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Record W2981480539 · doi:10.1002/admi.201900995

Geomaterial‐Functionalized Microfluidic Devices Using a Universal Surface Modification Approach

2019· article· en· W2981480539 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

VenueAdvanced Materials Interfaces · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersChina Scholarship CouncilConsejo Nacional de Ciencia y Tecnología
KeywordsMaterials sciencePolydimethylsiloxaneSurface modificationMicrofluidicsContact angleNanotechnologyLayer (electronics)Silicon dioxideChemical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract The layer‐by‐layer (LbL) self‐assembly technique is used to coat the surface of flow channels in microfluidic chips with geomaterials. The surface modifications diminish the discrepancy between the surface chemistry of synthesized microfluidic devices and those of underground porous rocks. Hence, the use of visual models and, in particular, microfluidic devices is broadened to simulate the multiphase flows and fluid–solid interactions in actual rocks. Glass and quartz substrates are successfully coated with silicon dioxide (SiO 2 ), bentonite, and montmorillonite. On‐chip functionalization of polydimethylsiloxane (PDMS) and glass micromodels with SiO 2 is also accomplished. The functionalized coatings using confocal laser scanning microscopy (CLSM), atomic force microscopy (AFM), and contact angle measurements are characterized. The surface modification technique is shown to be material‐independent, which generates a hydrophilic surface. The surface‐coated chips, functionalized by clay particles, are utilized to illustrate the role of water salinity on oil displacement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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