NextFin News - In a development that could fundamentally alter the energy economics of the global artificial intelligence race, a collaborative team of Chinese researchers has unveiled a revolutionary cooling principle capable of inducing sub-zero temperatures in under 30 seconds. The discovery, published in the journal Nature on February 2, 2026, introduces what the scientists term the "solution pressure caloric effect." This method utilizes a mixture of water and ammonium thiocyanate (NH₄SCN) to achieve cooling efficiencies that far surpass existing commercial technologies. The research was spearheaded by the Institute of Metal Research at the Chinese Academy of Sciences, in collaboration with the Beijing High Pressure Science Research Center and Xi'an Jiaotong University.
The mechanism operates through a four-step cycle of pressurization and depressurization, analogous to the action of a wet sponge. When the aqueous solution of ammonium thiocyanate is pressurized, the salt precipitates and releases heat into the environment. Upon sudden decompression, the salt rapidly redissolves, absorbing massive amounts of thermal energy from its surroundings. In laboratory tests conducted at room temperature, the solution’s temperature plummeted by 30°C within 20 seconds. In higher-temperature environments, the temperature drop exceeded 50°C, demonstrating a theoretical efficiency of approximately 77%—a figure significantly higher than the 40-50% efficiency typical of traditional vapor compression systems.
This breakthrough arrives at a critical juncture for the technology sector. According to data cited by Tom's Hardware, cooling systems currently account for nearly 40% of the total electricity consumption in modern data centers. As U.S. President Trump’s administration continues to emphasize American leadership in AI, the energy demands of next-generation chips have become a primary bottleneck. The Chinese refrigeration industry already contributes roughly 2% to China's GDP but consumes 20% of its total electricity. By transitioning from energy-intensive gas-compression cycles to this liquid-solid phase transition model, the researchers suggest that the carbon footprint and operational costs of AI infrastructure could be drastically reduced.
From an analytical perspective, the "solution pressure caloric effect" addresses the three historical failures of solid-state cooling: low heat exchange efficiency, limited scale, and high carbon emissions. While previous solid-state materials struggled with thermal conductivity, the use of an aqueous medium in this new discovery allows for rapid heat transfer and integration into existing liquid-cooling loops. This makes it a "drop-in" candidate for the high-density server racks required for Large Language Model (LLM) training. Furthermore, ammonium thiocyanate is relatively inexpensive and, despite being a salt, is notably non-corrosive to many common industrial metals, which lowers the barrier for commercial adoption in HVAC and enterprise computing environments.
However, the path to global commercialization faces significant hurdles. Ammonium thiocyanate is hygroscopic and can cause skin irritation, necessitating specialized handling protocols and hermetically sealed systems to ensure long-term stability. Moreover, while the cooling phase is highly efficient, the pressurization phase still requires a mechanical energy input. The strategic impact of this technology cannot be overstated; as China seeks to close the gap with the U.S. in AI hardware, the ability to operate data centers at a fraction of current energy costs provides a massive competitive edge in total cost of ownership (TCO) for cloud providers.
Looking forward, the integration of this technology into the global supply chain will likely depend on the scalability of the pressure-vessel components required to maintain the cycle. If the Chinese Academy of Sciences can successfully transition this from a laboratory setting to a modular industrial product, we may see a shift in data center architecture by 2028. This would not only alleviate the strain on national power grids—a concern recently highlighted by U.S. President Trump regarding domestic energy independence—but also set a new global standard for "Green AI." The discovery signals a shift in the innovation frontier from purely silicon-based performance to the fundamental thermodynamics of the infrastructure that supports it.
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