Imaging of liquids and cryogenic biological materials by electron microscopy has been recently enabled by innovative approaches for specimen preparation and the fast development of optimized instruments for cryo-enabled electron microscopy (cryo-EM). Yet, cryo-EM typically lacks advanced analytical capabilities, in particular for light elements. With the development of protocols for frozen wet specimen preparation, atom probe tomography (APT) could advantageously complement insights gained by cryo-EM. Here, we report on different approaches that have been recently proposed to enable the analysis of relatively large volumes of frozen liquids from either a flat substrate or the fractured surface of a wire. Both allowed for analyzing water ice layers which are several micrometers thick consisting of pure water, pure heavy water, and aqueous solutions. We discuss the merits of both approaches and prospects for further developments in this area. Preliminary results raise numerous questions, in part concerning the physics underpinning field evaporation. We discuss these aspects and lay out some of the challenges regarding the APT analysis of frozen liquids.

Atom probe tomography (APT) is often introduced as providing “atomic-scale” mapping of the composition of materials and as such is often exploited to analyze atomic neighborhoods within a material. Yet quantifying the actual spatial performance of the technique in a general case remains challenging, as it depends on the material system being investigated as well as on the specimen's geometry. Here, by using comparisons with field-ion microscopy experiments, field-ion imaging and field evaporation simulations, we provide the basis for a critical reflection on the spatial performance of APT in the analysis of pure metals, low alloyed systems and concentrated solid solutions (i.e., akin to high-entropy alloys). The spatial resolution imposes strong limitations on the possible interpretation of measured atomic neighborhoods, and directional neighborhood analyses restricted to the depth are expected to be more robust. We hope this work gets the community to reflect on its practices, in the same way, it got us to reflect on our work.

We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages.