Floating Carbon Nitride Composites for Practical Solar Reforming of Pre-treated Wastes to Hydrogen Gas
Bibliographic record
Abstract
Photoreforming (PR) is a promising green-energy technology that can use sunlight to mitigate biomass and plastic waste while producing hydrogen gas at ambient pressure and temperature. However, practical challenges including photocatalyst lifetime, recyclability, and low production rates in turbid waste suspensions limit PR’s industrial potential. By immobilising PR catalyst materials (carbon nitride/platinum; CNx|Pt and carbon nitride/nickel phosphide; CNx|Ni2P) on hollow glass microspheres, which act as floating supports enabling practical composite recycling, such limitations can be overcome. Substrates derived from plastic and biomass, including poly(ethylene terephthalate) (PET) and cellulose, are reformed by floating PR composites, which are reused for up to 10 consecutive cycles under realistic, vertical simulated solar irradiation (AM1.5G), reaching activities of 921 ± 166 µmolH2 m−2 h−1 on pre-treated PET. Floating PR composites are also advantageous in realistic waste where turbidity prevents light absorption by non-floating catalyst powders, achieving 503.2 ± 1.9 µmolH2 m−2 h−1 using floating CNx versus non-detectable H2 production with non-floating CNx. Low Pt loadings (0.033 ± 0.0013 % m/m) demonstrate consistent performance and recyclability, allowing efficient use of precious metals for PR hydrogen production at the largest areal scale (217 cm2) reported to date, taking an important step toward practical PR implementation.
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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 | 0.001 |
| 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".