Nanomaterials for large-scale and low-cost energy storage

为了实现《巴黎协定》将全球变暖导致的气温上升限制在1.5°C的目标，我们必须在2050年之前完全消除全球二氧化碳排放。大力扩大风能和太阳能发电是实现这一目标的核心途径，但这些能源的间歇性带来了挑战：夜间生产不足意味着化石燃料发电机必须继续运行，而白天生产过剩则导致大量能源被丢弃。这些因素限制了增加可再生能源容量的经济可行性。大规模能源储存有望成为解决这一问题的可行方案，用电动汽车取代现有的内燃机运输车队将额外减少14%的全球二氧化碳排放量。然而，开发可扩展到全球水平的高容量能源存储需要新的具有成本效益的材料，并消除对稀缺和昂贵金属的依赖。这项研究旨在寻找新的材料成分，以提高电池性能并降低成本。具体而言，我们将致力于为当前一代电池开发改进的无钴阴极和阳极，以及使用固态电解质和非锂离子的新电池架构。其目标是将成本降低两倍，提高能量密度，加快充电速度，并增强安全性。基于解决方案的合成方法将允许高通量组合搜索，这将通过广泛的材料和器件特性得到证实。我们将通过机器学习算法进行数据分析和实验设计来补充实验，进一步提高材料发现的速度。互补的计算模型将在原子水平上提供对结构性质和化学反应的见解。拟议中的研究将使加拿大在新兴的绿色经济中处于经济优势。全球储能市场预计将从2017年的300亿美元增加到2026年的1400亿美元。这一增长将主要受到电动汽车和太阳能电网存储的推动。该计划将培养学生在高影响力研究的前沿，包括设备设计，材料合成和表征，并与领先的国际专家密切合作。它还将为学生提供动手使用计算技术的机会，特别是机器学习，这已经成为现代研究和It的重要工具。

In order to meet the Paris Agreement's target of limiting temperature increases due to global warming to 1.5°C, we must completely eliminate global CO2 emissions by 2050. Significantly expanding wind and solar energy generation is a central way to do so, but the intermittency of these energy sources poses a challenge: underproduction at night means that fossil-fuel energy generators must continue to operate, and overproduction during the day results in large quantities of energy being discarded. These factors limit the economic viability of increasing the capacity of renewable energy sources. Large-scale energy storage promises to be a viable solution to this problem and replacing the existing combustion engine transportation fleet with electric vehicles will cut an additional 14% of global CO2 emissions. However, developing high-capacity energy storage scalable to global levels requires new cost-efficient materials and eliminating the reliance on scarce and costly metals. The proposed research aims to find new material compositions that will improve battery performance and reduce their cost. Specifically, we will work on developing improved, cobalt-free cathodes and anodes for the current generation of batteries, as well as new battery architectures using solid-state electrolytes and non-lithium ions. The goal is to achieve a two-fold reduction in cost, increased energy density, faster charging speed, and enhanced safety. Solution-based synthetic methods will allow for a high-throughput combinatorial search that will be corroborated by extensive materials and device characterization. We will supplement the experiments by machine learning algorithms for data analysis and design of experiments to further boost the speed of materials discovery. Complementary computational models will provide insights into structural properties and chemical reactions at the atomic level. The proposed research will place Canada at an economic advantage in the emerging green economy. The global market for energy storage is expected to increase from USD 30B in 2017 to USD 140B by 2026. The increase will primarily be driven by electric vehicles and grid storage for solar energy. The proposed program will train students at the forefront of high-impact research, including device design, materials synthesis and characterization, and in close collaboration with leading international experts. It will also provide students with a hands-on involvement in the use of computational techniques, particularly, machine learning, which has become an essential tool in modern research and IT.