Teaching Microfluidic Diagnostics Using Jell-O® Chips
Bibliographic record
Abstract
Microfluidics has emerged as a versatile technology that has found many applications, including DNA chips, fuel cells, and diagnostics. As the field of microfluidic diagnostics grows, it is important to introduce the principles of this technology to young students and the general public. The objective of this project was to create a simple and effective method that could be used to teach key microfluidics concepts using easily accessible materials. Similar to the poly(dimethylsiloxane) soft lithography technique, a Jell-O(®) "chip" is produced by pouring a mixture of Jell-O(®) and gelatine solution into a mold, which is constructed using foam plate, coffee stirrers, and double-sided tape. The plate is transferred to a 4°C refrigerator for curing, and then the Jell-O(®) chip is peeled off for experimental demonstrations. Three types of chips have been fabricated with different molds: a JELLO mold, a Y-channel mold, and a pH-sensor mold. Using these devices, the basics of microfluidic diagnostics can be demonstrated in one or two class periods. The method described in this chapter provides teachers with a fast and inexpensive way to introduce this technology, and students with a fun and hands-on way to understand the basics of microfluidic diagnostics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".