Facile biosynthesis, separation and conjugation of gold nanoparticles to doxorubicin
Bibliographic record
Abstract
Particle shape and size determine the physicochemical and optoelectronic properties of nanoscale materials, including optical absorption, fluorescence, and electric and magnetic moments. It is thus desirable to be able to synthesize and separate various particle shapes and sizes. Biosynthesis using microorganisms has emerged as a more ecologically friendly, simpler, and more reproducible alternative to chemical synthesis of metal and semiconductor nanoparticles, allowing the generation of rare forms such as triangles. Here we show that the plant pathogenic fungus Helminthosporum solani, when incubated with an aqueous solution of chloroaurate ions, produces a diverse mixture of extracellular gold nanocrystals in the size range from 2 to 70 nm. A plurality are polydisperse spheres, but a significant number are homogeneously sized rods, triangles, pentagons, pyramids, and stars. The particles can be separated according to their size and shape by using a sucrose density gradient in a tabletop microcentrifuge, a novel and facile approach to nanocrystal purification. Conjugation to biomolecules can be performed without further processing, as illustrated with the smallest fraction of particles which were conjugated to the anti-cancer drug doxorubicin (Dox) and taken up readily into HEK293 cells. The cytotoxicity of the conjugates was comparable to that of an equivalent concentration of Dox.
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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) | 0.000 | 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".