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Record W2999700637 · doi:10.3390/mi11010108

Durability and Recoverability of Soft Lithographically Patterned Hydrogel Molds for the Formation of Phase Separation Membranes

2020· article· en· W2999700637 on OpenAlexaff
Asad Asad, Masoud Rastgar, Hadi Nazaripoor, Mohtada Sadrzadeh, Dan Sameoto

Bibliographic record

VenueMicromachines · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMembraneMaterials scienceMoldFabricationSelf-healing hydrogelsSolventChemical engineeringPhase (matter)PorosityExtraction (chemistry)CastingChromatographyComposite materialChemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Hydrogel-facilitated phase separation (HFPS) has recently been applied to make microstructured porous membranes by modified phase separation processes. In HFPS, a soft lithographically patterned hydrogel mold is used as a water content source that initiates the phase separation process in membrane fabrication. However, after each membrane casting, the hydrogel content changes due to the diffusion of organic solvent into the hydrogel from the original membrane solution. The absorption of solvent into the hydrogel mold limits the continuous use of the mold in repeated membrane casts. In this study, we investigated a simple treatment process for hydrogel mold recovery, consisting of warm and cold treatment steps to provide solvent extraction without changing the hydrogel mold integrity. The best recovery result was 96%, which was obtained by placing the hydrogel in a warm water bath (50 °C) for 10 min followed by immersing in a cold bath (23 °C) for 4 min and finally 4 min drying in air. This recovery was attributed to nearly complete solvent extraction without any deformation of the hydrogel structure. The reusability of hydrogel can assist in the development of a continuous membrane fabrication process using HFPS.

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 score0.106
Threshold uncertainty score0.302

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 head0.019
GPT teacher head0.281
Teacher spread0.262 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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