Self‐Powered System for H<sub>2</sub> Production and Biomass Upgrading
Bibliographic record
Abstract
Abstract Hydrazine‐oxidation‐assisted self‐powered H2 generation system greatly expands the applicability of hydrogen production technology. However, the high cost of hydrazine greatly impedes the widespread adoption of hydrazine‐contained energy systems for large‐scale H2 production. Besides, the gaseous products of hydrazine splitting, comprising a mixture of H2 and N2, necessitate energy‐intensive downstream separation. Here, taking advantage of a low‐potential furfural oxidation reaction (FOR) on the Cu electrode, a self‐powered H2 production system by integrating a direct furfural fuel cell (DFFC) and a bipolar H2 production electrolyser is reported. Ru‐dispersed Cu nanowire with remarkable catalytic activity is developed as a hydrogen evolution reaction (HER) catalyst to couple with the FOR. The HER‐FOR electrolyzer achieves bipolar H2 production with an apparent 200% Faradaic efficiency, attaining a current density of 100 mA cm−2 with a low cell voltage of 0.43 V. The DFFC displays an open circuit potential of 0.969 V and a peak power density up to 193 mW cm−2. Inspired by the bipolar H2 production that eliminates the gas separation, a self‐powered system utilizing furfural as the sole consumable, which yields a pure H2 production rate of 6 mmol h−1 m−2 is demonstrated. This work provides a new avenue for constructing self‐powered H2 production systems.
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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) | 0.002 | 0.001 |
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".