A short review on green H2 production by aqueous phase reforming of biomass derivatives
Bibliographic record
Abstract
Abstract Due to high energy content and environmentally friendly attributes, hydrogen is regarded as an ideal energy carrier, serving as a viable alternative to fossil fuels. Steam reforming of fossil fuels is currently the dominant source of hydrogen production with negative environmental impacts, therefore aqueous phase reforming (APR) of biomass derivatives represents an attractive method for green hydrogen production due to its relatively mild operating temperatures and carbon neutrality. This work provides an overview of the types of catalysts employed in the APR process and their pros and cons regarding their performance and operating conditions. Effects of various catalyst supports, e.g., alloy oxides, composite active metals and ceria, and feedstocks, on performance of the catalysts in APR are also discussed. Recent advances and challenges in APR are summarized into several aspects, (1) doping metals/inorganics into support, (2) structural manipulation and defect induction to support, (3) synthesis of single-atom catalysts, (4) development of more eco-friendly processes or catalysts. The present review can provide guidance for prospective development of efficient catalysts and supports for APR of biomass derivatives for green H2 production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".