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
Sample preparation methods have an important role in transmission electron microscopy (TEM).New materials (ceramics, thin films, composites, etc.) that are now present have a specific property and have to be precisely treated and usually post-treated.This poses evernew challenges, constant adjustments, and improvements in advanced TEM preparation techniques like focused ion beam sample preparation, conventional sample preparation, and other TEM preparation methods.In this paragraph, we compared two nowadays most frequently used TEM sample preparation techniques; conventional sample preparation and focused ion beam sample preparation.TEM samples were, in the last stage of preparation, final-treated using NanoMill (model 1040, Fischione Instruments, Inc.) to achieve the best results for further TEM/STEM analysis.The study was created for a discussion about using different approaches to achieve the best result for the TEM sample preparation.The first TEM sample was prepared using FEI Helios NanoLab NL650 dual-beam Focused Ion Beam (FIB).The sample was finally treated (thinned and cleaned) with NanoMill under specific conditions for FIB-type samples.The second TEM sample was conventionally prepared.The sample was thinned, dimpled down to transparent thickness (Dimple grinder, Gatan Inc.), and ion-milled using PIPS (Precision Ion Polishing System, Gatan Inc.) to achieve perforation.In the last stage, the sample was finally treated with NanoMill under specific conditions for conventional-type sample preparation.We want to expose how important is to choose the proper technique and appropriate conditions for TEM sample preparation.Relative thickness study distinctly revealed improvement in sample quality that affects the final STEM investigations.
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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