Microtiter Plate-Based Microfluidic Platforms: Sealing, Leakage Testing, and Performance of a 96-Well SPRI Device
Bibliographic record
Abstract
Highly parallelized biochemical analysis is a significant step toward achieving high throughput processing of patient samples for diagnosis and treatment monitoring. The standard microtiter plate is used to carry out multiple reactions for high throughput screening. By incorporating polymer microfluidic devices at each well in the microtiter plate format, the capability of the format could be significantly enhanced for high throughput processing of large numbers of biochemical samples in a cost-effective manner. Low cost replication of the microtiter plates is done using micro molding techniques, so microfabrication technology for making large area mold inserts (LAMIs) containing microfluidic devices at each well of a microtiter plate format is needed. A large area mold insert (LAMI) in the footprint of the standard microtiter plate was fabricated using an SU-8 based UV-LIGA technique. Excellent lithography results, with vertical sidewalls, were obtained by utilizing flycutting to minimize SU-8 film thickness variation and a UV filter for attenuating high absorbance UV wavelengths. Overplating of nickel in the SU-8 polymeric molds was used to make high quality metallic mold inserts with vertical sidewalls. Micro molding of polycarbonate (PC) was done using hot embossing, resulting in good replication fidelity over the large surface area. Thermal fusion bonding of the molded PC chips yielded good sealing results and the developed polymer microfluidic platforms showed good fluidic uniformity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".