Microfluidics on lensless, semiconductor optical image sensors: challenges and opportunities for democratization of biosensing at the micro‐and nano‐scale
Bibliographic record
Abstract
Optical image sensors are 2D arrays of pixels that integrate semiconductor photodiodes and field effect transistors for efficient photon conversion and processing of generated electrons. With technological advancements and subsequent democratization of these sensors, opportunities for integration with microfluidics devices are currently explored. 2D pixel arrays of such optical image sensors can reach dimensions larger than one centimeter with a sub-micrometer pixel size, for high spatial resolution lensless imaging with large field of view, a feat that cannot be achieved with lens-based optical microscopy. Moreover, with advancements in fabrication processes, the field of microfluidics has evolved to develop microfluidic devices with an overall size below one centimeter and individual components of sub-micrometer size, such that they can now be implemented onto optical image sensors. The convergence of these fields is discussed in this article, where we review fundamental principles, opportunities, challenges, and outlook for integration, with focus on contact-mode imaging configuration. Most recent developments and applications of microfluidic lensless contact-based imaging to the field of biosensors, in particular those related to the potential for point of need applications, are also discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".