Anatase Nanoparticles for Raman Nanothermometry
Bibliographic record
Abstract
The determination of the local temperature is an interesting and intriguing topic in the nanotechnology and nanomedicine world, in terms of tuning the best noninvasive measurement protocol and identification of the more versatile and performing material.In this paper, the Raman technique and titania NPs have been exploited for the realization of a new optical nanotermometer.Biocompatible titania NPs have been properly synthesized, following a combination of sol-gel and solvothermal green synthesis approaches, with the aim of obtaining samples of pure anatase, characterized by crystallite dimensions defined and good control over the final morphology and dispersibility.Powder XRD measurements and room temperature Raman measurements confirmed that the synthesized samples are single-phase anatase.The SEM images clearly showed the nanometric dimension of NPs.Stokes and anti-Stokes Raman measurements, collected with the excitation laser at 514.5 nm (CW Ar/Kr ion laser), substantiate the possibility of evaluating the local temperature, which has been tested in the range of 298 -313 K, a range of interest for biological applications.The power of the laser has been carefully chosen in order to avoid eventual heating due to the laser irradiation.The data show that TiO2 NPs possess a high sensitivity and low uncertainty in the range of a few degrees as Raman nanothermometer material.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".