Coarse-grained modelling of membranes for fuel cell applications

雅各布是由一个工业案例助学金支持，这个类别正在用于他的JeS记录，使凭证号码和他的资金是链接到他的记录在整个四年的研究生学习。他也是一个IDS的学生与物理科学学院的学费减免。Jacob将首先在CDT的材料科学计算方法的主持下攻读物理学硕士学位，并在完成该学位后在James Elliott教授的指导下转入材料科学系，继续攻读下述博士学位。该项目将通过结合建模和测量方法预测和验证质子导电氟碳离聚物薄膜的结构-性能关系，建立在初始的多体耗散粒子动力学（MDPD）模型，以完善解释的中子反射率（NR）和掠入射小角X射线散射（GI-SAXS）测量的离聚物薄膜在一系列基板。MDPD模型预测显着的结构变化的离聚物薄膜内作为厚度的函数在3-10 nm的范围内，并使用NR数据获得的约翰逊万丰，模型无关的方法已开发用于提取密度分布从中子散射数据进行比较的模型，这是基于以前的工作反演SAXS。该项目的下一步将是用实验数据验证改进的模型预测，理解和纠正差异，然后引入性能预测作为膜厚度，离聚物类型，溶剂类型，碳表面功能，Pt负载和干燥温度的函数。

Jacob is supported by an Industrial CASE studentship and this category is being used for his JeS records, so that the voucher number and his funding are linked to his records throughout the four years of his postgraduate studies. He is also an IDS student with a fee waiver from the School of the Physical Sciences. Jacob will first take a taught MPhil in Physics under the auspices of the CDT in Computational Methods for Materials Science and on completion of this degree transfer to the Department of Materials Science under the supervision of Professor James Elliott to follow the PhD described below.The project will predict and verify structure-property relationships in thin proton-conducting fluorocarbon ionomer films by a combined modelling and measurement approach, building on an initial many-body dissipative particle dynamics (MDPD) model to refine interpretation of neutron reflectivity (NR) and grazing-incident small-angle X-ray scattering (GI-SAXS) measurements of ionomer thin films on a range of substrates. The MDPD model predicts significant structural changes within the ionomer thin film as a function of thickness in the 3-10 nm range and, using NR data acquired by Johnson Matthey, model-independent methods have been developed for extracting density profiles from neutron scattering data to compare with models, which are based on previous work inverting SAXS. The next steps for this project will be to verify the refined model predictions with experimental data, understand and rectify the discrepancies and then introduce property prediction as a function of film thickness, ionomer type, solvent type, carbon surface functionality, Pt loading and drying temperature.