Bioinspired and Bioderived Aqueous Electrocatalysis
Bibliographic record
Abstract
The development of efficient and sustainable electrochemical systems able to provide clean-energy fuels and chemicals is one of the main current challenges of materials science and engineering.Over the last decades, significant advances have been made in the development of robust electrocatalysts for different reactions, with fundamental insights from both computational and experimental work.Some of the most promising systems in the literature are based on expensive and scarce platinum-group metals; however, natural enzymes show the highest per-site catalytic activities, while their active sites are based exclusively on earth-abundant metals.Additionally, natural biomass provides a valuable feedstock for producing advanced carbonaceous materials with porous hierarchical structures.Utilizing resources and design inspiration from nature can help create more sustainable and cost-effective strategies for manufacturing cost-effective, sustainable, and robust electrochemical materials and devices.This review spans from materials to device engineering; we initially discuss the design of carbon-based materials with bioinspired features (such as enzyme active sites), the utilization of biomass resources to construct tailored carbon materials, and their activity in aqueous electrocatalysis for water splitting, oxygen reduction, and CO 2 reduction.We then delve in the applicability of bioinspired features in electrochemical devices, such as the engineering of bioinspired mass transport and electrode interfaces.Finally, we address remaining challenges, such as the stability of bioinspired active sites or the activity of metal-free carbon materials, and discuss new potential research directions that can open the gates to the implementation of bioinspired sustainable materials in electrochemical devices.CONTENTS 6.2.Flow Fields T 6.3.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".