MétaCan
Menu
Back to cohort
Record W2165188055 · doi:10.1002/jssc.200800671

Transverse diffusion of laminar flow profiles – a generic method for mixing reactants in capillary microreactor

2009· review· en· W2165188055 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

VenueJournal of Separation Science · 2009
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroreactorLaminar flowCapillary actionMixing (physics)DiffusionLaminar flow reactorChemistryFlow (mathematics)ChromatographyAnalytical Chemistry (journal)Materials scienceChemical engineeringMechanicsThermodynamicsOrganic chemistryOpen-channel flowCatalysis

Abstract

fetched live from OpenAlex

The capillary is an attractive format for integrated microanalyses, which start with the injection of separate reactants into the capillary and their mixing inside the capillary. Due to the nonturbulent nature of flow inside the capillary, mixing reactants in a generic way is a challenging task. Three approaches have been suggested as a solution: mixing by electrophoresis, mixing by longitudinal diffusion, and, most recently, mixing by transverse diffusion of laminar flow profiles (TDLFP). This is the first review on TDLFP, describing: (i) the physical basis of the method, (ii) its theory, (iii) analytical and numerical solutions for the calculation of concentration profiles of mixed reactants, (iv) up-to-date applications, and (v) problems to be solved and future directions.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.344
Teacher spread0.311 · 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