Bacterial Stern layer diffusion: experimental determination with spectral induced polarization and sensitivity to nitrite toxicity
Bibliographic record
Abstract
ABSTRACT Spectral induced polarization signatures have been used as proxies for microbial abundance in subsurface environments, by taking advantage of the charged properties of microbial cell membranes. The method's applicability, however, remains qualitative, and signal interpretation ambiguous. The adoption of spectral induced polarization as a robust geo‐microbiological tool for monitoring microbial dynamics in porous media requires the development of quantitative relationships between biogeochemical targets and spectral induced polarization parameters, such as biomass density and imaginary conductivity ( σ″ ). Furthermore, deriving cell density information from electrical signals in porous media necessitates a detailed understanding of the nature of the cell membrane surface charge dynamics. We present results from a fully saturated sand‐filled column reactor experiment where Shewanella oneidensis growth during nitrate reduction to ammonium was monitored using spectral induced polarization. While our results further confirm the direct dependence of σ″ on changing cell density, Cole–Cole derived relaxation times also record the changing surface charging properties of the cells, ascribed to toxic stress due to nitrite accumulation. Concurrent estimates of cell size yield the first measurement‐derived estimation of the apparent surface ion diffusion coefficient for cells ( D s = 5.4 ±1.3 µm 2 s −1 ), strengthening the link between spectral induced polarization and electrochemical cell polarization. Our analysis provides a theoretical framework on which to build σ″ –cell density relations using bench‐scale experiments, leading to eventual robust non‐destructive monitoring of in situ microbial growth dynamics.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".