(General Student Poster Award Winner, 2nd Place) Enhanced Oxygen Evolution Reaction Performance through Alkaline-Earth Metal Doped Fe-Rich Nano Dry-Petals: A Cost-Effective and Eco-Friendly Electrocatalyst Approach
Bibliographic record
Abstract
The progress towards hydrogen production via electrolysis technologies rely on the advancement of economical, abundant, non-toxic, stable, and efficient non-precious metal catalysts for oxygen evolution reactions (OER). As the second most abundant metal on Earth, iron (Fe) offers a cost-effective alternative to more expensive OER catalysts derived from ruthenium (Ru), iridium (Ir), cobalt (Co) and nickel (Ni). However, Fe-based catalysts typically display limited OER activity. Here we present a unique strategy of introducing an alkaline-earth metal into Fe-rich crystal for the enhanced OER activity in alkaline condition. To achieve this, a facile method was developed to obtain a nano dry-petals structured M-FeOOH electrocatalyst. The optimized M-FeOOH electrocatalyst exhibits an impressively low overpotential of +259 mV at a current density of 10 mA cm-2 in 1.0 M KOH which was found to be superior to the pristine FeOOH electrode. Through a series of experiments and density functional theory (DFT) calculations, we reveal that the addition of an alkaline-earth metal to FeOOH as a dopant optimizes the electronic structure and free energy for adsorbed intermediates synergistically. As a result, the OER activity of the M-FeOOH electrocatalyst is significantly improved. Our results highlight the possibility of doping FeOOH OER catalysts using an affordable and eco-friendly approach, setting the stage for the development of advanced OER electrocatalysts.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.018 |
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".