Influence of Bacterial Physiology on Processing of Selenite, Biogenesis of Nanomaterials and Their Thermodynamic Stability
Bibliographic record
Abstract
sp. MPV1 can convert up to 2.5 mM selenite within 120 h, surviving the challenge posed by high oxyanion concentrations. The data show that thiol-based biotic chemical reaction(s) occur upon bacterial exposure to low selenite concentrations, whereas enzymatic systems account for oxyanion removal when 2 mM oxyanion is exceeded. The selenite bioprocessing produces selenium nanomaterials, whose size and morphology depend on the bacterial physiology. Selenium nanoparticles were always produced by MPV1 cells, featuring an average diameter ranging between 90 and 140 nm, which we conclude constitutes the thermodynamic stability range for these nanostructures. Alternatively, selenium nanorods were observed for bacterial cells exposed to high selenite concentration or under controlled metabolism. Biogenic nanomaterials were enclosed by an organic material in part composed of amphiphilic biomolecules, which could form nanosized structures independently. Bacterial physiology influences the surface charge characterizing the organic material, suggesting its diverse biomolecular composition and its involvement in the tuning of the nanomaterial morphology. Finally, the organic material is in thermodynamic equilibrium with nanomaterials and responsible for their electrosteric stabilization, as changes in the temperature slightly influence the stability of biogenic compared to chemogenic nanomaterials.
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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 | 0.001 |
| 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) | 0.001 | 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".