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Record W2086368617 · doi:10.1021/acsami.5b01416

Tunable Ultrathin Membranes with Nonvolatile Pore Shape Memory

2015· article· en· W2086368617 on OpenAlexaff
Hidenori Kuroki, Crescent Islam, Igor Tokarev, Heng Hu, Guojun Liu, Sergiy Minko

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsQueen's University
FundersDivision of Materials ResearchJapan Society for the Promotion of ScienceNational Science Foundation
KeywordsMaterials scienceMembraneNanoporousChemical engineeringDispersityPorosityCopolymerThin filmColloidPolymerNanoparticleNanotechnologyPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

The concept of a responsive nanoporous thin-film gel membranes whose pores could be tuned to a desired size by a specific "molecular signal" and whose pore geometry becomes "memorized" by the gel is reported. The ∼100 nm thick membranes were prepared by dip-coating from a solution mixture of a random copolymer comprising responsive and photo-cross-linkable units and monodisperse latex nanoparticles used as a sacrificial colloidal template. After stabilization of the films by photo-cross-linking the latex template was removed, yielding nanoporous structures with a narrow pore size distribution and a high porosity. The thin-film membranes could be transferred onto porous supports to serve as tunable size-selective barriers in various colloids separation applications. The pore dimensions and hence the membrane's colloidal-particle-size cutoff were reversibly regulated by swelling-shrinking of the polymer network with a specially selected low-molar-mass compound. The attained pore shape was "memorized" in aqueous media and "erased" by treatment in special solvents reverting the membrane to the original state.

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 score0.028
Threshold uncertainty score0.951

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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