Cost-Effective Electrodeposited Mixed Transition Metal Electrocatalysts for Efficient Hydrogen Evolution Reaction
Bibliographic record
Abstract
Abstract Hydrogen is a clean fuel with high energy density, considered one of the alternative energy sources of the future. Hydrogen evolution reaction (HER) could produce pure hydrogen on a large scale while striving for effective electrocatalysts. Here, binary and ternary mixed transition metals (Mn, Co, and Ni) were synthesized by an electrodeposition method and employed as efficient HER electrocatalysts. It was found that the combination of transition metals could positively tune the corresponding morphology and activity rather than using single metals. Namely, NiMn electrocatalysts with an onset potential of 83 mV and a Tafel slope of 103 mV.dec− 1 showed superior activity toward HER in alkaline media compared to the other developed electrocatalysts. This high activity was related to improved intrinsic activity, higher energy efficiency, and enhanced conductivity thanks to the synergy between manganese and nickel. NiMn electrocatalyst also displayed a durable and stable performance, rendering it a promising electrocatalyst for efficient electrocatalysis of HER.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".