Biomimetic, Mild Chemical Synthesis of CdTe-GSH Quantum Dots with Improved Biocompatibility
Bibliographic record
Abstract
Multiple applications of nanotechnology, especially those involving highly fluorescent nanoparticles (NPs) or quantum dots (QDs) have stimulated the research to develop simple, rapid and environmentally friendly protocols for synthesizing NPs exhibiting novel properties and increased biocompatibility. In this study, a simple protocol for the chemical synthesis of glutathione (GSH)-capped CdTe QDs (CdTe-GSH) resembling conditions found in biological systems is described. Using only CdCl(2), K(2)TeO(3) and GSH, highly fluorescent QDs were obtained under pH, temperature, buffer and oxygen conditions that allow microorganisms growth. These CdTe-GSH NPs displayed similar size, chemical composition, absorbance and fluorescence spectra and quantum yields as QDs synthesized using more complicated and expensive methods.CdTe QDs were not freely incorporated into eukaryotic cells thus favoring their biocompatibility and potential applications in biomedicine. In addition, NPs entry was facilitated by lipofectamine, resulting in intracellular fluorescence and a slight increase in cell death by necrosis. Toxicity of the as prepared CdTe QDs was lower than that observed with QDs produced by other chemical methods, probably as consequence of decreased levels of Cd(+2) and higher amounts of GSH. We present here the simplest, fast and economical method for CdTe QDs synthesis described to date. Also, this biomimetic protocol favors NPs biocompatibility and helps to establish the basis for the development of new, "greener" methods to synthesize cadmium-containing QDs.
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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".