Transition metal oxide catalytic abilities for fuel cell applications: Density functional theory (DFT) studies
Bibliographic record
Abstract
Because of its heavy reliance on fossil fuels, the world's existing energy supply releases pollutants into the atmosphere. Researchers have conducted extensive studies on greener energy sources, particularly fuel cell technology, which generates power from electrochemical energy while emitting minimal carbon. But there are obstacles to fuel cell efficiency and commercialization, such as the slow oxygen reduction reaction (ORR) and the expensive and unstable platinum (Pt) catalysts used in fuel cell membranes. This work explores the use of tungsten oxide, cobalt, and titanium oxide nanoparticles as inexpensive, active electrocatalysts. Despite extensive research on the monoxides of these metals, their bimetallic compositions when combined with oxygen to function as fuel cell catalysts remain poorly understood. This work evaluates the catalytic capabilities of the crystallographic surfaces of these oxides using Density Functional Theory (DFT) via CASTEP and DMol3, as well as the Adsorption Locator module. These surfaces, which include CoWO4, Co3WO8, and TiWO4, have different levels of stability and reactivity when it comes to absorbing hydrogen and oxygen. This makes them potentially useful for changing the hydrogen oxidation and oxygen reduction reactions in fuel cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".