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Related Concept Videos

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used.

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AutoLEI: An XDS-based pipeline with graphical user interface for automated real-time and offline batch 3D ED/microED

Lei Wang1, Yinlin Chen1, Emma Scaletti Hutchinson2

  • 1Department of Chemistry, Stockholm University, 106 91 Stockholm, Sweden.

Iucrj
|December 23, 2025
PubMed
Summary

A new automated pipeline, AutoLEI, streamlines processing for three-dimensional electron diffraction (3D ED), also known as microED. This accelerates structure determination from small crystals for molecules, MOFs, and proteins.

Keywords:
3D ED3D electron diffractionbeam-sensitive materialsdata analysiselectron crystallographymicroEDmicrocrystal electron diffractionoffline batch data processingreal-time data processing

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Area of Science:

  • Crystallography
  • Materials Science
  • Structural Biology

Background:

  • Three-dimensional electron diffraction (3D ED), or microcrystal electron diffraction (microED), is a powerful technique for analyzing submicron-sized crystals.
  • Rapid data collection in 3D ED/microED now allows for dozens of datasets to be acquired in a single session.
  • Efficient processing of numerous datasets is crucial for accelerating structure determination.

Purpose of the Study:

  • To develop a fast and automated workflow for processing, scaling, and merging large numbers of 3D ED/microED datasets.
  • To present AutoLEI, an XDS-based pipeline with a graphical user interface for automated batch data processing.
  • To demonstrate the pipeline's functionality and versatility across various sample types and data processing modes (real-time and offline).

Main Methods:

  • Development of an XDS-based computational pipeline named AutoLEI.
  • Implementation of a graphical user interface for user-friendly operation.
  • Testing and validation using diverse datasets including small organic molecules, metal-organic frameworks (MOFs), and proteins.

Main Results:

  • AutoLEI enables automated, real-time, and offline batch processing of 3D ED/microED data.
  • The pipeline successfully processed datasets from various materials, demonstrating its broad applicability.
  • Functionality was showcased through four distinct examples, highlighting efficiency and versatility.

Conclusions:

  • AutoLEI significantly accelerates the structure determination process by automating data handling.
  • The pipeline is a versatile tool applicable to a wide range of samples in 3D ED/microED studies.
  • The developed workflow enhances the efficiency of structural analysis using microcrystal electron diffraction.