Machine learning for predicting catalytic ammonia decomposition: An approach for catalyst design and performance prediction-Reference-Cited by-同舟云学术

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Machine learning for predicting catalytic ammonia decomposition: An approach for catalyst design and performance prediction

Published:2024-06 Issue: Volume:89 Page:111688
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Guo Wenjuan,Shafizadeh Alireza,Shahbeik Hossein,Rafiee Shahin,Motamedi Shahrzad,Ghafarian Nia Seyyed Alireza,Nadian Mohammad Hossein,Li Fanghua,Pan Junting,Tabatabaei Meisam,Aghbashlo Mortaza

Funder

Chinese Academy of Agricultural Sciences

Institute of Tropical Aquaculture and Fisheries, University Malaysia Terengganu

University of Tehran

Ministry of Higher Education, Malaysia

Publisher

Elsevier BV

Reference51 articles.

1. Ammonia decomposition in the thermochemical waste-heat recuperation systems: a view from low and high heating value;Pashchenko;Energ. Conver. Manage.,2022

2. A universal machine learning framework for electrocatalyst innovation: a case study of discovering alloys for hydrogen evolution reaction;Chen;Adv. Funct. Mater.,2022

3. Ammonia as effective hydrogen storage: a review on production, storage and utilization;Aziz;Energies,2020

4. H2 production via ammonia decomposition using non-noble metal catalysts: a review;Bell;Top. Catal.,2016

5. Co–Ce–Al–O mesoporous catalysts for hydrogen generation via ammonia decomposition;Maleki;Int. J. Hydrogen Energy,2022

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