Preparation of ER/NR foamed composites with adjustable shape memory and oil adsorption properties DOI
Mingyue Pang,

Haibiao Wang,

Zheng Yuan

и другие.

Journal of Polymer Engineering, Год журнала: 2025, Номер unknown

Опубликована: Июнь 5, 2025

Abstract A novel class of eco-friendly natural Eucommia rubber (ER)/natural (NR) foam composites with tunable shape memory and oil adsorption capabilities has been successfully developed through optimized vulcanization-foaming integration technology. This study utilized comprehensive characterization techniques such as universal testing machine measurements, scanning electron microscopy, complementary analytical methods to systematically evaluate the foamed composites’ mechanical properties, behavior, absorption characteristics. Through these advanced approaches, we elucidated precise influence foaming agent H concentration on both macroscopic performance microstructural evolution composites. When increasing content, cellular morphology exhibited substantial expansion. structural transformation was accompanied by a marked density reduction from 0.52 g/cm 3 0.18 , concurrently driving corresponding decline in tensile strength 11.1 MPa 4.3 MPa. Beyond fundamental property variations, manifested distinctive functional characteristics including notable effects temperature-responsive capabilities. Particularly noteworthy precisely performance, which could be strategically modulated controlled physical state transitions under thermal stimuli. responsiveness establishes promising potential for intelligent material applications requiring adaptive oil–water separation functionality.

Язык: Английский

Generative Design of Thermoset Shape Memory Polymers Driven by Chemical Group: A Conditional Variational Autoencoder Approach DOI Open Access

Borun Das,

Andrew J. Peters, Guoqiang Li

и другие.

Journal of Polymer Science, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

ABSTRACT The discovery of novel thermoset shape memory polymers (TSMPs) for additive manufacturing can be accelerated through the use a deep‐generative algorithm, minimizing need laborious traditional laboratory experiments. This study is first to introduce an innovative approach that uses deep generative learning model, namely conditional variational autoencoder (CVAE), discover TSMPs with lower glass transition temperature () and high recovery stress values (). In this study, specific chemical groups, such as epoxy, amine, thiol, vinyl, are integrated constraints generate while preserving essential reaction properties. To address challenges posed by small dataset, CVAE model used graph‐extracted features. Unlike previous studies focused on single‐polymer systems, research extends two‐monomer samples, discovering 22 TSMPs. has practical implications in manufacturing, biomedical devices, aerospace, robotics samples from limited data.

Язык: Английский

Процитировано

0

Bioinspired Soft Machines: Engineering Nature’s Grace into Future Innovations DOI Creative Commons
Ajay Vikram Singh, Mohammad Hasan Dad Ansari, Arindam K. Dey

и другие.

Journal of Functional Biomaterials, Год журнала: 2025, Номер 16(5), С. 158 - 158

Опубликована: Апрель 28, 2025

This article explores the transformative advances in soft machines, where biology, materials science, and engineering have converged. We discuss remarkable adaptability versatility of whose designs draw inspiration from nature’s elegant solutions. From intricate movements octopus tentacles to resilience an elephant’s trunk, nature provides a wealth for designing robots capable navigating complex environments with grace efficiency. Central this advancement is ongoing research into bioinspired materials, which serve as building blocks creating machines lifelike behaviors adaptive capabilities. By fostering collaboration innovation, we can unlock new possibilities shaping future seamlessly integrate interact natural world, offering solutions humanity’s most pressing challenges.

Язык: Английский

Процитировано

0

Smart Materials for Next-Generation Manufacturing of 3D, 4D, and 5D Printing Technologies DOI
Tejendra K. Gupta, Abhilash T. Nair,

Akshita Akshita

и другие.

Engineering materials, Год журнала: 2025, Номер unknown, С. 525 - 543

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Smart Nanomaterials: Current State and Future Prospects in Drug Delivery and Tissue Engineering DOI

E. Elizabeth Rani,

D. Sakthi Sanjana,

Karthikeyan Elumalai

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Май 6, 2025

Язык: Английский

Процитировано

0

Preparation of ER/NR foamed composites with adjustable shape memory and oil adsorption properties DOI
Mingyue Pang,

Haibiao Wang,

Zheng Yuan

и другие.

Journal of Polymer Engineering, Год журнала: 2025, Номер unknown

Опубликована: Июнь 5, 2025

Abstract A novel class of eco-friendly natural Eucommia rubber (ER)/natural (NR) foam composites with tunable shape memory and oil adsorption capabilities has been successfully developed through optimized vulcanization-foaming integration technology. This study utilized comprehensive characterization techniques such as universal testing machine measurements, scanning electron microscopy, complementary analytical methods to systematically evaluate the foamed composites’ mechanical properties, behavior, absorption characteristics. Through these advanced approaches, we elucidated precise influence foaming agent H concentration on both macroscopic performance microstructural evolution composites. When increasing content, cellular morphology exhibited substantial expansion. structural transformation was accompanied by a marked density reduction from 0.52 g/cm 3 0.18 , concurrently driving corresponding decline in tensile strength 11.1 MPa 4.3 MPa. Beyond fundamental property variations, manifested distinctive functional characteristics including notable effects temperature-responsive capabilities. Particularly noteworthy precisely performance, which could be strategically modulated controlled physical state transitions under thermal stimuli. responsiveness establishes promising potential for intelligent material applications requiring adaptive oil–water separation functionality.

Язык: Английский

Процитировано

0