Disparity of Cytochrome Utilization in Anodic and Cathodic Extracellular Electron Transfer Pathways of <i>Geobacter sulfurreducens</i> Biofilms
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Extracellular electron transfer (EET) in microorganisms is prevalent in nature and has been utilized in functional bioelectrochemical systems. EET of Geobacter sulfurreducens has been extensively studied and has been revealed to be facilitated through c -type cytochromes, which mediate charge between the electrode and G. sulfurreducens in anodic mode. However, the EET pathway of cathodic conversion of fumarate to succinate is still under debate. Here, we apply a variety of analytical methods, including electrochemistry, UV–vis absorption and resonance Raman spectroscopy, quartz crystal microbalance with dissipation, and electron microscopy, to understand the involvement of cytochromes and other possible electron-mediating species in the switching between anodic and cathodic reaction modes. By switching the applied bias for a G. sulfurreducens biofilm coupled to investigating the quantity and function of cytochromes, as well as the emergence of Fe-containing particles on the cell membrane, we provide evidence of a diminished role of cytochromes in cathodic EET. This work sheds light on the mechanisms of G. sulfurreducens biofilm growth and suggests the possible existence of a nonheme, iron-involving EET process in cathodic mode.
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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".