MétaCan
Menu
Back to cohort
Record W4285412929 · doi:10.1039/d2sm00787h

A photothermally triggered one-component shape memory polymer material prepared by cross-linking porphyrin-based amphiphilic copolymer self-assemblies

2022· article· en· W4285412929 on OpenAlexaff
Yufang Song, Yiming Chen, Wangqiu Qian, Dongjian Shi, Weifu Dong, Yang Wang, Piming Ma, Hongji Zhang

Bibliographic record

VenueSoft Matter · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversité de Sherbrooke
FundersChina Postdoctoral Science Foundation
KeywordsCopolymerMaterials scienceMoietyPorphyrinAmphiphilePolymer chemistryPolymerMethacrylateMonomerAcrylateMethyl methacrylateChemical engineeringChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

, in the biomedical field). Here, we synthesized a new type of porphyrin-based amphiphilic random copolymer bearing a reactive moiety of carbonyl group by co-polymerizing methyl methacrylate (MMA), butyl acrylate (BA), diacetone acrylamide (DAAM), acrylic acid (AA) and double-bonded vinyl porphyrin monomers, followed by induced self-assembly in aqueous solution to give rise to amphiphilic random copolymer nanoparticles. The nanoparticles were further crosslinked by means of adipic dihydrazide (ADH) to fabricate the photothermally triggered one-component shape memory polymer material. Compared with the most-studied multi-phase/multi-component shape memory polymer materials, the porphyrin moiety, playing the role of a photo-to-heat converter, covalently bonded into the polymer structure would certainly make it more homogeneous and more stable in principle.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueSoft MatterSame topicPolymer composites and self-healingFrench-language works237,207