Short‐wave Infrared Photoluminescence Lifetime Mapping of Rare‐Earth Doped Nanoparticles Using All‐Optical Streak Imaging
Bibliographic record
Abstract
Abstract The short‐wave infrared (SWIR) photoluminescence lifetimes of rare‐earth doped nanoparticles (RENPs) have found diverse applications in fundamental and applied research. Despite dazzling progress in the novel design and synthesis of RENPs with attractive optical properties, existing optical systems for SWIR photoluminescence lifetime imaging are still considerably restricted by inefficient photon detection, limited imaging speed, and low sensitivity. To overcome these challenges, SWIR photoluminescence lifetime imaging microscopy using an all‐optical streak camera (PLIMASC) is developed. Synergizing scanning optics and a high‐sensitivity InGaAs CMOS camera, SWIR‐PLIMASC has a 1D imaging speed of up to 138.9 kHz in the spectral range of 900–1700 nm, which quantifies the photoluminescence lifetime of RENPs in a single shot. A 2D photoluminescence lifetime map can be acquired by 1D scanning of the sample. To showcase the power of SWIR‐PLIMASC, a series of core‐shell RENPs with distinct SWIR photoluminescence lifetimes is synthesized. In particular, using Er 3+ ‐doped RENPs, SWIR‐PLIMASC enables multiplexed anti‐counterfeiting. Leveraging Ho 3+ ‐doped RENPs as temperature indicators, this system is applied to SWIR photoluminescence lifetime‐based thermometry. Opening up a new avenue for efficient SWIR photoluminescence lifetime mapping, this work is envisaged to contribute to advanced materials characterization, information science, and biomedicine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".