美国麻省理工学院Bazant, Martin Z.团队报道了从X射线视频逐像素学习异质反应动力学。相关研究成果于2023年9月13日发表在《自然》。

众所周知，空间不均匀、不稳定界面的反应速率很难量化，但在许多化学系统的工程中是必不可少的，如电池和电催化剂。通过操作显微镜对这些材料进行的实验表征产生了丰富的图像数据集，但由于反应动力学、表面化学和相分离的复杂耦合，仍然缺乏从这些图像中学习物理的数据驱动方法。

该文中，研究表明，可以从碳包覆的磷酸铁锂（LFP）纳米颗粒的原位扫描透射X射线显微镜（STXM）图像中了解非均相反应动力学。将STXM图像的大型数据集与热力学一致的电化学相场模型、偏微分方程（PDE）约束优化和不确定性量化相结合，研究人员提取了自由能景观和反应动力学，并验证了它们与理论模型的一致性。研究人员还同时了解了反应速率的空间异质性，这与通过俄歇电子显微镜（AEM）获得的碳涂层厚度分布非常匹配。

在180000个图像像素中，与学习模型的平均差异非常小（<7%），与实验噪声相当。研究结果为学习传统实验方法无法达到的非平衡材料性质开辟了可能性，并为表征和优化非均匀反应表面提供了一种新的非破坏性技术。

附：英文原文

Title: Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel

Author: Zhao, Hongbo, Deng, Haitao Dean, Cohen, Alexander E., Lim, Jongwoo, Li, Yiyang, Fraggedakis, Dimitrios, Jiang, Benben, Storey, Brian D., Chueh, William C., Braatz, Richard D., Bazant, Martin Z.

Issue&Volume: 2023-09-13

Abstract: Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries1 and electrocatalysts2. Experimental characterizations of such materials by operando microscopy produce rich image datasets3–6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation7. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (&lt;7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces. Analysis of a large dataset of scanning transmission X-ray microscopy images of carbon-coated lithium iron phosphate nanoparticles shows that the heterogeneous reaction kinetics of battery materials can be learned from such videos pixel by pixel.

DOI: 10.1038/s41586-023-06393-x

Source: https://www.nature.com/articles/s41586-023-06393-x