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Record W2943954127 · doi:10.1039/c9lc00254e

Microfluidic blood vasculature replicas using backside lithography

2019· article· en· W2943954127 on OpenAlexafffund
Marianne Fenech, Vincent Girod, Viviana Clavería, Sebastien Méance, Manouk Abkarian, Benoît Charlot

Bibliographic record

VenueLab on a Chip · 2019
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsMicrofluidicsLithographyNanotechnologySoft lithographyMaterials scienceBiomedical engineeringOptoelectronicsEngineeringMedicineFabricationPathology

Abstract

fetched live from OpenAlex

Blood vessels in living tissues are an organized and hierarchical network of arteries, arterioles, capillaries, veinules and veins. Their sizes, lengths, shapes and connectivity are set up for an optimum perfusion of the tissues in which they deploy. In order to study the hemodynamics and hemophysics of blood flows and also to investigate artificial vasculature for organs on a chip, it is essential to reproduce most of these geometric features. Common microfluidic techniques produce channels with a uniform height and a rectangular cross section that do not capture the size hierarchy observed in vivo. This paper presents a new single-mask photolithography process using an optical diffuser to produce a backside exposure leading to microchannels with both a rounded cross section and a direct proportionality between local height and local width, allowing a one-step design of intrinsically hierarchical networks.

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 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 score0.460
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations68
Published2019
Admission routes2
Has abstractyes

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