Force-controlled robotic mechanochemical synthesis†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-09-16 DOI:10.1039/D4DD00189C
Yusaku Nakajima, Kai Kawasaki, Yasuo Takeichi, Masashi Hamaya, Yoshitaka Ushiku and Kanta Ono
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Abstract

We demonstrate a novel mechanochemical synthesis method using a robotic powder grinding system that applies a precisely controlled and constant mechanical force. This approach significantly enhances reproducibility and enables detailed analysis of reaction pathways. Our results indicate that robotic force control can alter the reaction rate and influence the reaction pathway, highlighting its potential for elucidating chemical reaction mechanisms and fostering the discovery of new chemical reactions. Despite its significance, the application of a controllable constant force in macroscale mechanochemical synthesis remains challenging. To address this gap, we compared the reproducibilities of various mechanochemical syntheses using conventional manual grinding, ball milling, and our novel robotic approach with perovskite materials. Our findings indicate that the robotic approach provides significantly higher reproducibility than conventional methods, facilitating the analysis of reaction pathways. By manipulating the grinding force and speed, we revealed that robotic force control can alter both the reaction rate and pathway. Consequently, robotic mechanochemical synthesis has significant potential for advancing the understanding of chemical reaction mechanisms and discovering new reactions.

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力控机器人机械化学合成
我们成功地展示了利用机器人粉末研磨系统进行机械化学合成的方法,该系统能够施加精确控制的恒定机械力。尽管具有重要意义,但在大规模机械化学合成中应用可控恒定力仍具有挑战性。为了弥补这一不足,我们比较了传统手工研磨、球磨和我们使用包光体材料的新型机器人合成方法在各种机械化学合成中的再现性。我们的研究结果表明,与传统方法相比,机器人方法的重现性要高得多。这种可重复性的提高为分析反应路径提供了可能。我们研究了通过控制研磨力和速度对反应路径的影响。结果表明,机器人力控制可改变反应速率并影响反应路径。因此,机器人机械化学合成在阐明化学反应机理和促进新化学反应的发现方面具有潜力。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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