Cell Surface Electrochemical Heterogeneity of the Fe(III)-Reducing Bacteria <i>Shewanella putrefaciens</i>
Bibliographic record
Abstract
Acid-base titration experiments and electrostatic force microscopy (EFM) were used to investigate the cell surface electrochemical heterogeneity of the Fe(III)-reducing bacteria, Shewanella putrefaciens. The acid-base titrations extended from pH 4 to 10, and the titration data were fit using a linear programming pKa spectrum approach. Overall, a five-site model accounted for the observed titration behavior with the most acidic sites corresponding to carboxylic groups and phosphodiester groups, intermediate sites phosphoryl groups, and two basic sites equivalent to amine or hydroxyl groups. The pH for the point of zero charge on the bacteria was 5.4. In EFM images of cells rinsed in solutions at pH 4.0, 7.0, and 8.0, a pronounced increase in small (< or = 100 nm diameter) high contrast patches was observed on the cells with increasing pH. The pH dependence of EFM image contrast paralleled the pattern of cell surface charge development inferred from the titration experiments; however, quantitative analysis of high contrast regions in the EFM images yielded lower surface charge values than those anticipated from the titration data. For example at pH 7, the calculated surface charge of high contrast regions in EFM images of the bacterial cells was -0.23 microC/cm2 versus -20.0 microC/cm2 based on the titration curve. The differences in surface charge estimates between the EFM images and titration data are consistent not only with charge development throughout the entire volume of the bacterial cell wall (i.e., in association with functional groups that are not directly exposed at the cell surface) but also with the presence of a thin structural layer of water containing charge-compensating counterions. In combination, the pKa spectra and EFM data demonstrate that a particularly high degree of electrochemical heterogeneity exists within the cell wall and at the cell surface of S. putrefaciens.
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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".