Materials and meta data for electrolysis and hydrogen production
Techniques like electrodeposition, thermal decomposition, electroless plating, pressing, sintering, and coating are the traditional catalysts manufacturing routes for electrolysers.
Research Opportunities
Summary
To produce green hydrogen, there are mainly ten known types of electrolysis: (i) proton exchange membrane (PEM), (ii) anion exchange membrane (AEM), (iii) solid oxide water electrolysis (SOWE), (iv) alkaline water electrolysis (AWE), (v) photolysis, (vi) thermochemical, (vii) photoelectrochemical, (viii) geothermal closed loop, (ix) supercritical solutions (HPHT), and (x) untreated seawater electrolysis.
Within the coating route, there are methods (e.g., physical: physical vapor deposition, dip-coating, spin-coating, casting, filtration, lay-by-layer assembly, thermal spray; and chemical: coupling agents, sol-gel method, chemical vapor deposition, surface grafting, in-situ growth, electroless plating, electroplating) for functional catalytic membrane development.
Each electrolyser types, choice of materials and manufacturing comes with challenges, and therefore data centric approach (at least focusing on one manufacturing route, e.g., coating) may accelerate change in discovery and data practices.
With research gap in material selection strategies, proposed hydrogen production informatics will be critical to advancing applications exploiting their catalyst properties for hydrogen production. This is also directly aligned with post-COP26 global Hydrogen and Net Zero Strategy. With following objectives, we aim to develop a method in the field of materials used in all electrolysis types leading to the development of green hydrogen production informatics.
- Electrolyser material data gathering from open access followed by data classification (i.e., numerical, categorical, time-series, text)
- Create machine learning (ML) predictive tool to analyse sensitivity of the electrolyser performance with respect to materials, manufacturing, and electrolyser types. This will be carried by developing algorithm which can read the data, create dependent and independent data sets based on associated features, splitting the data in training, and testing sets, training the model using the algorithm, evaluating the classifier, and choosing the classifier with most accuracy).
- Investigate the relationship between materials and information by apply combinatorial chemistry, material property, manufacturing, structure-property, materials data management and product life management, leading to meta data gathering and create value of each data points.
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