Selective Photocatalytic Dehydrogenation of Formic Acid on Graphite Carbon Nitride with Dual-Sited Cobalt and Platinum Cocatalysts
Bibliographic record
Abstract
Photocatalytic hydrogen production from the selective dehydrogenation of liquid organic hydrogen carriers is emerging as a promising alternative for green hydrogen generation. In this work, graphite carbon nitride (CN)-based photocatalyst is rationally designed by photodeposition of cobalt oxide nanoparticles as the hole trapper for the deprotonation of formic acid (FA), while the photogenerated electrons collected by in situ photodeposited platinum (Pt) nanoparticles reduce these protons to produce sustainable hydrogen. As a result, the well-designed photocatalyst (Co-CN) exhibits excellent hydrogen evolution activity (9039 μmol/h/g) and >99.98% dehydrogenation selectivity. Besides, Co-CN shows great durability in the long-time cycling test. Density functional theory reveals the contribution of Pt and cobalt oxide on the deprotonation from O–H and C–H breakage, respectively. Technoeconomic analysis demonstrates the potential of this reaction system for scale-up application. This present work demonstrates a great example for green hydrogen production from dehydrogenation of liquid organic hydrogen carriers under a mild condition.
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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) | 0.000 | 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 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".