Volumetric Temperature Mapping Using Light-Sheet Microscopy and Upconversion Fluorescence from Micro- and Nano-Rare Earth Composites
Bibliographic record
Abstract
We present a combination of light-sheet excitation and two-dimensional fluorescence intensity ratio (FIR) measurements as a simple and promising technique for three-dimensional temperature mapping. The feasibility of this approach is demonstrated with samples fabricated with sodium yttrium fluoride nanoparticles co-doped with rare-earth ytterbium and erbium ions (NaYF4:Yb3+/Er3+) incorporated into polydimethylsiloxane (PDMS) as a host material. In addition, we also evaluate the technique using lipid-coated NaYF4:Yb3+/Er3+ nanoparticles immersed in agar. The composite materials show upconverted (UC) fluorescence bands when excited by a 980 nm near-infrared laser light-sheet. Using a single CMOS camera and a pair of interferometric optical filters to specifically image the two thermally-coupled bands (at 525 and 550 nm), the two-dimensional FIR and, hence, the temperature map can be readily obtained. The proposed method can take optically sectioned (confocal-like) images with good optical resolution over relatively large samples (up to the millimetric scale) for further 3D temperature reconstruction.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".