Mechanically Activated Solvent-Free Assembly of Ultrasmall Bi<sub>2</sub>S<sub>3</sub> Nanoparticles: A Novel, Simple, and Sustainable Means To Access Chalcogenide Nanoparticles
Bibliographic record
Abstract
Nanosized Bi 2 S 3 particles are one of the most promising nanomaterials for biomedical imaging, due to a unique combination of low toxicity and the high electron count of bismuth. However, the study and use of nano-sized Bi 2 S 3 have been prevented by major synthetic challenges, including sensitivity to air and harsh solvothermal conditions. Herein, we describe a novel and surprisingly simple pathway, based on solid-state bottom-up self-assembly, to access the elusive Bi 2 S 3 nanoparticles functionalized with surface ligands that allow dispersion in either organic or aqueous media. This one-pot, room-temperature synthesis utilizes mechanical activation in the solid state, either by milling or even manual grinding, to induce the spontaneous assembly of monodisperse 2 nm diameter nanoparticles applicable for CT imaging in physiological media. This solvent-free methodology utilizes readily available precursors to provide unprecedented one-step access to monodisperse nano-Bi 2 S 3 in multigram amounts, including polyethylene glycol (PEG)-coated nanoparticles, without the need for controlled atmospheres or solvothermal treatment.
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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 | 0.001 |
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".