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Record W2885666891 · doi:10.1021/acsomega.8b01310

Facile Strategies to Synthesize Dual Location Dual Acidic pH/Reduction-Responsive Degradable Block Copolymers Bearing Acetal/Disulfide Block Junctions and Disulfide Pendants

2018· article· en· W2885666891 on OpenAlexaff
Arman Moini Jazani, Newsha Arezi, Keaton Maruya-Li, Sungmin Jung, Jung Kwon Oh

Bibliographic record

VenueACS Omega · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsConcordia University
Fundersnot available
KeywordsCopolymerAmphiphilePolymerizationAtom-transfer radical-polymerizationDisulfide bondChemistryCombinatorial chemistryChain transferDisulfide LinkagePolymer chemistryAcetalDual (grammatical number)Materials scienceOrganic chemistryRadical polymerizationPolymerBiochemistry

Abstract

fetched live from OpenAlex

We report new dual acidic pH/reduction-responsive degradable amphiphilic block copolymers featured with dual acidic pH-labile acetal linkage and a reductively-cleavable disulfide bond at the hydrophilic/hydrophobic block junction as well as pendant disulfide bonds in the hydrophobic block. Centered on the use of a macroinitiator approach, three strategies utilize the combination of atom transfer radical polymerization and reversible addition fragmentation chain transfer polymerization in a sequential or concurrent mechanism, along with facile coupling reactions. Combined structural analysis with dual-stimuli-responsive degradation investigation allows better understanding of the architectures and orthogonalities of the formed block copolymers as a diblock or a triblock copolymer. Our study presents the development of effective synthetic strategies to well-defined multifunctional amphiphilic block copolymers that exhibit dual-stimuli-responsive degradation at dual location (called the DL-DSRD strategy), thus potentially promising as nanoassemblies for effective drug delivery.

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 categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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.

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

Citations15
Published2018
Admission routes1
Has abstractyes

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