Erbium Single‐Band Nanothermometry in the Third Biological Imaging Window: Potential and Limitations
Bibliographic record
Abstract
Abstract Near‐infrared (NIR) nanothermometers are sought after in biomedicine when it comes to measuring temperatures subcutaneously. Yet, temperature sensing within the third biological imaging window (BW‐III), where the highest contrast images can be obtained, remains relatively unexplored. Here, LiErF 4 /LiYF 4 rare‐earth nanoparticles (RENPs) are studied as NIR nanothermometers in the BW‐III. Under 793 nm excitation, LiErF 4 /LiYF 4 RENPs emit around 1540 nm, corresponding to the 4 I 13/2 → 4 I 15/2 radiative transition of Er 3+ . The fine Stark structure of this transition allows to delineate intensity regions within the emission band that can be used for single‐band ratiometric nanothermometry. These nanothermometers have a relative temperature sensitivity of ≈0.40% °C −1 . The temperature‐dependent energy transfer to the surrounding solvent molecules plays a significant role in the thermometric properties of the RENPs. In addition, Ce 3+ ions are doped in the core of the RENPs to examine whether it affects the NIR emission and temperature sensitivity. Ce 3+ at 1 mol% marginally influences the downshifting emission intensity of the RENPs, yet increases the relative thermal sensitivity to ≈0.45% °C −1 . Furthermore, Ce 3+ quenches the visible upconversion emission of the RENPs. Together, LiErF 4 :Ce 3+ /LiYF 4 RENPs enable single‐band photoluminescence nanothermometry in the BW‐III, with the future possibility of its integration within multifunctional decoupled theranostic nanostructures.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".