GaN-Mediated Synergy with Iridium Species for Light-Driven Neat Formic Acid Dehydrogenation
Bibliographic record
Abstract
Light-driven formic acid (FA) dehydrogenation for on-site H 2 production provides an innovative strategy for addressing the issues of H 2 storage and transportation. Herein, by assembling gallium nitride (GaN) nanowires with dispersed IrO 2 nanoclusters, a nanohybrid photocatalyst with strong interaction was developed for efficient and durable light-driven H 2 production from neat FA without any solvents and/or additives. As revealed by in situ spectroscopic characterizations and computational investigations, the GaN nanowire-mediated interaction with IrO 2 nanoclusters facilitates efficient separation and directional transfer of charge carriers and endows the photocatalyst with antisintering capability. Moreover, the synergistic effect between GaN and IrO 2 selectively breaks the O–H and C–H bonds of FA in sequence via a formate reaction pathway with a greatly reduced activation energy. Benefiting from these distinct properties, the photocatalyst delivers a H 2 evolution rate of 220.5 mol·g cat. –1 ·h –1 with a marked selectivity of 99% and a turnover frequency of 8.1 × 10 5 per hour under focused light illumination without external thermal input. A record-high total turnover number of 1.3 × 10 8 is achieved over an operation of 700 h. This work provides a strategy for mediating catalytic sites by GaN for light-driven FA dehydrogenation.
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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".