Lei Bingyu, Svensson Per H, Yushmanov Pavel, Kloo Lars
Applied Physical Chemistry, Department of Chemistry, KTH Royal Institute of Technology, Stockholm SE-114 28, Sweden.
Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg SE-431 53, Sweden.
ACS Appl Mater Interfaces. 2025 May 7;17(18):26701-26709. doi: 10.1021/acsami.5c02605. Epub 2025 Apr 22.
The urgent need for renewable energy solutions requires rapid advancements in materials discovery. In response, we present AURORA, an innovative robotic platform that enhances this process by integrating automated synthesis, characterization, and evaluation into a single unit, thereby improving efficiency and reducing errors. Its modular design allows for adaptable screening of diverse materials, including metal halide perovskites, and their application in solar cell devices. Our study demonstrates the ability of AURORA to autonomously synthesize and evaluate polycrystalline, mixed halide perovskites, including a novel mesoscopic solar cell array with improved data reliability and throughput. AURORA also conducts postsynthesis treatments and dynamic analyses under stress, setting it apart from traditional methods. These features make AURORA a transformative tool for the discovery of novel materials, with potential machine learning integration for optimization. Our results highlight the application of AURORA as a robust and adaptable platform for future developments in automated materials research.
对可再生能源解决方案的迫切需求要求在材料发现方面迅速取得进展。作为回应,我们推出了AURORA,这是一个创新的机器人平台,通过将自动合成、表征和评估集成到一个单元中来增强这一过程,从而提高效率并减少错误。其模块化设计允许对包括金属卤化物钙钛矿在内的各种材料进行适应性筛选,以及它们在太阳能电池器件中的应用。我们的研究证明了AURORA自主合成和评估多晶混合卤化物钙钛矿的能力,包括具有更高数据可靠性和通量的新型介观太阳能电池阵列。AURORA还进行合成后处理和应力下的动态分析,这使其有别于传统方法。这些特性使AURORA成为发现新型材料的变革性工具,并具有潜在的机器学习集成以进行优化。我们的结果突出了AURORA作为一个强大且适应性强的平台在自动化材料研究未来发展中的应用。