Low-picomolar limits of detection using high-power light-emitting diodes for fluorescence
Bibliographic record
Abstract
Fluorescence detectors are ever more frequently being used with light-emitting diodes (LEDs) as the light source. Technological advances in the solid-state lighting industry have produced LEDs which are also suitable tools in analytical measurements. LEDs are now available which deliver 700 mW of radiometric power. While this greater light power can increase the fluorescence signal, it is not trivial to make proper use of this light. This new generation of LEDs has a large emitting area and a highly divergent beam. This presents a classic problem in optics where one must choose between either a small focused light spot, or high light collection efficiency. We have selected for light collection efficiency, which yields a light spot somewhat larger than the emitting area of the LED. This light is focused onto a flow cell. Increasing the detector cell internal diameter (i.d.) produces gains in (sensitivity)3. However, since the detector cell i.d. is smaller than the LED spot size, scattering of excitation light towards the detector remains a significant source of background signal. This can be minimized through the use of spectral filters and spatial filters in the form of pinholes. The detector produced a limit of detection (LOD) of 3 pM, which is roughly three orders of magnitude lower than other reports of LED-based fluorescence detectors. Furthermore, this LOD comes within a factor of six of much more expensive laser-based fluorescence systems. This detector has been used to monitor a separation from a gel filtration column of fluorescently labeled BSA from residual labeling reagent. The LOD of fluorescently labeled BSA is 25 pM.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".