Short-Term Inactivation Rates of Selected Gram-Positive and Gram-Negative Bacteria Attached to Metal Oxide Mineral Surfaces: Role of Solution and Surface Chemistry
Bibliographic record
Abstract
Metal oxides such as ferric or aluminum oxides can play an important role in the retention of bacteria in granular aquatic environments; however, their role in bacterial inactivation is not well understood. Herein, we examined the role of water chemistry and surface chemistry on the short-term inactivation rates of three bacteria when adhered to surfaces. To evaluate the role of water chemistry on the inactivation of attached bacteria, the loss in membrane integrity of bacteria attached to an iron oxide (Fe2O3) surface was measured over a range of water ionic strengths of either monovalent or divalent salts in the absence of a growth substrate. The influence of surface chemistry on the inactivation of attached bacteria was examined by measuring the loss in membrane integrity of cells attached to three surfaces (SiO2, Fe2O3, and Al2O3) at a specific water chemistry (10 mM KCl). Bacteria were allowed to attach onto the SiO2 or metal oxide coated slides mounted in a parallel-plate flow cell, and their inactivation rate (loss in membrane integrity) was measured directly without removing the cells from the surface and without disturbing the system. X-ray photoelectron spectroscopy analysis revealed a high correlation between the amounts of C-metal or O-metal bonds and the corresponding bacterial inactivation rates for each surface. Finally, for all three surfaces, a consistent increase in inactivation rate was observed with the type of bacterium in the order: Enterococcus faecalis, Escherichia coli O157:H7, and Escherichia coli D21f2.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".