Double rare-earth nanothermometer in aqueous media: opening the third optical transparency window to temperature sensing
Bibliographic record
Abstract
Owing to the alluring possibility of contactless temperature probing with microscopic spatial resolution, photoluminescence nanothermometry at the nanoscale is rapidly advancing towards its successful application in biomedical sciences. The emergence of near-infrared nanothermometers has paved the way for temperature sensing at the deep tissue level. However, water dispersibility, adequate size at the nanoscale, and the capability to efficiently operate in the second and third biological optical transparency windows are the requirements that still have to be fulfilled in a single nanoprobe. In this work, these requirements are addressed by rare-earth doped nanoparticles with core/shell-architecture, dispersed in water, whose excitation and emission wavelengths conveniently fall within the biological optical transparency windows. Under heating-free 800 nm excitation, double nanothermometry is realized either with Ho3+–Nd3+ (1.18–1.34 μm) or Er3+–Nd3+ (1.55–1.34 μm) NIR emission band ratios, both displaying equal thermal sensitivities around 1.1% °C−1. It is further demonstrated that, along with the interionic energy transfer processes, the thermometric properties of these nanoparticles are also governed by the temperature dependent energy transfer to the surrounding solvent (water) molecules. Overall, this work presents a novel water dispersible double ratiometric nanothermometer operating in the second and third biological optical transparency windows. The temperature dependent particle–solvent interaction is also presented, which is critical for e.g. future in vivo 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".