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학술발표회초록보기

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  • 02월 28일 17시 이후 : 초록수정 불가능, 일정확인 및 검색만 가능

제123회 대한화학회 학술발표회, 총회 및 기기전시회 안내 A new approach for size determination of nanoparticles by single particle inductively coupled plasma-mass spectrometry (sp-ICP-MS)

등록일
2019년 2월 12일 11시 37분 42초
접수번호
5009
발표코드
ANAL1.O-5 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
목 09시 : 16분
발표형식
구두발표
발표분야
Analytical Chemistry - Oral Presentation of Young Analytical Chemists I
저자 및
공동저자
Yeon Hee Park, Heung Bin Lim*
Department of Chemistry, Dankook University, Korea
A new approach for the size determination of Au nanoparticles in the size range of 15 nm – 100 nm was explored using single particle inductively coupled plasma-mass spectrometer (sp-ICP-MS). Since current sp-ICP-MS employed aqueous standard solutions to determine the particle size, many assumptions were made and experimental parameters should be determined, which caused measurement errors, although providing universality. In this work, several points were improved to enhance analytical performance in sp-ICP-MS. For higher sensitivity, the level of background subtraction was carefully determined after fitting to a normal distribution, followed by building a correlation curve of average intensity area per particle vs particle radius. When the results were fitted to an allometric function of Y=aX³ at the optimized level of mean plus 3σ, excellent agreement was found, showing a correlation coefficient (R²) of 0.995<. Furthermore, a window selection method for identifying nanoparticles with multi datapoint profile was studied for mixture analysis, in which the window range of the intensity-frequency plot was determined from the number of particle and verified by building up the correlation curve. Conclusively, the formulated method using average intensity area per particle gave a way to determine unknown sizes from the correlation curve using standard Au nanoparticles, instead of aqueous standard solutions. Furthermore, the window selection method showed a potential to extract the size information of nanoparticles with multi datapoints from unknown mixtures.

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