Encapsulation of Dual Emitting Giant Quantum Dots in Silica Nanoparticles for Optical Ratiometric Temperature Nanosensors
Bibliographic record
Abstract
Accurate temperature measurements with a high spatial resolution for application in the biomedical fields demand novel nanosized thermometers with new advanced properties. Here, a water dispersible ratiometric temperature sensor is fabricated by encapsulating in silica nanoparticles, organic capped PbS@CdS@CdS “giant” quantum dots (GQDs), characterized by dual emission in the visible and near infrared spectral range, already assessed as efficient fluorescent nanothermometers. The chemical stability, easy surface functionalization, limited toxicity and transparency of the silica coating represent advantageous features for the realization of a nanoscale heterostructure suitable for temperature sensing. However, the strong dependence of the optical properties on the morphology of the final core–shell nanoparticle requires an accurate control of the encapsulation process. We carried out a systematic investigation of the synthetic conditions to achieve, by the microemulsion method, uniform and single core silica coated GQD (GQD@SiO2) nanoparticles and subsequently recorded temperature-dependent fluorescent spectra in the 281-313 K temperature range, suited for biological systems. The ratiometric response—the ratio between the two integrated PbS and CdS emission bands—is found to monotonically decrease with the temperature, showing a sensitivity comparable to bare GQDs, and thus confirming the effectiveness of the functionalization strategy and the potential of GQD@SiO2 in future biomedical applications.
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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".