An automated high-throughput robotic platform for accelerated battery and fuels discovery - DIGIBAT

用于加速电池和燃料发现的自动化高通量机器人平台 - DIGIBAT

• 批准号: EP/W036517/1
• 负责人: Magdalena Titirici
• 金额: $ 211.07万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW036517%2F1
• 关键词: automated; throughput; robotic; platform; accelerated

Batteries and electrocatalytic devices (i.e electrolysers, fuel cells) have multiple components spanning different length scales. The materials design space in these research fields is too large to be explored empirically. While experimental work can be directed by computational modelling to make this challenge more tenable, this is time consuming, and the number of tests/syntheses is still be too large on the experimental scale.DIGIBAT will combine computational tools (e.g. atomistic and molecular modelling, process modelling, computer-aided design, machine learning algorithms, data science) and automated HT synthesis, characterisation and testing from atoms to devices to accelerate the discovery and optimisation ofnew batteries and electrofuels.Specifically, DIGIBAT will comprise three HT stations: Platform A dedicated to materials synthesis and characterisation, Platform B dedicated to HT electrodes manufacturing all the way to device manufacturing and Platform C dedicated to HT electrochemical testing for both batteries and electrocatalysts. DIGIBAT will bepaired with materials characterisation also applied in HT, including in operando characterisation. By executing data-rich experiments, DIGIBAT will increase the pace of innovation, while enhancing reproducibility by eliminating human errors.The research enabled by ATLAS will target challenges related to: (1) the discovery and optimisation of new battery chemistries, (2) synthesising, optimising, and testing recycled battery materials; (3) Discovering precious metal free electrocatalysts for green H2 production and fuel cells; (4) Efficient N2 to ammonia and CO2 reduction to fuels and chemicals for electrocatalysts discovery

电池和电催化装置（即电解槽、燃料电池）具有跨越不同长度尺度的多个组件。这些研究领域的材料设计空间太大，无法进行实证探索。虽然实验工作可以通过计算建模来指导，使这一挑战更加可行，但这是耗时的，并且在实验规模上测试/合成的数量仍然太大。（例如原子和分子建模、过程建模、计算机辅助设计、机器学习算法、数据科学）和自动HT合成，从原子到设备的表征和测试，以加速新电池和电燃料的发现和优化。具体来说，DIGIBAT将包括三个HT站：平台A致力于材料合成和表征，平台B专用于HT电极制造，一直到器械制造，平台C专用于电池和电催化剂的HT电化学测试。DIGIBAT将与HT中也应用的材料表征（包括操作表征）配对。通过执行数据丰富的实验，DIGIBAT将加快创新的步伐，同时通过消除人为错误来提高可重复性。ATLAS支持的研究将针对与以下相关的挑战：（1）发现和优化新的电池化学，（2）合成，优化和测试可回收电池材料，（3）发现用于绿色氢气生产和燃料电池的无贵金属电催化剂，（4）开发新的催化剂，（5）开发新的催化剂，（6）开发新的催化剂，（7）开发新的催化剂，（8）开发新的催化剂，（9）开发新的催化剂，（9）开发新的催化剂，（10）开发新的催化剂，（10）开发新的催化剂，（11）开发新的催化剂，（11）开发新的催化剂，（12）开发新的催化剂，（11）开发新的催化剂，（12）开发新的催化剂，（13）开发新的催化剂，（11）开发新的催化剂，（（4）将N2高效还原为氨和CO2还原为燃料和化学品，用于电催化剂的发现