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dc.contributorMing-Kuang Wangen_US
dc.contributorChen-Fang Linen_US
dc.contributorWen-Hui Kuanen_US
dc.contributorYang-Hsin Shihen_US
dc.contributor.advisorChia-Ming Changen_US
dc.contributor.authorChang, Chiung-Wenen_US
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dc.description.abstractThe environment is often exposed to chemical mixtures from multiple sources. The toxicity of various chemical mixtures is higher than single chemicals. However, the vast majority of toxicity studies deal with single chemicals, and therefore the prediction of mixture toxicity becomes a necessary and vital issue. In recent years, the development of quantum mechanical theory was combined with the progress of computational technology, which means quantitative calculation can be conducted from the atomic or molecular structure of a substance with little or even without empirical results. Besides, the parameter calculated was directly connected to organic activity, toxicity, chemical reaction, to construct the projection patterns, among which the QSAR is generally used to project mixture toxicity now. In this study, the objective is the binary mixtures toxicity of 12 benzene and its derivatives in the environment and 9 organophosphorus pesticides used with high frequency domestically, from which the DFT of quantum mechanical theory developed in recent years is used as a basis to build up the QSAR of toxicity prediction. The differences between Semi-empirical (AM1) and DFT (B3LYP) are discussed as well, and the prediction pattern will further be applied to each field to access mixtures and reach the goal of fast prediction. The results suggest that the results of prediction pattern are similar from either B3LYP or AM1 that are used to calculate benzene and its derivatives and mixtures. When using one parameter to predict toxicity, total surface area (TSA), apolar surface area (APSA), electron affinity (EA), and chemical potential (μ) are major factors. As multi-parameters are concerned, the increase of reaction energy (ΔEAB) and global soft (S) are required as parameters. Thus, AM1 has priority for choices in the future due to its fast calculation. When constructing prediction patterns of toxicity, surface area is an important parameter no matter benzene and its derivatives or organophosphorus pesticides are concerned with single parameter. And surface area could be influenced by chemical polarity depending on different subjects of prediction. Moreover, groups of fat and fragrance are factors to influence toxicity as well, so the number of ring and atom are used as parameters while the variety of toxic substances are complicated. When predicting mixture toxicity with multi-parameters, TSA, Etot, η, S, and μ are necessary parameters. And avoid aromatic compounds induce difference, so use sum of ring (R) and sum of atoms (NO, NN, NS, NP, NCl). Besides, ΔEAB is an important parameter in mixture.en_US
dc.description.abstract環境中常同時存在多種來源的化學混合物,而許多混合化學物質的毒性高於單一化學物質。然而大部分的毒性研究對象都是單一化學物質,因此預測混合物毒性為必要且重要的議題。近年來,量子力學的興起結合了電腦計算技術的提升,可由較少甚至不需實驗結果即可從物質的原子、分子結構進行定量計算,並藉由計算所得的參數直接與生物活性、毒性、化學反應性等建構預測模式,其中,定量結構活性關係(QSAR)是目前被廣泛使用於預測混合毒性的方法。 本研究分別以環境中常出現的12種苯類化合物與國內使用量較高的9種有機磷劑農藥之混合毒性為研究標的,以近年在量子力學興起的密度泛函理論(DFT)為基礎建立預測毒性之定量結構活性關係,並探討以半經驗(AM1)及密度泛函法(B3LYP)計算的差異,欲由此預測模式進一步套用在各個領域中評估混合物,並達到快速預測的目的。 研究結果顯示,無論是以B3LYP或AM1法計算苯類化合物和混合物都可得到相似的預測模式結果-以單參數為考量時,總表面積、非極性表面積、電子親和力和化學勢為主要的影響因子;而以多參數為考量時,需增加能量差(ΔEAB)與整體軟度為參數。因此未來在選擇使用方法上可以計算較快速的AM1法為優先。而在有機磷劑農藥之毒性預測方面,以單參數為考量時,單一型態的結果顯示環數與體積為主要參數,其次是總表面積、極性表面積與分子量;而混合型態則是以環數為主要參數,次之為分子量、總表面積、極性表面積與體積。當以多參數為考量時,須再添加總能量與化學勢,而在混合型態需增加各原子數目作為參數。另外,在建立毒性預測模式時,不論是苯類化合物或有機磷劑在考量單參數時,表面積皆是一重要的參數,而且依預測的對象不同,會受極性與非極性的影響。另外,脂肪族與芳香族也是影響毒性的因子之一,故當致毒物的種類較複雜時,須以環數和各原子數目作為參數。而以多參數預測毒性時,總能量、整體軟硬度與化學勢為必考慮的參數。另外,在混合型態預測上,混合物作用能量是必考慮因子,因其可表示混合物間的交互作用。zh_TW
dc.description.tableofcontents摘要 i Abstract ii 目錄 iii 圖目錄 iv 表目錄 vi 第一章 緒論 1 1-1研究動機 1 1-2研究目的 2 第二章 背景簡介與文獻回顧 3 2-1苯和其衍生物 3 2-2農藥(Pesticide) 4 2-3混合物聯合毒性作用 7 2-4定量結構活性關係(Quantitative structure-activity relationships, QSAR) 15 2-5計算化學與量子化學 16 2-5-1半經驗法(Semi-empirical) 17 2-5-2密度泛函理論(Density functional theory, DFT) 17 2-6軟硬酸鹼理論(Hard-soft acid-base principle, HSAB) 19 2-7密度泛函理論與軟硬酸鹼理論的合併 20 第三章 計算方法 22 3-1研究架構 22 3-2分子參數計算 23 3-2-1 結構最佳化計算 25 3-2-2 得失電子計算 26 3-3混合毒性計算方法 27 3-4 QSAR分析和數據處理 28 第四章 結果與討論 29 4-1建立以密度泛函理論為基礎的定量結構活性關係(DFT-based quantitative structure-activity relationship, DFT-based QSAR) 29 4-2以密度泛函法計算之參數進行定量結構活性關係的複因子迴歸 56 4-2-1單一型態苯類化合物之迴歸結果 56 4-2-2混合型態苯類化合物之迴歸結果 74 4-3以半經驗法計算之參數進行定量結構活性關係的複因子迴歸 89 4-3-1單一型態苯類化合物之迴歸結果 89 4-3-2混合型態苯類化合物之迴歸結果 104 4-3-3單一型態有機磷劑農藥之迴歸結果 120 4-3-4混合型態有機磷劑農藥之迴歸結果 130 第五章 結論 144zh_TW
dc.subjectdensity functional theoryen_US
dc.subjectquantitative structure-activity relationshipsen_US
dc.subjectorganophosphorus pesticidesen_US
dc.subjectorganic pollutantsen_US
dc.titleDFT-based Quantitative Structure-Activity Relationships for Predicting Mixture Toxicity of Organic Pollutantsen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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