Cobalt-Loaded Carbon Nitride Demonstrates Enhanced Photocatalytic Production of H<sub>2</sub> from Lignocellulosic Biomass Components
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Photocatalytic production of hydrogen (H 2 ) from biomass is a promising avenue for advancing sustainable energy generation. We prepared carbon nitride (CN x ) with cobalt (Co) as an oxidation cocatalyst (denoted CN x /Co) to improve photocatalytic H 2 production through photoreforming components of lignocellulosic biomass under visual light irradiation. CN x /Co was synthesized by loading Co onto preformed CN x through a straightforward thermal deposition. The thermal loading of Co at 450 °C led to the formation of a mixed valence CoO x, which shifted Co 2+ character to Co 3+ over the course of the hydrogen evolution reaction (HER). Compared to CN x without Co, our materials with 0.3 and 0.6 wt % Co demonstrate twice the apparent quantum yield (AQY) for H 2 production under irradiation at 405 nm using glucose as a sacrificial electron donor (3.0% and 2.8%, respectively, vs 1.4%). Time-resolved spectroscopic investigations indicate that the Co extracts charges in the subnanosecond time scale and promotes the formation of beneficial long-lived charges. Impressively, some photocatalytic activity is observed when using the robust polymers of cellulose and lignin as the oxidation substrates (0.2 and 0.1% AQY, respectively). The ability to oxidize abundant biomass without extensive prepreparation is promising for waste upcycling applications.
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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".