MnO<sub>x</sub>‐Decorated Nickel‐Iron Phosphides Nanosheets: Interface Modifications for Robust Overall Water Splitting at Ultra‐High Current Densities
Bibliographic record
Abstract
Abstract Exploring highly active and stable bifunctional water‐splitting electrocatalysts at ultra‐high current densities is remarkably desirable. Herein, 3D nickel‐iron phosphides nanosheets modified by MnOx nanoparticles are grown on nickel foam (MnOx/NiFeP/NF). Resulting from the electronic coupling effect enabled by interface modifications, the intrinsic activities of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are improved. Meanwhile, 3D nanosheets provide abundant active sites for HER and OER, leading to accelerating the reaction kinetics. Besides, the shell‐protection characteristic of MnOx improves the durability of MnOx/NiFeP/NF. Therefore, MnOx/NiFeP/NF shows exceptional bifunctional electrocatalytic activities toward HER (an overpotential of 255 mV at 500 mA cm−2), OER (overpotentials of 296 and 346 mV at 500 and 1000 mA cm−2, respectively), and overall water splitting (cell voltages of 1.796 and 1.828 V at 500 and 1000 mA cm−2, respectively). Furthermore, it owns remarkably outstanding stability for overall water splitting at ultra‐high current densities (120 and 70 h at 500 and 1000 mA cm−2, respectively), which outperforms almost all of the non‐noble metal electrocatalysts. This work presents efficient strategies of interface modifications, 3D nanostructures, and shell protection to afford ultra‐high current densities.
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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 | 0.000 |
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".