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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Related Experiment Video

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Remote Magnetic Actuation of Micrometric Probes for in situ 3D Mapping of Bacterial Biofilm Physical Properties
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Spatially Aware Diffraction Mapping Enables Fully Autonomous MicroED.

Dmitry B Eremin1, Kunal K Jha1, David A Delgadillo1

  • 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.

Journal of the American Chemical Society
|November 10, 2025
PubMed
Summary
This summary is machine-generated.

Reciprocal Eyes (REyes) is a new platform that automates microcrystal electron diffraction (microED) for faster crystal structure determination. This intelligent system delivers structural solutions from diverse samples with minimal human intervention.

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Area of Science:

  • Crystallography
  • Electron Microscopy
  • Structural Chemistry

Background:

  • Microcrystal electron diffraction (microED) is a powerful technique for determining the 3D structure of small crystals.
  • Current microED workflows require significant expertise and manual intervention, limiting broader accessibility.
  • Automating microED can accelerate structural analysis and chemical discovery.

Purpose of the Study:

  • To introduce Reciprocal Eyes (REyes), an autonomous platform designed to enhance microED accessibility and utility.
  • To enable automated, high-throughput crystal structure determination using microED.
  • To demonstrate the platform's capability across diverse sample types.

Main Methods:

  • Development of an intelligent platform (REyes) integrating diffraction-based particle selection and real-time data processing.
  • Autonomous spatial mapping of diffraction signals and selection of crystallites based on lattice-quality metrics.
  • Testing REyes on four transmission electron microscopes (TEMs) with various sample types.

Main Results:

  • REyes successfully automates microED data acquisition and processing, delivering crystal structures without human intervention.
  • The platform consistently identifies and indexes high-resolution data from diverse compounds, including materials, peptides, metal complexes, natural products, and proteins.
  • Preliminary *ab initio* structural solutions were obtained from single crystallites across all tested sample types.

Conclusions:

  • REyes significantly enhances the accessibility and efficiency of microED for structural chemistry and chemical discovery.
  • The autonomous nature of REyes reduces the need for expert intervention, broadening the application of microED.
  • This platform holds promise for accelerating the pace of structural elucidation in various scientific fields.