The Intersection of CMOS Microsystems and Upconversion Nanoparticles for Luminescence Bioimaging and Bioassays
Bibliographic record
Abstract
Organic fluorophores and quantum dots are ubiquitous as contrast agents for bio-imaging and as labels in bioassays to enable the detection of biological targets and processes. Upconversion nanoparticles (UCNPs) offer a different set of opportunities as labels in bioassays and for bioimaging. UCNPs are excited at near-infrared (NIR) wavelengths where biological molecules are optically transparent, and their luminesce in the visible and ultraviolet (UV) wavelength range is suitable for detection using complementary metal-oxide-semiconductor (CMOS) technology. These nanoparticles provide multiple sharp emission bands, long lifetimes, tunable emission, high photostability, and low cytotoxicity, which render them particularly useful for bio-imaging applications and multiplexed bioassays. This paper surveys several key concepts surrounding upconversion nanoparticles and the systems that detect and process the corresponding luminescence signals. The principle of photon upconversion, tuning of emission wavelengths, UCNP bioassays, and UCNP time-resolved techniques are described. Electronic readout systems for signal detection and processing suitable for UCNP luminescence using CMOS technology are discussed. This includes recent progress in miniaturized detectors, integrated spectral sensing, and high-precision time-domain circuits. Emphasis is placed on the physical attributes of UCNPs that map strongly to the technical features that CMOS devices excel in delivering, exploring the interoperability between the two technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.002 |
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".