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控制在1.5摄氏度以内的目标，我们必须在2050年之前完全消除全球二氧化碳排放。大幅扩大风能和太阳能发电是实现这一目标的核心方式，但这些能源的间歇性构成了一个挑战：夜间产量不足意味着化石燃料发电机必须继续运行，而白天产量过剩导致大量能源被丢弃。这些因素限制了增加可再生能源能力的经济可行性。大规模储能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，将使全球二氧化碳排放量额外减少14%。然而，开发可扩展到全球水平的大容量能源储存需要新的低成本材料，并消除对稀缺和昂贵金属的依赖。这项拟议的研究旨在寻找新的材料成分，以提高电池性能并降低成本。具体地说，我们将致力于为当前一代电池开发改进的、无钴的阴极和阳极，以及使用固态电解液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。基于溶液的合成方法将允许高通量的组合搜索，这将得到广泛的材料和设备表征的证实。我们将通过机器学习算法对实验进行补充，进行数据分析和实验设计，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属性和化学反应的见解。这项拟议的研究将使加拿大在新兴的绿色经济中处于经济优势。预计到2026年，全球储能市场将从2017年的300亿美元增加到1400亿美元。这一增长将主要由电动汽车和太阳能电网存储推动。拟议的计划将与领先的国际专家密切合作，培训处于高影响力研究前沿的学生，包括设备设计、材料合成和表征。它还将为学生提供使用计算技术的实践机会，特别是机器学习，这已成为现代研究和信息技术的重要工具。

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.