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Record W1988533247 · doi:10.1039/b802373p

Lab-on-chip methodologies for the study of transport in porous media: energy applications

2008· article· en· W1988533247 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

VenueLab on a Chip · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPorous mediumMicrofluidicsMultiphase flowWettingLab-on-a-chipPercolation (cognitive psychology)Energy transportChipPorosityNanotechnologyPercolation theoryTransport phenomenaMaterials scienceProcess engineeringComputer scienceEngineeringChemical engineeringEngineering physicsTelecommunicationsPhysicsElectrical engineeringMechanicsTopology (electrical circuits)

Abstract

fetched live from OpenAlex

We present a lab-on-chip approach to the study of multiphase transport in porous media. The applicability of microfluidics to biological and chemical analysis has motivated much development in lab-on-chip methodologies. Several of these methodologies are also well suited to the study of transport in porous media. We demonstrate the application of rapid prototyping of microfluidic networks with approximately 5000 channels, controllable wettability, and fluorescence-based analysis to the study of multiphase transport phenomena in porous media. The method is applied to measure the influence of wettability relative to network regularity, and to differentiate initial percolation patterns from active flow paths. Transport phenomena in porous media are of critical importance to many fields and particularly in many energy-related applications including liquid water transport in fuel cells, oil recovery, and CO(2) sequestration.

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 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 score0.279
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.260
Teacher spread0.225 · 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