Hyperspectral Imaging and Optical Trapping: Complementary Tools for Assessing Direction‐Dependent Polarized Emission from Single Upconverting LiYF<sub>4</sub>:Yb<sup>3+</sup>/Er<sup>3+</sup> Microparticles
Bibliographic record
Abstract
Abstract Single‐particle fluorescent probes with the capacity to infer specific intracellular conditions, for instance, have great application potential in the realm of biomedicine. Imaging techniques that improve our understanding of the fluorescence processes at a single‐particle level are thus instrumental in actualizing this potential. This study demonstrates the importance of implementing synergistic single‐particle spectroscopic techniques to gain a more comprehensive understanding of the optical anisotropy exhibited by upconverting erbium and ytterbium co‐doped lithium yttrium tetrafluoride (LiYF4:Yb3+/Er3+) microparticles. More specifically, optical trapping and single‐particle polarized emission spectroscopy is herein leveraged to provide a plausible explanation for the spatial emission intensity distribution variation exhibited by LiYF4:Yb3+/Er3+ microparticles during hyperspectral imaging. By probing the polarized emission stemming from a single, optically trapped LiYF4:Yb3+/Er3+ microparticle, it is possible to find evidence that the emission intensity anisotropy exhibited by the respective microparticles during hyperspectral imaging arises as a consequence of the selection rules governing the emission probability in rare‐earth (RE3+) ions doped into a uniaxially birefringent host matrix such as LiYF4.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".