Untangling heat transport dynamics using luminescence nanothermometry
Bibliographic record
Abstract
Abstract Temperature touches all aspects of our daily life, including climate, production plants, food storage, transportation, metrology, microelectronics, and medicine, and is a major factor dictating performance of nanotechnologies.1-4However, while the heat transfer is well understood in bulk, neither experimental nor theoretical models provide a complete picture of the thermal dynamics at the nanoscale.5-7Here, in situ luminescence thermometry is used to probe the heat propagation taking place within lanthanide (Ln3+)-doped upconverting nanoparticles (UCNPs). We have designed UCNPs with Er3+and Tm3+thermometric layers positioned at different locations relative to their surface, varying the distance a heat wave travels before encountering the layers. Despite being separated only by a few tens of nanometers, the thermometric layer closer to the surface of UCNPs detects temperature increase much earlier than the one located at the center – yielding the heat propagation speed in UCNPs ~1.3 nm/s. The UCNPs featuring the two thermometric layers in a single nanostructure confirmed the above result and allowed us to uncover diffusive and non-diffusive (ballistic) heat transport regimes, as well as their interplay and complex heat exchange dynamics taking place in colloidal nanoparticles (nanofluids) at a room temperature.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".