Engineering Research Equipment Grant: A Surface Force Instrument for Adhesion Membrane Transport and Polymer Studies

该项目提供的设备允许三名调查人员 他们最近获得了美国国家科学基金会的"微型中心"资助， 通过受体-配体相互作用介导的细胞分离。 的 表面力仪器（SFI）提供了关于 这种联系的强度，它们之间的距离 有效的，并且在流体膜表面的情况下（由 沉积在云母表面的脂质双层） 成键分子可以扩散到一个 另 分子通过膜的运输也是 引起了两位研究者的极大兴趣。 SFI提供直接 分子在孔隙间分配的信息 （由两个云母表面的细缝"孔隙"模拟）， 散装液体。 预测表明， 可以测试随着孔半径变化的分配系数。 许多基本的材料特性直接取决于 分子间力 这些属性反过来又被用于 直接用于化学工程计算， 将聚合物溶解度、胶体稳定性、粘附性 膜渗透、吸附等现象。 在 过去，必须使用导出的量作为 衡量这些互动。 SFI允许直接 测量这些力以及它们如何随位置变化。 它 因此，有可能为以下方面作出重要贡献： 化学工程的许多领域：在生产精确的 "第一原理"数据，在测试理论预测的基础上， 分子间的相互作用，我帮助生产改进的 相关功能。

The equipment provided by this project allows three investigators who recently have been awarded an NSF "minicenter" grant to study cell separations mediated by receptor-ligand interactions. The surface force instrument (SFI) provides direct information on the strength of such bonds, the distances over which they are effective, and in the case of fluid membrane surfaces (modeled by lipid bilayers deposited on the mica surfaces) how rapidly the bond-forming molecules can diffuse into close proximity to one another. The transport of molecules through membranes is also of great interest to two other researchers. The SFI provides direct information on the partitioning of molecules between pore (modeled by the thin slit "pore" of the two mica surfaces) and bulk liquid. Predictions suggesting an oscillatory variation in the partition coefficient as pore radius changes can be tested. Many fundamental materials properties are directly dependent on intermolecular forces. These properties, in turn, are used directly in chemical engineering calculations to predict and correlate polymer solubility, colloid stability, adhesion, membrane permeation, adsorption, and other phenomena. In the past, it has been necessary to use derived quantities as a measure of these interactions. The SFI permits direct measurement of these forces and how they vary with position. It therefore has the potential for making important contributions to many areas of chemical engineering: in producing accurate "first-principles" data, in testing theoretical predictions based on molecular interactions, and i helping to produce improved correlating functions.