Manipulation of Hydrogen Transfer Behaviors by RhCu Alloying Enables an All‐in‐one Sustainable “Furfural‐Nitrate” System
Bibliographic record
Abstract
Abstract Nitrate and furfural are typical wastes mainly from industrialization and agriculturalization progresses, and their clean conversions are still very challenging for a sustainable future. Nevertheless, scant attention has been devoted to the core issues: the rational integration of two wastes recycling and the targeted manipulation of hydrogen (H*) transfer behaviors to address their sluggish reaction kinetics. Herein, we report an all‐in‐one electrochemical energy system that is thermodynamically designed by coupling nitrate reduction (NO 3 RR) and furfural oxidation reactions (FORs) together. Particularly, the poor kinetics for both electrode reactions are efficaciously optimized by the bifunctional electrocatalyst of RhCu alloy nanowires on copper foam (RhCu NW/CF) with highly improved dual‐directional H*‐modulation performances, thus initializing NO 3 RR for NH 3 synthesis at +0.31 V and driving FOR for H 2 harvest at an onset potential lower than 0 V. Eventually, such integrated “Furfural‐Nitrate” system can simultaneously effectuate the electricity energy supply (10.76 mW cm −2 ), wastewater purification, cathodic hydrogen storage (NH 3 ), anodic H 2 production, and biomass upgrading. Hence, it provides a promising perspective of “turning waste into treasure” in a rational manner, justifying its all‐in‐one property in addressing the global challenge of sustainable energy.
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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".