Atomic Force Microscope for Materials Characterization

这个提议需要原子力显微镜（AFM）。这一紧急要求是更换我们目前的仪器，该仪器遭受了灾难性的故障，无法修复。新的原子力显微镜将继续被安置在化学系/先进材料和技术中心（CAMTEC）的共享、开放的设施中。原子力显微镜是材料科学研究的常规工具。它经常被来自维多利亚大学科学和工程学院16个研究小组的几名研究人员（过去5年中约有67人）以及来自维多利亚（BC）材料相关公司的科学家使用。所要求的AFM将替换和升级温哥华岛唯一的同类设备（一个20多年的系统）。所要求的新仪器是一种最先进的设备，可以对非常小的特征（在纳米范围内-人类头发的宽度约为100,000纳米）进行成像，具有高空间分辨率和信噪比。该系统配备了先进的硬件和软件，允许快速成像采集和同时测量机械和电气特性。这种新的AFM允许产生大量的微观图像，可以提供一个完整的表征系统正在研究。这些独特的特性将在纳米结构太阳能电池器件的研究中得到探讨，以提高光到电的转换性能。在不久的将来，高效太阳能电池是实现可持续能源系统所必需的。新型纳米材料的使用，包括半导体、等离子体粒子和钙钛矿，为实现最大效率提供了潜在的方法。然而，形貌的控制和缺陷的存在通常是限制这些类型器件性能的步骤。所要求的AFM将允许在纳米尺度上揭示形态和性能之间关系的表征。所要求的AFM产生的大量数据也将用于改进制造和预测手性界面的性质。AFM将允许在短时间内获得不同类型的手性表面（不同类型的含硫氨基酸和肽）的大量图像。该海量数据集将与密度泛函理论和人工智能方法获得的计算数据相结合，进行大数据分析。目标是训练机器学习模型来预测这些手性界面的潜在结构和反应性，这将影响异相不对称催化剂和生物传感器的发展。新的AFM不仅将为新一代材料科学家提供高级培训，而且还将使研究有利于可持续能源，催化和生物传感器等新工业技术的发展。

This proposal requests an atomic force microscope (AFM). This urgent request is to replace our current instrument that has suffered a catastrophic failure and cannot be repaired.  The new AFM will continue to be housed in a shared, open access, facility at the Department of Chemistry/Center for Advanced Materials and Technologies (CAMTEC). The AFM is a routine tool for materials science research. It is regularly used by several researchers (about 67 individuals in the last 5 years) from 16 research groups from UVic science and engineering plus scientists from materials-related companies based in Victoria (BC). The requested AFM will replace and upgrade the only equipment (a 20+ years old system) of its kind in Vancouver Island. The requested new instrument is a state of the art equipment that allows imaging of very small features (in the nanometric range - the width of an human hair is about 100,000 namometers) with high spatial resolution and signal-to-noise ratio. The system is equipped with advanced hardware and software that allows for fast imaging acquisition and the simultaneous measurement of mechanical and electrical characteristics. This new AFM permit the generation of massive amounts microscopic images that can offer a full characterization of the system being investigated. These unique characteristics will be explored in the investigation of nanostructured solar cell devices that present enhanced performance for the light to electricity conversion. Highly efficient solar cells are required for the implementation of a sustainable energy system in a near future. The use of novel nanomaterials, including semiconductors, plasmonic particles and perovskites, provide potential approaches to achieve maximum efficiency. However, the control of the morphology and the presence of defects are generally limiting steps in the performance for those types of devices. The requested AFM will allow characterization that will reveal the relationship between morphology and performance at the nanoscale dimensions. The massive amount of data generated by the requested AFM will also be used to improve fabrication and predict properties of chiral interfaces. The AFM will allow the general of a large amount of images in short time of different types of chiral surfaces (different types of sulfur-containing amino acids and peptides). This massive data set will be combined with computational data obtained by density functional theory and artificial intelligence methods for big data analysis. The goal is to train machine learning models to make predictions regarding potential structure and reactivity of these chiral interfaces that will impact the development of heterogeneous asymmetric catalysts and biosensors. The new AFM will not only provide advanced training for a new generation of materials scientists, but also enable research that will benefit the development of new industrial technologies in sustainable energy, catalysis and biosensors.