Experimental study of the pH, ionic strength, and reversibility behavior of bacteria–mineral adsorption
Bibliographic record
Abstract
In this study, we investigate the adsorption of Bacillus subtilis onto the surfaces of two minerals, corundum and quartz, as a function of time, pH, ionic strength, and bacteria:mineral mass ratio. Experimental results indicate that the adsorption of bacteria onto a mineral surface is a completely reversible process with equilibrium being reached in less than 1 h. Our data also indicate that B. subtilis displays a higher affinity for corundum surfaces than for quartz surfaces, and that the extent of bacteria adsorption onto corundum increases with decreasing pH, with increasing bacteria:mineral mass ratio, and with decreasing ionic strength. The adsorption behavior is governed by the chemical speciation of the bacterial and mineral surfaces. We describe the experimental results with a chemical equilibrium model. The model accounts for hydrophobic and electrostatic interactions that occur between the bacteria and mineral surfaces, and can account for the effects of solution chemistry as well as surface speciation on the extent of adsorption. These results are the first to integrate the effects of pH, ionic strength, and bacteria:mineral ratio in a quantitative model. Such an approach enables bacteria-mineral adsorption reactions to be incorporated into more standard water-rock speciation models, providing a better understanding of mass transport in both natural and bio-engineered bacteria-bearing geochemical systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".