Evidence map›Paper›PMID 38025865›Full record

ArticleRSC advances2023

Toughening of epoxy thermosets by self-assembled nanostructures of amphiphilic comb-like random copolymers.

Li-Cheng Jheng, Ting-Yu Chang, Chin-Ting Fan, Tsung-Han Hsieh, Feng-Ming Hsieh, Wan-Ju Huang

Abstract read
In one paragraph

Article in RSC advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Li-Cheng JhengDepartment of Chemical and Materials Engineering, National Kaohsiung University of Science and Technology Kaohsiung Taiwan ROC lcjheng@nkust.edu.tw +886 7 3830674 +886 7 3814526 ext.15148.ORCID https://orcid.org/0000-0003-3377-1031
Ting-Yu ChangDepartment of Mold and Die Engineering, National Kaohsiung University of Science and Technology Kaohsiung Taiwan ROC.
Chin-Ting FanDepartment of Chemical and Materials Engineering, National Kaohsiung University of Science and Technology Kaohsiung Taiwan ROC lcjheng@nkust.edu.tw +886 7 3830674 +886 7 3814526 ext.15148.
Tsung-Han HsiehDepartment of Mold and Die Engineering, National Kaohsiung University of Science and Technology Kaohsiung Taiwan ROC.
Feng-Ming HsiehMaterial and Chemical Research Laboratories, Industrial Technology Research Institute Hsinchu Taiwan ROC.
Wan-Ju HuangDepartment of Chemical and Materials Engineering, National Kaohsiung University of Science and Technology Kaohsiung Taiwan ROC lcjheng@nkust.edu.tw +886 7 3830674 +886 7 3814526 ext.15148.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amphiphilic comb-like random copolymers synthesized from poly(ethylene glycol) methyl ether methacrylate (PEGMMA) and stearyl methacrylate (SMA) with PEGMMA contents ranging between 30 wt% and 25 wt% were demonstrated to self-assemble into various well-defined nanostructures, including spherical micelles, wormlike micelles, and vesicle-like nanodomains, in anhydride-cured epoxy thermosets. In addition, the polymer blends of the comb-like random copolymer and poly(stearyl methacrylate) were prepared and incorporated into epoxy thermosets to form irregularly shaped nanodomains. Our research findings indicate that both the comb-like random copolymers and polymer blends are suitable as toughening modifiers for epoxy. When added at a concentration of 5 wt%, both types of modifiers lead to substantial improvements in the tensile toughness (>289%) and fracture toughness of epoxy thermosets, with minor reductions in their elastic modulus (<16%) and glass transition temperature (<6.1 °C). The fracture toughness evaluated in terms of the critical stress intensity factor (

Identifiers

PMID38025865
PMCPMC10646570

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.