Fabrication of an integrated 670nm VCSEL-based sensor for miniaturized fluorescence sensing
Bibliographic record
Abstract
Integrated optical semiconductor sensors are a promising technology for both lab-on-a-chip and molecular imaging applications due to their low cost, small size, high sensitivity, and flexible designs. We present the design and fabrication of a GaAs-based monolithically integrated fluorescence sensor incorporating 670nm VCSELs and PIN photodetectors. This is the first integrated, VCSEL-based fluorescence sensor with excitation at a far-red wavelength and is specifically designed for in vivo sensing applications. In addition, we discuss considerations to simultaneously achieve high power VCSELs and low dark current PIN photodetectors required for sensitive fluorescence detection. These fabricated sensors incorporate 670nm VCSELs emitting 2.0mW at room temperature (RT) with adjacent detectors exhibiting RT dark less than 2pA/mm2 (100mV reverse bias). Fluorescence emission filters suitable for transmitting Cy5.5 fluorescent dye emission were integrated with the photodetectors. The sensor detects Cy5.5 molecules in vitro at 5nM concentration with linear response for concentrations up to 25μM. These miniature sensors are suitable for portable diagnostic assays and in vivo rodent studies.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".