結合理論計算與實驗方法定量分析溶劑對生物醫學用共聚合物的選擇性及其微結構的影響(1/2)

塊狀共聚合物溶液，因著溶劑的選擇性以及溶劑的濃度，對其自身聚集的變化非常多元， 可形成奈米級尺寸的各種微結構。目前雖然大部分的實驗結果均能定性地與理論計算結果 一致，然而影響微結構形成的重要因素『溶劑選擇性』，隨著實際系統中共聚合物與溶劑之 間交互作用力的不同，有著非常大的差異;因此，為了要透徹地了解所欲研究的實際塊狀 共聚合物溶液系統如何應用於奈米級微結構中，首要的工作應為『溶劑選擇性』的定量分 析，我們計畫比較理論計算與實驗方法所得到的結果，來定量分析出溶劑對於塊狀共聚合 物的選擇性。所研究的系統為目前非常熱門的生物醫學材料，例如PEG-PLLA 共聚合物。我 們首先探討共聚物其結晶行為及微結構尺寸如何受到鏈段比例及結晶溫度的影響;接著探 討較稀薄的溶液中，由於隨著溶劑濃度的改變，高分子可由單一鏈轉變為多條分子鏈聚集 成微胞，因此運用溶液中流變性質、動態光散射、以及小角中子散射的分析，可得到所形 成微胞的結構尺寸與形狀資訊，藉由與理論計算結果的比較，可望能將各塊狀分子與溶劑 之間的交互作用參數決定出來。最後，運用這些參數即可以利用自洽平均場理論計算得到 所對應的聚乙二醇─聚已內酯共聚物溶液的相圖，有系統的來探討分析在各種微結構型態 下，其尺寸如何受到共聚合物的組成、溶劑選擇性、以及溶劑的濃度之影響，並與實驗結 果比較。Block copolymer solutions can self-assemble into various microstructures due to the effects of solvent selectivity. Though most of the related experimental and theoretical results are qualitatively consistent, there exists little quantitative comparison between both fields. This is due to the fact that it is difficult to determine the solvent selectivity in real systems. Therefore, we aim to quantitatively determine the interaction parameters between each block and the solvent by comparing both experimental and numerical results. We consider a poly(ethylene-glycol) -polylactide (PEG-PLLA) diblock copolymer. In particular, PLLA component can crystallize below the melting temperature. As PLLA is biodegradable and PEG is non-toxic to human bodies, these materials play a very important role on the biomedical research. We first focus on the crystallization as well as the structure analysis of PEP-PLLA copolymer melts. We then analyze the dilute copolymers in the presence of selective solvents. With the aid of rheology measurements, dynamic light scattering, and small-angle neutron scattering, the structural parameters, such as micelle shape and size, are analyzed and compared with the numerical results. As such, we can determine the interaction parameters between PEG-solvent and PLLA-solvent. Once the solvent selectivity is obtained, we determine the corresponding phase diagrams of PEG-PLLA block copolymer solutions by self-consistent mean-field theory