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Record W2782808911 · doi:10.1021/acs.macromol.7b01894

Compression of Nanoslit Confined Polymer Solutions

2018· article· en· W2782808911 on OpenAlexafffund
Yue Qi, Lili Zeng, Ahmed Khorshid, Reghan J. Hill, Walter Reisner

Bibliographic record

VenueMacromolecules · 2018
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsPolymerNonlinear systemPartial differential equationConfined spaceCompression (physics)Forcing (mathematics)Materials scienceNanotechnologyChemical physicsPhysicsStatistical physicsChemistryThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Many systems of biophysical and technological interest consist of multiple interpenetrating chains in a confined volume, i.e., a confined polymer solution. Using nanofluidic approaches developed originally for the study of single chains in confined geometries, we develop an assay to create confined polymer solutions on-chip and then probe the solution response to applied compressive forcing. In our approach, multiple chains are introduced into a nanoslit via hydrodynamic flow and are then concentrated against a barrier that is permeable only to solvent. For sufficiently high concentration, the compressed solution profile can be described by a mean-field polymer model based on Doi’s two-fluid approach, with the chain free energy described by a Ginzburg-type free energy functional. This theory furnishes a partial differential equation based description of the concentration profile in terms of a nonlinear Schrödinger-type equation, providing a general theoretical framework for modeling confined polymer solution dynamics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.217
Teacher spread0.202 · 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 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

Citations7
Published2018
Admission routes2
Has abstractyes

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