Biomass Photoreforming for Hydrogen and Value‐Added Chemicals Co‐Production on Hierarchically Porous Photocatalysts
Bibliographic record
Abstract
Abstract Biomass, a naturally abundant, sustainable and clean resource has great potential as an alternative to replace the limited fossil feedstock for value‐added chemicals and fuels. Biomass with abundant reductive functional groups could theoretically act as electron donor to consume photogenerated holes and/or active free radicals. Biomass photoreforming over semiconductor photocatalysts using solar light as energy input attracts much attention in this context. However, biomass photoreforming still suffers low conversion efficiency and product selectivity due to its structural complexity, poor solubility and unclear reaction mechanism. Owing to the advanced features of mass diffusion of biomass derivatives and adjustable surface properties, hierarchically porous photocatalysts with desired active sites at each length scale of porosity have shown their superiority in boosting the conversion efficiency and selectivity of biomass photoreforming. Herein, a critical review is presented on selective biomass photoreforming for simultaneous H 2 and value‐added chemicals co‐production on hierarchically porous photocatalysts. The fundamentals of biomass photoreforming and current bottleneck of biomass valorization by photocatalytic process are presented and analyzed. The rational photocatalyst design with hierarchically porous structure to improve the biomass conversion and product selectivity by boosting mass transfer is highlighted. Finally the challenges and opportunities for photocatalytic biomass valorization are presented in the perspective ssection.
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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".