Biomineralization of BSA-Chalcogenide Bioconjugate Nano- and Microcrystals
Bibliographic record
Abstract
Lead selenide (PbSe), cadmium selenide (CdSe), and selenium (Se) nano- and microcrystals were synthesized by using respective metal acetate salts along with sodium selenite as the Se source in the presence of bovine serum albumen (BSA) as the capping/stabilizing agent. Aqueous phase hydrazine reduction at 85 °C produced fine crystalline morphologies within 48 h. Both PbSe and CdSe reactions produced Se microrods (MRs) as reaction byproduct. The concentrations of metal acetate and sodium selenite used were always 1:1 (i.e., 1.25 mM in each case) and that of hydrazine was fixed at 0.78 M. The amount of BSA was changed systematically from 1−10 × 10 −4 g/mL to determine its influence on the crystal growth of these chalcogenides. Their morphologies and chemical compositions were determined with FESEM, TEM, and EDX analysis. A selective and precise EDX analysis of a single particle helped us to elucidate its shape and chemical composition. Such analyses lead to the finding that both reactions produced Se rods, their sizes varied from the nano to micro scale with an increase of the amount of BSA. PbSe polyhedral nanocrystals were obtained at a low BSA amount, which ultimately attained the shape of thick MRs. However, no rod formation was observed for CdSe particles, which were always present in the form of groups of small nanoparticles along with Se MRs. Protein estimation indicated the presence of adsorbed BSA on the surface of chalcogenide particles. A potential reaction mechanism was proposed to explain the Se MRs formation as byproduct. Finally, the results were discussed on the basis of selective adsorption of denatured BSA on specific crystal planes of the rock salt (PbSe) geometry in order to produce rod like morphologies.
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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 | 0.000 |
| 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".