A microfluidic optical platform for real-time monitoring of pH and oxygen in microfluidic bioreactors and organ-on-chip devices
Bibliographic record
Abstract
There is a growing interest to develop microfluidic bioreactors and organ-on-chip platforms with integrated sensors to monitor their physicochemical properties and to maintain a well-controlled microenvironment for cultured organoids. Conventional sensing devices cannot be easily integrated with microfluidic organ-on-chip systems with low-volume bioreactors for continual monitoring. This paper reports on the development of a multi-analyte optical sensing module for dynamic measurements of pH and dissolved oxygen levels in the culture medium. The sensing system was constructed using low-cost electro-optics including light-emitting diodes and silicon photodiodes. The sensing module includes an optically transparent window for measuring light intensity, and the module could be connected directly to a perfusion bioreactor without any specific modifications to the microfluidic device design. A compact, user-friendly, and low-cost electronic interface was developed to control the optical transducer and signal acquisition from photodiodes. The platform enabled convenient integration of the optical sensing module with a microfluidic bioreactor. Human dermal fibroblasts were cultivated in the bioreactor, and the values of pH and dissolved oxygen levels in the flowing culture medium were measured continuously for up to 3 days. Our integrated microfluidic system provides a new analytical platform with ease of fabrication and operation, which can be adapted for applications in various microfluidic cell culture and organ-on-chip devices.
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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 | 0.001 | 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 | 0.000 | 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".