Electrical transport along bacterial nanowires from <i>Shewanella oneidensis</i> MR-1
Bibliographic record
Abstract
Bacterial nanowires are extracellular appendages that have been suggested as pathways for electron transport in phylogenetically diverse microorganisms, including dissimilatory metal-reducing bacteria and photosynthetic cyanobacteria. However, there has been no evidence presented to demonstrate electron transport along the length of bacterial nanowires. Here we report electron transport measurements along individually addressed bacterial nanowires derived from electron-acceptor-limited cultures of the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1. Transport along the bacterial nanowires was independently evaluated by two techniques: (i) nanofabricated electrodes patterned on top of individual nanowires, and (ii) conducting probe atomic force microscopy at various points along a single nanowire bridging a metallic electrode and the conductive atomic force microscopy tip. The S. oneidensis MR-1 nanowires were found to be electrically conductive along micrometer-length scales with electron transport rates up to 10(9)/s at 100 mV of applied bias and a measured resistivity on the order of 1 Ω·cm. Mutants deficient in genes for c-type decaheme cytochromes MtrC and OmcA produce appendages that are morphologically consistent with bacterial nanowires, but were found to be nonconductive. The measurements reported here allow for bacterial nanowires to serve as a viable microbial strategy for extracellular electron transport.
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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) | 0.000 | 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".