Experimental and Computational Insights into the Overall Water Splitting Reaction by the Fe–Co–Ni–P Electrocatalyst
Bibliographic record
Abstract
Nonprecious transition-metal phosphides (TMPs) are versatile materials with tunable electronic and structural properties that could be promising as catalysts for energy conversion applications. Despite the facts, TMPs are not explored thoroughly to understand the chemistry behind their rich catalytic properties for the water splitting reaction. Herein, spiky ball-shaped monodispersed TMP nanoparticles composed of Fe, Co, and Ni are developed and used as efficient electrocatalysts for hydrogen and oxygen evolution reaction (HER, OER), and overall water splitting in alkaline medium; and their surface chemistry was explored to understand the reaction mechanism. The optimized Fe 0.5 CoNi 0.5 P catalyst shows attractive activities of HER and OER with low overpotentials and Tafel slopes, and with high mass activities, turnover frequencies, and exchange current densities. When applied to overall water splitting, the electrolyzer Fe 0.5 CoNi 0.5 P||Fe 0.5 CoNi 0.5 P cell can reach a 10 mA cm –2 current density at cell voltages of only 1.52 and 1.56 V in 1.0 M and 30 wt % KOH, respectively, much lower than those of commercial IrO 2 ||Pt/C. The optimized electrolyzer with sizable numbers of chemically active sites exhibits superior durability up to 70 h and 5000 cycles in 1.0 M KOH and can attain a current density as high as 1000 mA cm –2, showing a class of efficient bifunctional electrocatalysis. Experimental and density functional theory-based mechanistic analyses reveal that surface reconstruction takes place in the presence of KOH to form the TMP precatalyst, which results in high coverage of oxygen active species for the OER with a low apparent activation energy ( E a ) for conversion of *OOH to O 2 . These also evidenced the thermoneutral adsorption of H* for the efficient HER half-reaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".