Monterrubio Iciar, Orive Joseba, Ismail Maha, Castillo Evaristo, García Javier, Redondo Ismael, Dauvergne Jean-Luc, Saracibar Amaia, Carrasco Javier, Casas-Cabanas Montse, Reynaud Marine
Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, Vitoria-Gasteiz, 01510, Spain.
Physical Chemistry Department, Pharmacy Faculty, Basque Country University (UPV/EHU), Álava, Vitoria-Gasteiz, 01006, Spain.
Chemistry. 2025 Sep 16;31(52):e02072. doi: 10.1002/chem.202502072. Epub 2025 Jul 30.
The development of sustainable advanced materials is increasingly driven by the need for sustainable, faster, scalable, and more efficient research workflows. Advancements in computational screening, high-throughput experimentation, and artificial intelligence (AI) are accelerating progress in materials discovery. To fully leverage the benefits of these complementary approaches, the implementation of materials acceleration platforms (MAPs) and self-driving laboratories (SDL) has emerged as a promising strategy. Here, we present the development of a semi-automated station for the lab-scale high-throughput synthesis (HTS) of inorganic materials, as part of the Materials Acceleration and Innovation plaTform for ENergy Applications (MAITENA). The system integrates two in-house-designed liquid-handling modules capable of performing sol-gel, Pechini, solid-state, and hydro/solvothermal syntheses. Each module enables the preparation of several dozen gram-scale samples per week with high reproducibility and minimal manual intervention. The system's capabilities are demonstrated through three case studies involving Li-ion battery materials. Results highlight the module's utilization for efficient screening of compositions and synthesis conditions to vary materials' properties. This accessible and modular infrastructure offers a practical route to implementing high-throughput strategies in inorganic materials research.
可持续先进材料的发展越来越受到对可持续、更快、可扩展且更高效研究工作流程的需求驱动。计算筛选、高通量实验和人工智能(AI)的进步正在加速材料发现方面的进展。为了充分利用这些互补方法的优势,材料加速平台(MAPs)和自动驾驶实验室(SDL)的实施已成为一种有前景的策略。在此,我们展示了一个用于实验室规模无机材料高通量合成(HTS)的半自动工作站的开发,作为能源应用材料加速与创新平台(MAITENA)的一部分。该系统集成了两个内部设计的液体处理模块,能够进行溶胶 - 凝胶、佩琴尼法、固态以及水热/溶剂热合成。每个模块每周能够制备几十个克级样品,具有高重现性且人工干预最少。通过涉及锂离子电池材料的三个案例研究展示了该系统的能力。结果突出了该模块在有效筛选组成和合成条件以改变材料性能方面的应用。这种易于使用且模块化的基础设施为在无机材料研究中实施高通量策略提供了一条实用途径。