Quantitative investi gation of structural paramete rs contri butions on anthraquinones sol ubility in SC-CO۲ by QSAR coupled to ANFIS

  • سال انتشار: 1391
  • محل انتشار: پانزدهمین سمینار شیمی فیزیک ایران
  • کد COI اختصاصی: ISPTC15_0964
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 144
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نویسندگان

f Nasiri

Biophysical Chemi stry Laboratory, Department of Chemistry, Faculty of Science Ferdowsi Univers ity of Mashhad, Mashhad, Iran

a Mohammadzadeh

Soil m echanics and foundation, Fa culty of Engin eering University of Gilan, Rasht, Iran

چکیده

In ۲۱s t century, si gnificant research is bei ng done on finding new methods of particle synthesis or the ir application at nano scale. Ma ny methods have been employed for synthe sis of nanom aterials. In recent years supercritica l fluids (SCFs) are findi ng wide ap plication in nano materials systemization. Thus, many theoretical dev elopment, experimental and appli cation studies have been done[۱&۲].Quantitative structure–activit y/property r elationship (QSAR/QSPR) methods are common and rather successful techniques in chemistry. However, in cases of complex relationships, conventional QS AR/QSPR methods often lead to insufficient or misleading inform ation because of nonlin ear relationships within the data set [۳].We h ave applied structural p arameters in uniquene ss and binary combinat ions to cal culate anthraquinones s olubility in supercritica l carbon dioxide. These structural parameters are comp uted by density functio nal theory: B۳LYP / ۶- ۳۱G. The se lected calc ulation meth od is available, fast an d reliable, so one can c alculate afo rementioned compoun ds solubility with high accuracy [۴]. Applying improvements in SC-CO ۲-based sy nthesis of na nomaterials, best results are achieved. This pa per propos es a new method, Ada ptive Neuro-Fuzzy Inference Syste m (ANFIS) to evaluate structural parameters of certain organic com pounds for their appro priate solubility in term s of QSA R models w ith the aid of artificial neural network (ANN) approach combined w ith the principle of fuzzy logic [۵]. The ANFIS was utiliz ed to predict solubility which accounts for non-linearities. A data set of ۲۱ compou nds was use d [۶]. The re sulted ۲ equ ations estim ate solubility with acceptable error.

کلیدواژه ها

QSA R, ANFIS, Fuzzy logic, Structural p arameters, N anomateria ls synthesis.

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