Mercaptosilane-Passivated CuInS<sub>2</sub> Quantum Dots for Luminescence Thermometry and Luminescent Labels
Bibliographic record
Abstract
Bright and nontoxic quantum dots (QDs) are highly desirable in a variety of applications, from solid-state devices to luminescent probes in assays. However, the processability of these species is often curbed by their surface chemistry, which limits their dispersibility in selected solvents. This renders a surface modification step often mandatory to make the QDs compatible with the solvent of interest. Here, we present a new synthetic approach to produce CuInS 2 QDs compatible with organic polar solvents and readily usable for the preparation of composite materials. 3-Mercaptopropyl trimethoxysilane (MPTS) was used simultaneously as solvent, sulfur source, and capping agent for the QD synthesis. The synthesized QDs possessed a maximum photoluminescence quantum yield around 6%, reaching approximately 55% after growing a ZnS shell. The partial condensation of MPTS molecules on the surface of QDs was probed by solid-state nuclear magnetic resonance, whose results were used to interpret the interaction of the QDs with different solvents. To prove the versatility of the developed QDs, imparted by the thiolated silane molecules, we prepared via straightforward procedures two nanocomposites of practical interest: (i) silica nanoparticles decorated with QDs and (ii) an inexpensive polymeric film with embedded QDs. We further demonstrate the potential of this composite film as a luminescence thermometer operational over a broad temperature interval, with relative thermal sensitivity above 1% K –1 in the range of biological interest.
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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) | 0.001 | 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".