Integration of optical and electrochemical sensors on a microfluidic platform using organic optoelectronic components and silver nanowires
Bibliographic record
Abstract
Since the emergence of microfluidic platforms sensors integration has been a major challenge. With the advances in miniaturization of these platforms, there is a need for solutions to integrate various optical components in order to build sensors, which will offer different detection characteristics such as several emission and sensing wavelengths. Moreover, the integration of an electrochemical sensor including a transparent electrode that will be compatible with the optical sensor represents an additional challenge. In this perspective, organic optoelectronic devices combined with silver nanowire electrodes could be a solution. The integration of a fluorescent sensor and an electrochemical oxygen sensor into a microfluidic platform and the different characteristics, advantages and disadvantages that offer organic light-emitting diodes (OLED), organic photodetectors (OPD) and silver nanowire electrodes are discussed. Finally, an example of the integration of an optical and an electrochemical sensor into a microfluidic chip for water pollution detection will be described.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".