An automated high-throughput robotic platform for accelerated battery and fuels discovery - DIGIBAT
An automated high-throughput robotic platform for accelerated battery and fuels discovery - DIGIBAT
批准号:
EP/W036517/1
负责人:
Magdalena Titirici
金额:
$211.07万
依托单位:
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
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
电池和电催化装置(即电解槽、燃料电池)有多个不同长度的组件。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,使这一挑战更加站得住脚,但这是耗时的,而且在实验规模上,测试/合成的数量仍然太大。DIGIBAT将结合计算工具(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)和从原子到设备的自动化HT合成、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将包括三个高温工作站:平台A致力于材料合成和表征,平台B致力于高温电极制造,一直到设备制造,平台C致力于电池和电催化剂的高温电化学测试。DIGIBAT将与也应用于HT的材料表征相匹配,包括在operando表征中。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提高再现性。ATLAS支持的研究将针对以下挑战:(1)发现和优化新的电池化学物质,(2)合成、优化和测试回收电池材料;(3)发现用于绿色制氢和燃料电池的无贵金属电催化剂;(4)有效地将N2还原为氨,将CO2还原为燃料和化学品,用于电催化剂的发现
英文摘要
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
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国内基金
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依托单位: