Introducing measure-by-wire, the systematic use of systems and control theory in transmission electron microscopy

TEMGYM Advanced – NanoMi Lens Characterisation

A complete analysis including finite element method (FEM) calculation, focal length properties, and thirdorder geometric aberrations of the open-source electrostatic lens from the NanoMi project is presented. The analysis is carried out by the software TEMGYM Advanced, a free package developed to carry out ray-tracing and lens characterisation in Python. Previously TEMGYM Advanced has shown how to analyse the aberrations of analytical lens fields; this paper expands upon this work to demonstrate how to apply a suitable fitting method to discrete lens fields obtained via FEM methods so that the aberrations of real lens designs can be calculated. Each software platform used in this paper is freely available in the community and creates a free and viable alternative to commercial lens design packages.

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

Rapid Generation of Metal-Organic Framework Phase Diagrams by High-Throughput Transmission Electron Microscopy

Metal-organic frameworks (MOFs) constructed from Zr₆ nodes and tetratopic carboxylate linkers display high structural diversity and complexity in which various crystal topologies can result from identical building units. To determine correlations between MOF topologies and experimental parameters, such as solvent choice or modulator identity and concentration, we demonstrate the rapid generation of phase diagrams for Zr₆-MOFs with 1,4-dibromo-2,3,5,6-tetrakis(4-carboxyphenyl)benzene linkers under a variety of conditions. We have developed a full set of methods for high-throughput transmission electron microscopy (TEM), including automated sample preparation and data acquisition, to accelerate MOF characterization. The use of acetic acid as a modulator yields amorphous, NU-906, NU-600, and mixed-phase structures depending on the ratio of N,N-dimethylformamide to N,N-diethylformamide solvent and the quantity of the modulator. Notably, the use of formic acid as a modulator enables direct control of crystal growth along the c direction through variation of the modulator quantity, thus realizing aspect ratio control of NU-1008 crystals with different catalytic hydrolysis performance toward a nerve agent simulant.

Software tools for automated transmission electron microscopy

The demand for high-throughput data collection in electron microscopy is increasing for applications in structural and cellular biology. Here we present a combination of software tools that enable automated acquisition guided by image analysis for a variety of transmission electron microscopy acquisition schemes. SerialEM controls microscopes and detectors and can trigger automated tasks at multiple positions with high flexibility. Py-EM interfaces with SerialEM to enact specimen-specific image-analysis pipelines that enable feedback microscopy. As example applications, we demonstrate dose reduction in cryo-electron microscopy experiments, fully automated acquisition of every cell in a plastic section and automated targeting on serial sections for 3D volume imaging across multiple grids.