In Situ Modulation of NiFeOOH Coordination Environment for Enhanced Electrocatalytic‐Conversion of Glucose and Energy‐Efficient Hydrogen Production
Bibliographic record
Abstract
Abstract Glucose electrocatalytic‐conversion reaction (GCR) is a promising anode reaction to replace the slow oxygen evolution reaction (OER), thus promoting the development of hydrogen production by electrochemical water splitting. Herein, NiFe‐based metal‐organic framework (MOF) is used as a precursor to prepare W‐doped nickel‐iron phosphide (W‐NiFeP) nanosheet arrays by ion exchange and phosphorylation, which exhibit a high electrocatalytic activity toward the hydrogen evolution reaction (HER), featuring an overpotential of only −179 mV to achieve the current density of 100 mA cm −2 in alkaline media. Notably, electrochemical activation of W‐NiFeP facilitates the in situ formation of phosphate groups producing W,P‐NiFeOOH, which, in conjunction with the W co‐doped amorphous layers, leads to a high electrocatalytic performance toward GCR, due to enhanced proton transfer and adsorption of reaction intermediates, as confirmed in experimental and theoretical studies. Thus, the two‐electrode electrolyzer of the W‐NiFeP/NF||W,P‐NiFeOOH/NF for HER||GCR needs only a low cell voltage of 1.56 V to deliver 100 mA cm −2 at a remarkable hydrogen production efficiency of 1.86 mmol h −1 , with a high glucose conversion (98.0%) and formic acid yields (85.2%). Results from this work highlight the significance of the development of effective electrocatalysts for biomass electrocatalytic‐conversion in the construction of high‐efficiency electrolyzers for green hydrogen production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".