Biogenic SeNPs from Bacillus mycoides SelTE01 and Stenotrophomonas maltophilia SelTE02: Characterization with reference to their associated organic coating
Bibliographic record
Abstract
The exploitation of biological systems (i.e. plants, fungi and bacteria) for the production of nanomaterials relies on their ability to bioconvert toxic metal(loid) ions into their less toxic and bioavailable elemental states forming mainly nanoparticles (NPs) or nanorods (NRs). Further, these methods of nanomaterial production are nowadays recognized as eco-friendly alternatives to the chemical synthesis processes. A common feature among the so-called biogenic nanomaterials is the presence of an organic layer surrounding them. However, we are just learning the existing relation between biogenic nanostructures and their organic material. Our work is focused on the study of bacterial strains for the production of selenium nanoparticles (SeNPs) as end product of selenite (SeO32−) bioconversion. In this context, our previous reports described the ability of two bacteria, namely Bacillus mycoides SelTE01 and Stenotrophomonas maltophilia SelTE02, to generate SeNPs, which were surrounded by organic material. Here, the potential role of this organic material as stabilizing agent of SeNPs was investigated altering both the bacteria cells culturing and the SeNPs extraction procedure, in order to understand the interaction between these two elements in suspension. As a result, SeNPs produced by both bacterial strains showed the tendency to aggregate when subjected to the treatments tested, suggesting an involvement of the surrounding organic material in their stabilization in suspension.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".