Bioinspired Synthesis of Reduced Graphene Oxide-Wrapped <i>Geobacter sulfurreducens</i> as a Hybrid Electrocatalyst for Efficient Oxygen Evolution Reaction
Bibliographic record
Abstract
Doping/decorating of graphene or reduced graphene oxide (rGO) with heteroatoms provides a promising route for the development of electrocatalysts which will be useful in many technologies, including water splitting. However, current doping approaches are complicated, not eco-friendly, and not cost-effective. Herein, we report the synthesis of doped/decorated rGO for oxygen evolution reaction (OER) using a simple approach that is cost-effective, sustainable, and easy to scale up. The OER catalyst was derived from the reduction of GO by an exo-electron-transferring bacterium, Geobacter sulfurreducens. Various analytical tools indicate that OER active elements such as Fe, Cu, N, P, and S decorate the rGO flakes. The hybrid catalyst (i.e., Geobacter/rGO) produces a geometric current density of 10 mA cm–2 at an overpotential of 270 mV versus the reversible hydrogen electrode with a Tafel slope of 43 mV dec–1 and possesses high durability, as evidenced through 10 h of stability testing. Electrochemical analyses suggest the importance of Fe and its possible role as an active site for OER. Overall, this work represents a simple approach toward the development of an earth-abundant, eco-friendly, and highly active OER electrocatalyst for various applications such as solar fuel production, rechargeable metal–air batteries, and microbial electrosynthesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".