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Record W3012863470 · doi:10.1039/d0ra00263a

Advances in passively driven microfluidics and lab-on-chip devices: a comprehensive literature review and patent analysis

2020· review· en· W3012863470 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.

Bibliographic record

VenueRSC Advances · 2020
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrofluidicsLab-on-a-chipNanotechnologyComputer scienceMaterials science

Abstract

fetched live from OpenAlex

The development of passively driven microfluidic labs on chips has been increasing over the years. In the passive approach, the microfluids are usually driven and operated without any external actuators, fields, or power sources. Passive microfluidic techniques adopt osmosis, capillary action, surface tension, pressure, gravity-driven flow, hydrostatic flow, and vacuums to achieve fluid flow. There is a great need to explore labs on chips that are rapid, compact, portable, and easy to use. The evolution of these techniques is essential to meet current needs. Researchers have highlighted the vast potential in the field that needs to be explored to develop rapid passive labs on chips to suit market/researcher demands. A comprehensive review, along with patent analysis, is presented here, listing the latest advances in passive microfluidic techniques, along with the related mechanisms and applications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.267
Teacher spread0.250 · 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