Temperature-Induced Energy Transfer in Dye-Conjugated Upconverting Nanoparticles: A New Candidate for Nanothermometry
Bibliographic record
Abstract
Lanthanide-doped upconverting nanoparticles (UCNPs) are highly promising candidates for bioimaging and for cellular nanothermometry as a novel diagnostic tool. Aiming for the diagnosis of diseases at very early stages in order to optimize therapy and recovery of the patient, it must be taken into account that thermal singularities are often one of the first indicators of a disease. It is therefore our goal to develop a nanothermometer based on UCNPs that is suitable to detect the temperature at a subcellular level in the physiological range. Thus, upconverting NaGdF 4:Er 3+,Yb 3+ nanoparticles that convert near-infrared (NIR) into visible (VIS) light are synthesized by thermal decomposition. Appropriate surface modification with a thermoresponsive polymer pNIPAM (poly( N -isopropylacrylamide)) guarantees dispersibility in aqueous media required for biomedical applications. In a further step, the combination of the obtained UCNPs with an organic dye (FluoProbe532A) provides potential donor-acceptor-pairs allowing for energy transfer processes, whereas the light emitted by the Er 3+ ions (donors) is absorbed by the organic dye (acceptor). It has been demonstrated that the dye-conjugated UCNPs undergo a temperature-dependent energy transfer process inducing a temperature-dependent increase in the thermal sensitivity when compared to unlabeled UCNPs. This result indicates the great potential of the presented nanoprobes for applications in nanothermometry.
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 | 0.001 | 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".