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Related Concept Videos

Mechanism of heat transfer01:19

Mechanism of heat transfer

Understanding heat transfer mechanisms is essential for understanding how our bodies maintain balance in different environmental conditions. When the environment is thermoneutral, the body is in a state of balance, neither using nor releasing energy to maintain its core temperature. However, when the environment is not thermoneutral, the body employs four heat transfer mechanisms to maintain homeostasis: conduction, convection, evaporation, and radiation. These mechanisms facilitate heat...

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Related Experiment Video

Updated: Jul 7, 2026

Optimized Fabrication Procedure for High-Quality Graphene-based Moiré Superlattice Devices
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Published on: July 11, 2025

A Structurally Robust Framework for Intelligent Graphene Thermometry via Few-Shot Transfer Learning and

Jun Yang1, Wenchao Luo1, Jun Lu1

  • 1Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China.

Small (Weinheim an Der Bergstrasse, Germany)
|July 6, 2026
PubMed
Summary

This study introduces a new graphene sensing framework that overcomes device variability using AI. It enables precise, calibration-efficient flexible thermometry and physiological monitoring, even in harsh conditions.

Keywords:
1D convolutional neural networksalgorithm‐hardware co‐designfew‐shot transfer learninggraphene temperature sensorssensor arrays

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Development and Functionalization of Electrolyte-Gated Graphene Field-Effect Transistor for Biomarker Detection

Published on: February 1, 2022

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Ultrathin graphene sensors offer potential for flexible electronics but suffer from device-to-device variability due to substrate imperfections.
  • This heterogeneity makes traditional batch calibration unreliable for high-precision applications.

Purpose of the Study:

  • To develop a variability-resilient sensing framework for graphene sensors.
  • To enable high-precision, calibration-efficient applications like flexible thermometry and physiological monitoring.

Main Methods:

  • Algorithm-hardware co-design utilizing a few-shot transfer learning architecture with a frozen-backbone neural network.
  • Training a system to learn universal physics of graphene carrier scattering and adapt to new devices with minimal calibration data.
  • Employing a 1D convolutional neural network to decode physiological waveforms from raw graphene signals.

Main Results:

  • The framework achieves high accuracy (R² > 0.99) with less than 1% of conventional calibration data.
  • Enables first-time high-precision graphene-based flexible thermometry (±0.2°C) and real-time temperature inference up to 200°C.
  • Demonstrates robust decoding of physiological waveforms with 94.95% accuracy despite structural variations.

Conclusions:

  • The developed sensing-as-inference paradigm transforms graphene into a viable material for intelligent wearable thermometry.
  • This approach addresses inherent material imperfections, paving the way for reliable and efficient graphene-based flexible electronics.