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Related Concept Videos

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Updated: May 28, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Toward Intelligent Sensing Systems: Non-Equilibrium Materials as Platforms for AI-Enabled Autonomous Discovery.

Ashutosh Tiwari1, Gitanjali Mishra1, Jagdish Narayan2

  • 1Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT 84112, USA.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Intelligent sensing systems integrate signal processing within materials, moving beyond traditional separated architectures. This approach enhances adaptability and efficiency for dynamic environments using functional materials and artificial intelligence.

Keywords:
adaptive materialsartificial intelligenceautonomous discoverydefects and disorderin-sensor computingmachine learningnon-equilibrium materials

Related Experiment Videos

Last Updated: May 28, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Materials Science and Engineering
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Conventional sensing systems utilize sequential architectures, separating signal acquisition, processing, and decision-making.
  • This separation leads to limitations in latency, energy efficiency, and adaptability, especially in data-intensive and dynamic environments.

Purpose of the Study:

  • To discuss an emerging framework for intelligent sensing systems that integrate sensing functions within materials.
  • To explore how non-equilibrium functional materials, coupled with artificial intelligence, can enable in-sensor information transformation.

Main Methods:

  • Examining key material platforms with intrinsic properties like nonlinearity and memory for in-sensor processing.
  • Investigating architectural strategies that integrate sensing and processing functionalities.
  • Exploring opportunities for closed-loop autonomous discovery in intelligent sensing systems.

Main Results:

  • Non-equilibrium materials enable in-sensor information transformation through nonlinearity, memory, and temporal dynamics.
  • AI-powered material capabilities support sensing platforms for encoding, processing, and interpreting data at the measurement point.
  • The convergence of materials science and AI facilitates adaptive, low-latency, and energy-efficient sensing.

Conclusions:

  • Emerging intelligent sensing systems leverage functional materials and AI to overcome limitations of conventional architectures.
  • This integrated approach enables sensing technologies that are adaptive, energy-efficient, and operate with reduced latency.
  • Future directions include addressing challenges in material variability, scalability, and system integration for practical applications.