Parameters for predicting the composition of nanocrystalline triglycerides under different processing conditions

预测不同加工条件下纳米晶甘油三酯组成的参数

• 批准号: 341652-2012
• 负责人: Mazzanti, Gianfranco
• 金额: $ 1.46万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572333
• 关键词: Parameters; predicting; composition; nanocrystalline; triglycerides

This research program arises from industry's necessity to tailor, by thermo-mechanical processing, the physical functionality of lipid materials, given restrictions (nutrition, health, availability, price) imposed on the formulation. A valuable fundamental and general knowledge on the materials science of these medium sized molecules must be developed to contribute significantly to our ability to control their crystallization. The long term objective of this research program is thus the development of predictive models that determine the self-assembly of lipid multicomponent materials under the fields imposed during processing. The conceptual and mathematical models needed include crystalline nucleation and growth (composition and rate) along with momentum, heat and mass transfer mechanisms. The imposed fields include chiefly temperature profiles and shear/extensional flow under which these materials spontaneously form nanoplatelets. The development of measuring tools for the properties and performance is another essential part of the research program. The cycle of optimization of the models will be continued by comparing its predictions with the measured outcomes of controlled process. This may seem obvious, but most of the important parameters for these models cannot be directly measured. To determine them a large collection of data is necessary, and the model parameters need to be fit to correspond to the data. The implementation is not trivial, as will become apparent in this proposal. The specific objectives for this proposal aim at obtaining values for key parameters of the recently proposed linear kinetic segregation (LKS) crystallization model. In the process, the model itself will be tested. Preliminary work conducted by us has showed both the promising value of this approach, as well as many of the difficulties that are expected. The parameters are the excess energies or the solid solution and the kinetic ratio of adsorption. They depend on the polymorphic form of the nanocrystals, but are thought to be independent of the crystallization conditions.

该研究计划源于行业需要通过热机械加工来定制脂质材料的物理功能性，给定对制剂施加的限制（营养、健康、可用性、价格）。必须发展关于这些中等大小分子的材料科学的有价值的基础知识和一般知识，以显着提高我们控制其结晶的能力。 因此，本研究计划的长期目标是开发预测模型，确定加工过程中施加的电场下脂质多组分材料的自组装。 所需的概念和数学模型包括结晶成核和生长（成分和速率）沿着动量、热量和质量传递机制。所施加的领域主要包括温度分布和剪切/拉伸流动下，这些材料自发地形成纳米片。 性能和性能测量工具的开发是研究计划的另一个重要组成部分。通过将模型的预测与受控过程的测量结果进行比较，将继续模型的优化周期。这似乎是显而易见的，但这些模型的大多数重要参数无法直接测量。为了确定它们，需要收集大量数据，并且需要拟合模型参数以对应于数据。正如本提案中将变得明显的那样，实现并不是微不足道的。 本提案的具体目标旨在获得最近提出的线性动力学偏析（LKS）结晶模型的关键参数值。在这个过程中，模型本身将受到检验。我们进行的初步工作表明，这一办法既有很好的价值，也有许多预期的困难。参数为固溶体过剩能和吸附动力学比。它们取决于纳米晶体的多晶型形式，但被认为与结晶条件无关。