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Thermal Sigmatropic Reactions: Overview

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Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
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In convection, thermal energy is carried by the large-scale flow of matter. Ocean currents and large-scale atmospheric circulation, which result from the buoyancy of warm air and water, transfer hot air from the tropics toward the poles and cold air from the poles toward the tropics. The Earth’s rotation interacts with those flows, causing the observed eastward flow of air in the temperate zones. Convection dominates heat transfer by air, and the amount of available space for the airflow...
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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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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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Heat transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
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Updated: Mar 21, 2026

Thermal Measurement Techniques in Analytical Microfluidic Devices
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Microstructure-Mediated Inverse Design of Thermal Barrier Coatings via Interpretable, Data-Efficient Machine

Tianmeng Huang1, Xiao Shan1, Hanchao Zhang2

  • 1Shanghai Key Laboratory of Advanced High-temperature Materials and Precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

ACS Applied Materials & Interfaces
|March 20, 2026
PubMed
Summary

This study introduces a machine learning framework for thermal barrier coating (TBC) design. It uses microstructure as an intermediary for accurate, data-efficient optimization, achieving significant thermal conductivity reductions.

Keywords:
atmospheric plasma sprayinterpretable machine learninginverse designmicrostructure-mediated designthermal barrier coatings

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computational Materials Science

Background:

  • Microstructure control is vital for thermal barrier coating (TBC) performance.
  • Complex process-structure relationships hinder predictive TBC design.
  • Data-driven methods face limitations due to scarce data and poor interpretability.

Purpose of the Study:

  • To develop a closed-loop machine learning framework for TBC design.
  • To decouple process-property relationships using microstructure as an interpretable intermediary.
  • To enable accurate TBC optimization with limited experimental data.

Main Methods:

  • Implemented a deep neural network for automated microstructure feature extraction from SEM images.
  • Developed forward prediction models for microstructure features (pores, unmelted regions, cracks).
  • Created an inverse design engine to translate target microstructures into manufacturing parameters.

Main Results:

  • Achieved high R-squared values for microstructure feature prediction (0.896 for pores, 0.878 for unmelted regions, 0.727 for cracks).
  • Uncovered dominant mechanisms governing thermal energy distribution and particle trajectory.
  • Demonstrated high accuracy in structural reproduction (92%) and parameter prediction (88%).

Conclusions:

  • The framework enables data-efficient, interpretable materials design for TBCs.
  • Designed coatings achieved 35-46% reduction in thermal conductivity at 1000°C.
  • This approach shifts TBC development towards intelligent precision design.