Revolutionizing Refrigeration: Nano-Engineered CHESS Thermoelectrics Double Efficiency for Solid-State Cooling

CHESS Thermoelectric Cooling Device - Johns Hopkins APL

As the global demand for energy-efficient, compact, and sustainable cooling systems continues to soar, researchers at the Johns Hopkins University Applied Physics Laboratory (APL) have unveiled a breakthrough that could reshape refrigeration as we know it. In collaboration with Samsung Research, the team introduced a new generation of solid-state cooling technology using CHESS (Controlled Hierarchically Engineered Superlattice Structures)—nano-engineered thermoelectric materials capable of achieving nearly double the efficiency of traditional devices.

Their findings, published in Nature Communications, mark a pivotal milestone in the development of high-performance thermoelectric refrigeration. These solid-state systems eliminate the need for harmful refrigerants or bulky mechanical compressors and are now not only viable—but scalable.

From National Security to Kitchen Cooling: A Decade of Innovation

Originally developed for national security applications, CHESS materials represent over 10 years of research at APL. They are manufactured as thin films using metal-organic chemical vapor deposition (MOCVD), a scalable, semiconductor-compatible process also used in solar cells and LED production. This opens the door to integrating CHESS devices into standard semiconductor fabs for mass production.

“The results are striking,” said Rama Venkatasubramanian, APL’s chief technologist for thermoelectrics. “With CHESS, we achieved nearly a 100% improvement in material-level efficiency, translating to 75% gains at the device level and 70% in fully integrated refrigeration systems—all at room temperature.”

A New Benchmark for Solid-State Thermoelectric Cooling

Thermoelectric cooling works by moving heat using electrons through semiconductor materials, eliminating the need for moving parts and toxic refrigerants. Historically, bulk thermoelectric materials have been inefficient and unsuitable for high-capacity or large-scale refrigeration.

The CHESS technology changes that paradigm. It uses just a grain-of-sand-sized amount of material—0.003 cm³ per unit—yet achieves dramatic performance gains in practical refrigeration scenarios. Tests conducted in collaboration with Samsung showed validated improvements through standardized refrigerator testing and thermal modeling.

From Micro-Fridges to Macro Impact: Scalable Cooling for the Future

APL researchers envision these nano-engineered modules powering everything from compact electronics to large building HVAC systems, similar to the scaling success of lithium-ion batteries. With growing environmental pressure to eliminate hydrofluorocarbon refrigerants and reduce energy usage, this solid-state approach provides a compelling path forward.

Jon Pierce, MOCVD growth lead at APL, emphasized the role of scalable manufacturing: “MOCVD is already used across commercial industries. That’s what makes CHESS a practical candidate—not just a lab innovation, but a real-world solution ready for mass deployment.”

Beyond Cooling: Energy Harvesting, Wearables, and Human-Machine Interfaces

The CHESS materials are also poised to revolutionize thermoelectric energy harvesting. They can convert temperature differences—such as body heat—into usable power, enabling innovations in wearables, prosthetics, and distributed electronics. Applications could span everything from spacecraft to biomedical sensors.

With AI-driven optimization on the roadmap, CHESS modules could soon manage energy consumption dynamically across refrigeration zones, data centers, or next-gen HVAC networks. The thermoelectric renaissance is here—and it’s built at the nanoscale.

Original article: https://www.sciencedaily.com/releases/2025/09/250919085242.htm

This blog post was prepared with the assistance of AI technologies for content generation and formatting.

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📘 Download our latest company brochure to explore our software features, capabilities, and success stories: PWmat PDF Brochure

🎁 Interested in trying our software? Fill out our quick online form to request a free trial and receive additional information tailored to your R&D needs: Request a Free Trial and Info

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

Comments

Post a Comment

Popular posts from this blog

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

Water Simulations Under Scrutiny: Researchers Confirm Methodological Errors

Image
Accurate water simulations are vital for research in biomolecular structures, drug discovery, and materials science. But a recent study from Oak Ridge National Laboratory (ORNL) has revealed a potential pitfall in long-standing computational methods, raising concerns about the reliability of countless molecular dynamics studies. The Time Step Problem in Simulations At the heart of the issue is the “time step” used in simulations—the small intervals at which calculations are performed. Since the 1970s, scientists have used time steps of 2 femtoseconds (quadrillionths of a second) to simulate molecular motion efficiently. However, the ORNL team has shown that using this standard can introduce significant errors when modeling liquid water, potentially leading to inaccurate predictions of properties such as density, pressure, and hydration energies. “Using longer time steps is tempting because it reduces computational cost,” explained senior scientist Dilip Asthagiri. “But a...
Read more

CrystalGPT: Redefining Crystal Design with AI-Driven Predictions

Image
July 18, 2025 In a major leap for materials chemistry, researchers in the UK have developed Molecular Crystal Representation from Transformers (MCRT) —an AI model likened to ChatGPT for its transformative impact. Dubbed "CrystalGPT" , this foundation model rapidly predicts the physical properties of molecular crystals, revolutionizing how chemists design materials in silico . The AI Revolution in Crystal Design Understanding and predicting the properties of molecular crystals is critical for designing new materials for applications ranging from pharmaceuticals to energy storage. Traditional computational approaches, though powerful, are often resource-intensive and slow. Machine learning methods such as Smooth Overlap of Atomic Positions (SOAP) and atom-centred symmetry functions (ACSFs) have offered improvements, but they struggle with large datasets and subtle structural nuances. CrystalGPT, developed by a team led by Andy Cooper at the University of Liverpool, o...
Read more