초록문의 abstract@kcsnet.or.kr

결제문의 member@kcsnet.or.kr

현재 가능한 작업은 아래와 같습니다.
  • 09월 01일 18시 이후 : 초록수정 불가능, 일정확인 및 검색만 가능

제118회 대한화학회 학술발표회, 총회 및 기기전시회 안내 QSPR studies for predicting the cloud point of nonionic surfactants

2016년 8월 25일 16시 30분 11초
ANAL2.O-25 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
금 10시 : 12분
분석화학 - Oral Presentation of Young Analytical Chemists Ⅱ
저자 및
조승현, 이민지, 이성광*
한남대학교 화학과, Korea
Cloud point(CP) is the temperature above which aqueous solutions of non-ionic and zwitterionic surfactants become turbid. Cloud Point Extraction(CPE) is a separation and preconcentration procedure that has been extensively applied for trace metal and organic compound determination in agreement with the ‘‘green chemistry’’ principles compared to those extractions that use organic solvents. Considering the complexity of synthesis and CP measurement for nonionic surfactants, the optimization of CP determination is relatively tedious and time consuming. Thus, the computational approach becomes an ideal alternative for analyzing, interpreting, and predicting the properties of unknown molecules. This study aimed to predict cloud point of nonionic by using a quantitative structure-property relationship(QSPR) method. The data set of cloud point were collected from a series of 85 nonionic surfactants. The molecular descriptors in models were calculated by PreADMET program and dataset was divided into training and test set by using KNIME. The forward selection and bootstrap sampling method were applied to determine the optimum descriptor of the multiple linear regression(MLR) in RapidMiner. The performance of each model were compared with R2 , RMSE(root mean square error), and MAE(mean absolute error) for training and test set. It was possible to know the applicable range of the prediction model by applicability domain(AD) of the results of each model. Y-scrambling was performed to confirm chance correlation of the model.