Biomolecular composition of capping layer and stability of biogenic selenium nanoparticles synthesized by five bacterial species
Bibliographic record
Abstract
Biogenic metal/metalloid nanoparticles of microbial origin retain a functional biomolecular capping layer that confers structural stability. Little is known about the composition of such capping material. In this study, selenium nanoparticles (SeNPs) synthesized by five different bacterial strains underwent comparative analysis with newly proposed protocols for quantifying the concentration of carbohydrates, proteins and lipids present in capping layers. SeNPs were therefore treated with two different detergents to remove portions of the surrounding caps in order to assess the resulting effects. Capping material quantification was carried out along with the measure of parameters such as hydrodynamic diameter, polydispersity and surface charge. SeNPs from the five strains showed differences in their distinct biomolecule ratios. On the other hand, structural changes in the nanoparticles induced by detergents did not correlate with the amounts of capping matrix removed. Thus, the present investigation suggests a hypothesis to describe capping layer composition of the bacterial SeNPs: some biomolecules are bound more strongly than others to the core metalloid matrix, so that the diverse capping layer components differentially contribute to the overall structural characteristics of the nanoparticles. Furthermore, the application of the approach here in combining quantification of cap-associated biomolecules with the measurement of structural integrity-related parameters can give the biogenic nanomaterial field useful information to construct a data bank on biogenically synthesized nanostructures.
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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) | 0.000 | 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".