Ultrasensitive Phosphorescence Sensors
Bibliographic record
Abstract
Fluorescence spectroscopy as the most widely used luminescence-based imaging technique has extended applications in detection systems. However, fluorescence spectroscopy also has some limitations such as a relatively low signal-to-noise ratio in biological applications due to the autofluorescence of living tissues. In this context, the phosphorescence spectroscopy has promising potentials given its higher emission lifetime and Stokes shift compared to the fluorescence, increasing both signal resolution and selectivity. Furthermore, the autofluorescence signal interference issue can also be eliminated by incorporating phosphorophores in imaging and detection applications. Considering their unique characteristics, phosphorescent materials have recently found wide applications ranging from organic light-emitting diodes and photovoltaic cells to sensing systems and bioimaging. In the current chapter, the basic theory of luminescence as well as the main differences between fluorescence and phosphorescence are elaborated. In addition, state-of-the-art applications of phosphorescent materials, particularly room temperature phosphorescence and quantum dots in sensing systems, and general design considerations of these systems are also discussed. Finally, bioimaging based on phosphorescence detection systems is discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".