Praseodymium-Doped Nanoparticles: Candidates for Near-Infrared-II Double- and Single-Band Nanothermometry
Bibliographic record
Abstract
Luminescent nanothermometers with high spatial and thermal resolution are desirable for a wide range of temperature-sensing applications. Lanthanide-doped nanoparticles can be excited by and emit light in the near-infrared (NIR) region, rendering them ideal candidates for NIR nanothermometry. Pr 3+ has demonstrated thermal sensing ability in the NIR region but only when excited by UV–visible light. Here, we propose a series of NIR-I excited Pr 3+, Ho 3+, and Yb 3+ -doped NaGdF 4 core/multishell nanoparticles for double- and single-band ratiometric nanothermometry operating in the NIR-II region. Thermal sensing ability was demonstrated in organic and aqueous dispersions as well as for powders. The S r based on the Pr 3+ 1 G 4 → 3 H 5 and Ho 3+ 5 I 6 → 5 I 8 radiative transitions reached 2.4% °C –1 at 40 °C, while the maximal S r obtained using the single-band Pr 3+ emission was 0.8% °C –1 at 10 °C. These findings demonstrate the promise of Pr 3+ -doped nanoparticles as NIR-NIR nanothermometers for thermal sensing applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".