(Invited) Exploring in the Near Infrared: From Quantum Dots to Rare-Earth Doped Nanoparticles
Bibliographic record
Abstract
Bioimaging shows great potential for disease detection due to its high sensitivity at the subcellular level and low cost of related imaging facilities. As compared to most commonly used optical probes that are excited and emit in the visible wavelength range, near-infrared (NIR) excitable and emitting nanomaterials are indeed more promising for bioimaging. It is because at certain NIR wavelengths (known as biological windows) tissues are basically optically transparent and show minimal scattering and absorption, allowing for deep tissue imaging. Meanwhile, tissue autofluorescence can also be avoided. In this talk, I will present our recent work [1-4] on the synthesis of NIR-emitting water soluble, stable core/shell/shell quantum dots (QDs) and rare-earth doped nanoparticles, and their use in biomedicine, including but not limited to bioimaging. References: [1] ACS Nano 2019, 13, 408-420; [2] Adv. Funct. Mater. 2018, 1706235 (Inside Back Cover); [3] Chem. Mater. 2019, 31, 3201-3210. [4] Science Advances, 2021, 7;7(15):eabc3012
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".