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Record W4283162413 · doi:10.26434/chemrxiv-2022-1ffmn

Fabrication and assembly of thermoplastic microfluidics; a review

2022· review· en· W4283162413 on OpenAlexafffund
Amid Shakeri, Shadman Khan, Noor Abu Jarad, Tohid F. Didar

Bibliographic record

VenueChemRxiv · 2022
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcMaster University
KeywordsMicrofluidicsNanotechnologyCommercializationMicrochannelFabricationAdaptabilityMaterials scienceEngineeringBiochemical engineering

Abstract

fetched live from OpenAlex

Various fields within biomedical engineering have been afforded rapid scientific advancement through the incorporation of microfluidics. As literature surrounding biological systems become more comprehensive and many microfluidic platforms show potential for commercialization, the development of representative fluidic systems has become more intricate. This has brought increased scrutiny towards the material properties of microfluidic substrates. Thermoplastics have been highlighted as a promising material, given their material adaptability and commercial compatibility. This review provides a comprehensive discussion surrounding recent developments pertaining to thermoplastic microfluidic device fabrication. Existing and emerging approaches related to both microchannel fabrication and device assembly are highlighted, with consideration towards how specific approaches induce physical and/or chemical properties that are optimally suited for relevant real-world 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2022
Admission routes2
Has abstractyes

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