Ratiometric temperature measurement using negative thermal quenching of intrinsic BiFeO3 semiconductor nanoparticles
Bibliographic record
Abstract
Manuscript published in the Open Access Journal: Željka Antić, K. Prashanthi, Sanja Kuzman, Jovana Periša, Zoran Ristić, V. R. Palkarc and Miroslav D. Dramićanin, Ratiometric temperature measurement using negative thermal quenching of intrinsic BiFeO<sub>3</sub> semiconductor nanoparticles, RSC Adv., 2020, 10, 16982. https://doi.org/10.1039/D0RA01896A A strategy for optical nanothermometry using the negative thermal quenching behavior of intrinsic BiFeO3 semiconductor nanoparticles has been reported here. X-ray diffraction measurement shows polycrystalline BiFeO3 nanoparticles with a rhombohedral distorted perovskite structure. Transmission electron microscopy shows agglomerated crystalline nanoparticles around 20 nm in size. Photoluminescence measurements show that intensity of the defect level emission increases significantly with temperature, while the intensity of near band emission and other defect levels emissions show an opposite trend. The most important figures of merit for luminescence nanothermometry: the absolute (Sa) and the relative sensor sensitivity (Sr) and the temperature resolution (DTm) were effectively resolved and calculated. The relative sensitivity and temperature resolution values are found to be 2.5% K1 and 0.2 K, respectively which are among the highest reported values observed so far for semiconductors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; both teacher heads agree on what is shown here.
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".