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Record W4379376426 · doi:10.1021/acsmacrolett.3c00153

Architecture-Based Programming of Polymeric Micelles to Undergo Sequential Mesophase Transitions

2023· article· en· W4379376426 on OpenAlexfundno aff
Parul Rathee, Nicole Edelstein‐Pardo, Francesca Netti, Lihi Adler‐Abramovich, Amit Sitt, Roey J. Amir

Bibliographic record

VenueACS Macro Letters · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsnot available
FundersFacultad de Ciencias Exactas, Universidad Nacional de La PlataTel Aviv UniversityAzrieli FoundationIsrael Science Foundation
KeywordsMicelleSelf-healing hydrogelsMesophaseAmphiphilePolymerMaterials scienceDegradation (telecommunications)Chemical engineeringCopolymerOrganic chemistryPolymer chemistryChemistryComputer scienceAqueous solutionComposite materialLiquid crystal

Abstract

fetched live from OpenAlex

Di- and triblock amphiphiles can form different mesophases ranging from micelles to hydrogels depending on their chemical structures, hydrophilic to hydrophobic ratios, and their ratio in the mixture. In addition, their different architectures dictate their exchange rate between the assembled and unimer states and consequently affect their responsiveness toward enzymatic degradation. Here we report the utilization of the different reactivities of di- and triblock amphiphiles, having exactly the same hydrophilic to lipophilic balance, toward enzymatic degradation as a tool for programming formulations to undergo sequential enzymatically induced transitions from (i) micelles to (ii) hydrogel and finally to (iii) dissolved polymers. We show that the rate of transition between the mesophases can be programmed by changing the ratio of the amphiphiles in the formulation, and that the hydrogels can maintain encapsulated cargo, which was loaded into the micelles. The reported results demonstrate the ability of molecular architecture to serve as a tool for programming smart formulations to adopt different structures and functions.

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 score0.017
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.270
Teacher spread0.255 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

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