Microfluidics for Processing Surfaces and Miniaturizing Biological Assays
Bibliographic record
Abstract
Abstract This review is an account of our efforts to develop a versatile and flexible microfluidic technology for surface‐processing applications and miniaturizing biological assays. The review is presented in the context of current trends in microfluidic technology and addresses some of the major challenges for confining chemical and biochemical processes on surfaces: the sealing of a microchannel with a surface, the world‐to‐chip interface, the displacement of liquids in small conduits, the sequential delivery of multiple solutions, the accurate patterning of surfaces, the coincident detection of various analytes, and the detection of analytes in a small and dilute sample. Our solutions to these problems include the use of reversible sealing, capillary phenomena for powering and controlling liquid transport, and non‐contact microfluidics for spotting and drawing (on surfaces) with flow conditions. These solutions offer many advantages over conventional techniques for handling minute amounts of liquids and may find applications in lithography, biopatterning (e.g., the patterning of biomolecules), diagnostics, drug discovery, and also cellular assays.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".