Microcrystal Growth Pathways Investigated with Machine Learning Segmentation and Classification in Scanning Electron Microscopy

Advances in electron microscopy have revolutionized material characterization on the nano- and microscales, providing important insights into local ordering, structure, and size and quality distributions. While shape and size can be rigorously quantified through microscopy, it is often limited to local structure analysis and fails to describe bulk sample quality. Herein, a flexible machine learning (ML) tool is described that can segment and classify faceted crystals in scanning electron microscopy (SEM) micrographs to determine sample quality through the crystal size and product distribution. As a case study, this tool was applied to investigate crystal growth pathways (classical nucleation and growth compared to nonclassical growth) in DNA-mediated nanoparticle assembly through size and product (single crystal, fused crystal, or noncrystal) distribution of samples containing over 13000 colloidal crystal products. Strong DNA bond strengths (controlled by DNA sequence) lead to fast nucleation that exhausts the monomer concentration, resulting in smaller colloidal crystals. Alternatively, increased thermal energy and crystallization time lead to nonclassical crystallization pathways (coalescence) that result in larger colloidal crystals. This tool is useful since experimental conditions can now be deliberately identified to control colloidal crystal size and size distribution, important considerations for researchers interested in designing and synthesizing colloidal crystal metamaterials.电子显微技术的进步极大革新了纳米与微米尺度的材料表征工作，为揭示材料的局域有序性、微观结构以及尺寸与品质分布提供了关键洞见。尽管通过显微成像可对颗粒形貌与尺寸进行严谨量化，但这类方法通常仅局限于局域结构分析，无法表征块体样品的整体品质。本文介绍了一款灵活的机器学习（machine learning, ML）工具，可对扫描电子显微镜（scanning electron microscopy, SEM）显微图像中的面状晶体进行分割与分类，并通过晶体尺寸与产物分布评估样品品质。作为案例研究，该工具被应用于探究DNA介导纳米颗粒组装过程中的晶体生长路径——经典成核生长与非经典生长的对比——通过分析包含逾13000个胶体晶体产物的样品的尺寸与产物类型（单晶、熔合晶体或非晶体）分布开展研究。较强的DNA键合强度（由DNA序列调控）可促成快速成核，耗尽单体浓度，最终生成尺寸更小的胶体晶体。反之，提升热能与结晶时长则会导向非经典结晶路径（团聚融合），最终得到尺寸更大的胶体晶体。该工具具备重要应用价值：研究人员可借此精准确定并调控实验条件，以定制胶体晶体的尺寸与尺寸分布，这对于致力于设计与合成胶体晶体超材料的研究者而言是关键的考量因素。