Bifunctional Water Splitting Performance of NiFe LDH Improved by Pd<sup>2+</sup> Doping
Bibliographic record
Abstract
Abstract Nobel metal doping is an effective strategy to enhance the catalytic activity of electrocatalysts. Herein, a novel bifunctional electrocatalyst based on NiFe layered double hydroxide with ultra‐low Pd2+ doping (NiFePd LDH) was constructed by a one‐step hydrothermal method, where the Pd2+ is introduced by PdCl42−. The results show that Ni2+ and Pd2+ species are concomitantly deposited, and the slow‐release introduction of Pd2+ improves the element uniform distribution and effectively affects the electronic structures of active species by inducing local defects and lattice distortions, which is beneficial for stimulating the catalytic activity of NiFePd LDH. Under the optimal hydrothermal time, NiFePd LDH‐3 h only requires OER/HER overpotentials of 270 mV at 50 mA cm−2/‐316 mV at −10 mA cm−2, respectively, whose Tafel slopes are only 69.3/135.8 mV dec−1. As a bifunctional catalyst, it achieves a low voltage of 1.74 V at 10 mA cm−2 for overall water splitting with excellent long‐term durability.
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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".