Measuring temperature heterogeneities during solar-photothermal heating using quantum dot nanothermometry
Bibliographic record
Abstract
Small metallic nanoparticles with appropriate surface plasmon resonance frequencies can be extremely efficient absorbers of solar radiation. This efficient absorption can lead to localized heating and highly heterogeneous temperatures. These unique optical properties have inspired research into the development of environmentally relevant solar-to-heat conversion technologies that are based on the light absorption of nanomaterials. The development of robust, reliable, and straight-forward techniques for measuring spatially resolved temperatures in photothermally heated systems can be an indispensable tool to aid future work in this area. Herein, we consider the application of a fluorescent technique that can measure spatially resolved temperatures in solar photothermal systems using CdSe quantum dots (<10 nm diameter). The local temperature of the quantum dot can be determined by monitoring the shift in its fluorescence wavelength resulting from the dilatation of the lattice with increasing temperature. To exploit this property, we fabricated Au nanorod-quantum dot architectures using linkers of varying lengths, and measured the light induced temperature change increasing more rapidly closer to the surface of an Au nanorod. We also compared the effect of Au nanorod coatings and found that silica coating leads to higher overall temperatures compared to organic stabilized Au nanorods.
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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".