Enhanced stability and efficient anodic hydrogen production using chromium-modified copper catalysts for hydroxymethylfurfural electrooxidation
Bibliographic record
Abstract
Producing low-carbon hydrogen is one of crucial steps toward mitigating the impacts of the rapidly changing climate. While conventional water electrolysis offers a sustainable method to generate hydrogen, it is industrially limited by the high energy demands and related cost of the anodic oxygen evolution reaction (OER), which remains a major bottleneck. In this study, we tackled this challenge by replacing the OER with a more energy-efficient reaction: the oxidation of Hydroxymethylfurfural (HMF), which also produces hydrogen as a co-product via the aldehyde-to‑hydrogen pathway. We developed a chromium-modified copper nano-catalyst enabling the low potential (0.3 V vs. RHE) anodic hydrogen production at a high current density of 200 mA/cm 2 with 100 % Faradaic efficiency—doubling the activity compared to the copper benchmark. X-ray absorption spectroscopy analysis revealed an improved retention of the Cu metallic state upon Cr addition, a critical factor for efficient and stable performance. Furthermore, our theoretical models demonstrated that the modified catalyst enhances charge transfer while promoting HMF adsorption and facilitating C H bond cleavage. Operationally, we improved the system stability by reducing the hydroxide retention time to prevent the spontaneous degradation of HMF in alkaline medium. The proposed innovative method doubles hydrogen output for the same electricity input, offering a scalable hydrogen production and efficient solution through advanced catalyst design and optimized operations.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".