Development of newly designed biomass-based electrodes used in water electrolysis for clean hydrogen production
Bibliographic record
Abstract
The conventional electrolysis is recognized as a mature and promising hydrogen (H 2 ) production technology, but there is still a strong need for further performance improvement. In this regard, achieving an effective H 2 evolution reaction at the cathode requires costly catalysts, such as platinum and various catalyst-modified electrode materials. Nevertheless, these materials are expensive and involve complex production procedures. Due to an increasing interest in deploying biomaterial-based cathodes as potential alternatives to conventional cathode materials, we make the focus of this study on such materials, and a graphite-loaded bioelectrode is, in this regard, synthesized for electrolysis application for effective H 2 production. The surface morphology and electrochemical activity of the produced biocathode are characterized. Our results show that the H 2 production performance of the system improves with the increasing graphite dosage on the biocathode and with the applied voltage ranging from 2 to 6 V. At improved operating conditions, the highest H 2 production rate of 1000 ppm (8.18 mg/m 3 min) is obtained using a 1.5 g graphite-loaded biocathode at an applied voltage of 6 V. Consequently, the produced graphite-loaded biocathode can be a promising option for sustainable and effective H 2 production with waste minimization, owing to its high conductivity, low-cost, and good stability. • Graphite-starch-doped bioelectrode was prepared for hydrogen production applications. • Starch-derived graphite-loaded bioelectrode displayed a high carbon content of 77.7 %. • Highest hydrogen production rate obtained at 6 V for 1.5 g of graphite-loaded bioelectrode. • Highest anodic current of 0.7 mA/cm 2 was observed for the 1.5 g graphite-loading. • The manufactured bioelectrode presented notable production at high graphite loadings.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".