Microkinetic modelling‐driven insights into water–gas shift catalysis—Towards unravelling active transition metal‐based bimetallic alloys
Bibliographic record
Abstract
Abstract Amid the ongoing energy crisis, the demand for ecofriendly fuel has skyrocketed. Hydrogen (H 2 ) is a clean energy source showcasing exciting potential to be the fuel of future. It can be produced through water–gas shift (WGS) reaction. Presently, the catalysts used for WGS reaction have some limitations–thermal sintering, carbon monoxide (CO) poisoning and lack of applicability to small scale operations. To minimize these obstacles, a microkinetic model (MKM) has been developed to identify most active transition metal catalysts as well as bimetallic alloy catalysts for WGS reaction. The MKM is constructed over stepped (211) sites of transition metal catalyst using CatMAP, wherein carbon and oxygen binding energies are used as descriptors. At the reaction conditions of 573 K, and 10 bar, with an initial conversion of 10%, Cu exhibited the maximum turnover of 10 −4 s −1 and the activity trend is noted to be–Cu > Co > Pt > Ni > Rh > Ru > Pd > Au > Ag. Most of the monometallic catalysts exhibited lower turnovers due to catalyst deactivation. To eradicate this problem, bimetallic alloys of Cu, Co, Pt, and Ni‐based catalysts were explored whereby Cu 3 Rh and Cu 3 Pt were identified as potential alloy catalysts lying at the top of the volcano curve (~10 −2 s −1 ) for the reaction. Besides these, Co 3 Pt, Co 3 Pd, Cu 3 Ni, Pt 3 Cu, Ni 3 Pt, and Ni 3 Pd also exhibited turnover rates (~10 −3 s −1 ) higher than the most active monometallic catalysts. These findings reveal high‐performance catalysts, which may subsequently be tested experimentally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".