The Key Role of Intrinsic Lifetime Dynamics from Upconverting Nanosystems in Multiemission Particle Velocimetry
Bibliographic record
Abstract
Evaluation of particle dynamics at the nano- and microscale poses a challenge to the development of novel velocimetry techniques. Established optical methods implement external or internal calibrations of the emission profiles by varying the particle velocity and are limited to specific experimental conditions. The proposed multiemission particle velocimetry approach aims to introduce a new concept for a luminescent probe, which guarantees accurate velocity measurements at the microscale, independent of the particle concentration or experimental setup, and without need for calibration. The simplicity of these analyses relies on the intrinsic luminescence dynamics of core-shell upconverting nanoparticles. Upon excitation with a focused near-infrared pulsed laser, the nanoparticle emits photons at different wavelengths. The time interval between emissions from different excited states is independent of the local environment or particle velocity. The velocity of the particles is calculated by measuring the distance between the maxima of two different emissions and dividing it by the known difference in luminescence lifetimes. This method is demonstrated using simple digital imaging of nanoparticles flowing in 75-150 µm diameter capillaries. Using this novel approach typically results in a relative standard deviation of the experimental velocities of 5% or lower without any calibration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".