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Record W2127237813 · doi:10.1039/c2lc21004e

Virtual microwells for digital microfluidic reagent dispensing and cell culture

2011· article· en· W2127237813 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLab on a Chip · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsReagentMicrofluidicsNanotechnologyMaterials scienceChemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.186
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it