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Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

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).
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...

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Related Experiment Video

Updated: Jul 3, 2026

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics
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Light-powered end-to-end neutron detection and imaging with an edge-deployed optical AI chip.

Shanny Lin1,2, Hanqing Zhu2, Steven Clayton1

  • 1Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Scientific Reports
|November 27, 2025
PubMed
Summary

We developed an automated neutron detection workflow using an optical neural network (ONN) for enhanced radiation hardness. This system achieves over 96% accuracy and sub-pixel resolution for neutron imaging applications.

Keywords:
Deep learningEdge computingOptical neural networksUltracold neutrons

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Area of Science:

  • Nuclear physics and engineering
  • Advanced sensor technology
  • Artificial intelligence in scientific instrumentation

Background:

  • Neutron detection is critical across nuclear physics, energy, technology, and safeguards.
  • Current detection methods can be limited by radiation damage and processing speed.
  • Automated workflows are needed for efficient and reliable neutron detection and imaging.

Purpose of the Study:

  • To implement an automated, end-to-end neutron detection and imaging workflow.
  • To enhance the radiation-hardness and operational lifetime of neutron detection instruments.
  • To achieve high-accuracy neutron detection with sub-pixel resolution using edge-based optical neural networks.

Main Methods:

  • Developed an automated workflow for neutron detection using solid-state image sensors.
  • Deployed the workflow on an edge-based optical neural network (ONN).
  • Implemented a two-stage neural network framework: a region proposal network followed by a fully connected network for sub-pixel localization.

Main Results:

  • Achieved over 96% neutron detection accuracy.
  • Obtained sub-pixel and sub-micron position resolution for neutron hits.
  • Demonstrated the advantages of ONN hardware: radiation-hardness, low power consumption, and high computing speed.

Conclusions:

  • The developed automated workflow and two-stage neural network framework enable efficient and accurate neutron detection.
  • Edge-based ONN deployment significantly improves instrument radiation-hardness and lifetime.
  • This approach offers a promising alternative to electronic counterparts for integrated neutron detection systems.