메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
하유진 (한국공학대학교) 김민욱 (파인테크글로벌) 김욱배 (한국공학대학교)
저널정보
한국트라이볼로지학회 Tribology and Lubricants Tribology and Lubricants 제39권 제5호
발행연도
2023.10
수록면
190 - 196 (7page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

이 논문의 연구 히스토리 (2)

초록· 키워드

오류제보하기
Anti-adhesion coatings are very important in the processing of adhesive materials such as optical clear adhesive (OCA) films. Choosing the appropriate release coating material for dies and tools can be quite challenging. Hydrophobic surface treatment is usually performed, and its performance is often estimated by the static water contact angle (CA). However, the relationship between the release performance and the CA is not well understood. In this study, the water CAs of surfaces coated with anti-adhesion materials and the peel strengths of the acrylic-based adhesive films are evaluated. STC5 and SUS304 are selected as the base materials. Base materials with different surface roughnesses are produced by hairline finishing, mirror-polishing, and end milling. Four fluoropolymer compounds, including a self-assembled monolayer, are selected to make the base surface hydrophobic. Static, advancing, and receding CAs are mostly increased due to the coating, but the CA hysteresis is found to increase or decrease depending on the coating material. The peel strengths all decreased after coating and are largely dependent on the coating material, with significantly lower values observed for fluorosilane and perfluoropolyether silane coatings. The peel strength is observed to correlate better with the static CA and advancing CA than with the receding CA or hysteresis. However, it is not possible to accurately predict the anti-adhesion performance based on water CA alone, as the peel strengths are not fully proportional to the CAs.

목차

Abstract
1. 서론
2. 실험방법
3. 실험결과 및 분석
4. 결론
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-151-24-02-088226880