TM LDH Meets Birnessite: A 2D‐2D Hybrid Catalyst with Long‐Term Stability for Water Oxidation at Industrial Operating Conditions
Bibliographic record
Abstract
Abstract Efficient noble‐metal free electrocatalyst for oxygen evolution reaction (OER) is critical for large‐scale hydrogen production via water splitting. Inspired by Nature's oxygen evolution cluster in photosystem II and the highly efficient artificial OER catalyst of NiFe layered double hydroxide (LDH), we designed an electrostatic 2D‐2D assembly route and successfully synthesized a 2D LDH(+)‐Birnessite(−) hybrid. The as‐constructed LDH(+)‐Birnessite(−) hybrid catalyst showed advanced catalytic activity and excellent stability towards OER under a close to industrial hydrogen production condition (85 °C and 6 M KOH) for more than 20 h at the current densities larger than 100 mA cm−2. Experimentally, we found that besides the enlarged interlayer distance, the flexible interlayer NiFe LDH(+) also modulates the electronic structure of layered MnO2, and creates an electric field between NiFe LDH(+) and Birnessite(−), wherein OER occurs with a greatly decreased overpotential. DFT calculations confirmed the interlayer LDH modulations of the OER process, attributable to the distinct electronic distributions and environments. Upshifting the Fe‐3d orbitals in LDH promotes electron transfer from the layered MnO2 to LDH, significantly boosting up the OER performance. This work opens a new way to fabricate highly efficient OER catalyst for industrial water oxidation.
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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".