Virtual microwells for digital microfluidic reagent dispensing and cell culture
Bibliographic record
Abstract
Digital microfluidic (DMF) liquid handling includes active (electrostatic) and passive (surface tension) mechanisms for reagent dispensing. Here we implement a simple and straightforward Teflon-AF liftoff protocol for patterning hydrophilic sites on a two-plate device for precise passive dispensing of reagents forming virtual microwells--an analogy to the wells found on a microtitre plate. We demonstrate here that devices formed using these methods are capable of reproducible dispensing of volumes ranging from ~80 to ~800 nL, with CVs of 0.7% to 13.8% CV. We demonstrate that passive dispensing is compatible with DMF operation in both air and oil, and provides for improved control of dispensed nano- and micro- litre volumes when compared to active electrostatic dispensing. Further, the technique is advantageous for cell culture and we report the first example of reagent dispensing on a single-plate DMF device. We anticipate this method will be useful for a wide range of applications--particularly those involving adherent cell culture and analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".