Related Concept Videos
01:19Mechanism 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...
01:27Thermal expansion and Thermal stress: Problem Solving
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55 °C.
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55 °C.
01:20Mechanisms of Heat Transfer II
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...
01:14Mechanisms of Heat Transfer
Heat transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
Conduction, accounting for approximately 3% of body heat loss at rest, is the process of exchanging heat between molecules of two materials in direct contact. This can result in both heat loss and gain. For instance, when the body is submerged in water, which conducts heat 20 times more effectively than air, it can either lose or gain significant heat.
Conduction, accounting for approximately 3% of body heat loss at rest, is the process of exchanging heat between molecules of two materials in direct contact. This can result in both heat loss and gain. For instance, when the body is submerged in water, which conducts heat 20 times more effectively than air, it can either lose or gain significant heat.
01:16Thermal Sigmatropic Reactions: Overview
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.
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in 1,5-hexadiene, referred to as...
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in 1,5-hexadiene, referred to as...
01:14Mechanisms of Heat Transfer I
Just as interesting as the effects of heat transfer on a system are the methods by which the heat transfer occur. Whenever there is a temperature difference, heat transfer occurs. It may occur rapidly, such as through a cooking pan, or slowly, such as through the walls of a picnic ice box. So many processes involve heat transfer that it is hard to imagine a situation where no heat transfer occurs. Yet, every heat transfer takes place by only three methods: conduction, convection, and radiation.
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07:03
In Situ Surface Temperature Measurement in a Conveyor Belt Furnace via Inline Infrared Thermography
Published on: May 30, 2020
Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows.
Zhengui Zhou1, Changyuan Chen1, Bin Li1
1Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.
Light, Science & Applications
|June 22, 2026
Summary
Researchers developed smart windows using vanadium dioxide (VO2) and machine learning for energy efficiency. These windows offer superior thermal management and privacy protection in vehicles and buildings.
Area of Science:
- Materials Science
- Energy Science
- Artificial Intelligence
Background:
- Smart windows are crucial for energy efficiency in buildings and electric vehicles.
- Vanadium dioxide (VO2) Fabry-Perot resonators offer passive heat transfer modulation but face challenges in structural control and multispectral selectivity.
- Current smart window technologies struggle to balance optical properties for diverse applications like privacy and energy saving.
Purpose of the Study:
- To design energy-efficient thermochromic smart windows for directional privacy protection (DPP) using a novel approach.
- To overcome the limitations of VO2-based smart windows by precisely controlling their structure and optical properties.
- To develop a framework for the inverse design of function-oriented smart windows.
Main Methods:
- Implementation of a physics-guided neural network for smart window design.
- Controlled synthesis of VO2 nanoparticles and optimization of spacer layers.
- Experimental validation of the designed smart window's performance.
Main Results:
- The developed DPP smart window achieved a luminous transmission below 0.15.
- Demonstrated high modulation of near-infrared transmittance (0.12) and longwave infrared emissivity (0.56).
- Outperformed commercial and previously reported smart windows in energy-saving performance and thermal management when integrated into electric vehicles and building envelopes.
Conclusions:
- The physics-guided neural network enables the inverse design of advanced smart windows with tailored functionalities.
- The VO2-based DPP smart window offers a significant advancement in energy efficiency and thermal management for various applications.
- This research provides a pathway for developing next-generation, function-oriented smart windows for real-world energy-saving solutions.