Evidence map›Paper›PMID 41358038›Full record

ArticleChemical science2026

Impact of dynamic bond strength on the training of liquid crystal elastomers.

E Ghimire, I S Appen, C A Lindberg, L Blagitz de Abreu E Silva, S J Rowan

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Article in Chemical science, 2026. 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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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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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

5 authors.

E GhimirePritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA stuartrowan@uchicago.edu.ORCID https://orcid.org/0000-0001-9358-7514
I S AppenPritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA stuartrowan@uchicago.edu.
C A LindbergPritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA stuartrowan@uchicago.edu.ORCID https://orcid.org/0000-0001-8391-4753
L Blagitz de Abreu E SilvaUniversity of Chicago Laboratory Schools Chicago Illinois 60637 USA.
S J RowanPritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA stuartrowan@uchicago.edu.ORCID https://orcid.org/0000-0001-8176-0594

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft materials can be considered trainable when their functional/mechanical properties can be systematcially improved upon exposure to repeated external environmental stresses, such as mechanical loads. One class of material that has the potential to be trainable is dynamic liquid crystal elastomers (dLCEs), which are lightly crosslinked polymer networks that contain both anisotropic liquid crystalline molecules (mesogens) and dynamic bonds. In this work, the trainability of dLCEs is studied by utilizing the mechanical adaptability of mesogenic units in combination with reprogrammability and reconfigurability enabled by the dynamic bonds. The effects of dynamic bond strength on the thermomechanical and liquid crystalline properties of LCEs were studied by synthesizing a series of aza-Michael based dynamic LCEs by incorporating diamine crosslinkers containing either no dynamic bonds, disulfide bonds, or diselenide bonds. Compared to the two other systems, the LCE containing diselenide bonds exhibited significantly higher toughness at room temperature. The effects of mechanical strain on the trainability of the films was studied under slightly elevated temperatures, where diselenide-containing dLCEs exhibited enhanced actuation and stiffer mechanical properties with higher strain levels. Moreover, using the combined thermal and mechanical training protocols, spiral actuators fabricated from the dLCEs demonstrated self-sustained motion upon heating them above their nematic to isotropic transition temperatures. In addition, retrainability was demonstrated in diselenide LCEs by generating two distinct actuator shapes from a single sample, where blue light was used to spatially control the effective regions of training. Overall, this study examines how the strength of dynamic bonds influences the properties and trainability of LCEs and how functional responses can be tailored by applying different training protocols.

Identifiers

PMID41358038
PMCPMC12679475

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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.