인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
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.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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