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Record W4410165866 · doi:10.1021/acs.biomac.4c01649

Programmable Fabrics of Enzyme-Responsive Amphiphiles: A Multiscale Platform for Hierarchical Mesophase Transformations

2025· article· en· W4410165866 on OpenAlexaff
Nicole Edelstein‐Pardo, Shira Kutchinsky, Amit Sitt, Roey J. Amir

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsNovelis (Canada)
FundersArmy Research OfficeIsrael Science Foundation
KeywordsMesophaseAmphiphileEnzymeChemistryNanotechnologyMaterials sciencePolymer chemistryChemical engineeringOrganic chemistryCopolymerEngineeringPolymerPhase (matter)

Abstract

fetched live from OpenAlex

Systems capable of undergoing a controlled cascade of mesophase transitions across hierarchical scales represent a novel class of dynamic materials. Here, we describe an electrospun polymeric fabric composed of enzyme-responsive di- and triblock copolymers that undergoes a hierarchical cascade of four distinct mesophases. Initially, on immersion in water, the macroscale fabric dissolves, forming nanoscale micelles. Enzymatic degradation of the diblock components triggers a transition into a triblock-based hydrogel. Finally, the enzymatic degradation of the hydrogel into hydrophilic polymers leads to complete dissolution. By adjusting the di- and triblock ratios, we can finely tune the fabric's dissolution rate. Moreover, the fibers can encapsulate hydrophobic agents, which are retained within the micelle and hydrogel phases, enabling their controlled release. This cascade of mesophase transitions, from a macroscopic solid to nanoscale assemblies, organized hydrogels, and eventual molecular dissolution, demonstrates sophisticated hierarchical control, unlocking new opportunities for biomedical applications of programmable materials.

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 score0.127
Threshold uncertainty score0.678

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 head0.312
Teacher spread0.293 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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