ensemble models cutting-edge Neural Networks operate satisfactorily on these datasets easily generalized. neural network poor interpretability, there not been any studies assessment that consider geo-temporal dynamics; thus, building methods is currently practical. Future research should follow workflow. Investigating potential system optimization, evaluation sustainable requires further investigation.

Language: Английский

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Machine learning descriptor-assisted exploration of metal-modified graphene hydrogen storage materials

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 119, P. 45 - 55

Published: March 20, 2025

Language: Английский

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Modulation of Hydrogen Evolution Reaction Performance of MXenes by Doped Transition Metals: Comprehensive Exploration of High-Throughput Computing and Machine Learning

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Due to the unique properties of MXenes, doping transition metals can modulate their catalytic and make them potential materials for hydrogen evolution reaction (HER). Nevertheless, extensive combinatorial space poses a challenge rapid screening catalysts. To address this issue, we conducted high-throughput calculations on series metal atom-doped Ti3CNO2 Zr2HfCNO2. Furthermore, local structure corresponding electronic changes are analyzed, focusing influence HER properties. site identification features were introduced train multisite prediction model with final accuracy R2 = 0.97 predicted trend adsorption Gibbs free energy (ΔGH*) across range MXenes structures, which doped TM atoms. The results show that Nb, Sc, Rh, W, Ti, V resulted in |ΔGH*| < 0.2 eV more than 38 M'2M″CNO2, respectively, they effective dopant atoms enhancing ability M'2M″CNO2. This study not only demonstrates performance but also highlights importance models development efficient

Language: Английский

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Advancements in Ti3C2 MXene-Integrated Various Metal Hydrides for Hydrogen Energy Storage: A Review

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(9), P. 673 - 673

Published: April 28, 2025

The current world is increasingly focusing on renewable energy sources with strong emphasis the economically viable use of to reduce carbon emissions and safeguard human health. Solid-state hydrogen (H2) storage materials offer a higher density compared traditional gaseous liquid methods. In this context, review evaluates recent advancements in binary, ternary, complex metal hydrides integrated 2D Ti3C2 MXene for enhancing H2 performance. This perspective highlights progress made through development active sites, created by interactions between multilayers, few-layers, internal edge sites hydrides. Specifically, selective incorporation content has significantly contributed improvements performance various Key benefits include low operating temperatures enhanced capacity observed MXene/metal hydride composites. versatility titanium multiple valence states (Ti0, Ti2+, Ti3+, Ti4+) Ti-C bonding plays crucial role optimizing absorption desorption processes. Based these promising developments, we emphasize potential solid-state interfaces fuel cell applications. Overall, MXenes represent significant advancement realizing efficient storage. Finally, discuss challenges future directions advancing toward commercial-scale solutions.

Language: Английский

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Thermodynamic stability descriptor of A2BX6-type perovskite materials

Materials Chemistry and Physics, Journal Year: 2024, Volume and Issue: unknown, P. 130324 - 130324

Published: Dec. 1, 2024

Language: Английский

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