Effective thermal management remains a critical challenge in designing and operating next-generation electronics, data centers, and energy systems. Devices are steadily shrinking and handling increased power densities. Traditional cooling strategies, such as heat sinks and immersive cooling systems, fall short in delivering the targeted, localized cooling needed to prevent or address thermal hotspots. Current solutions for localized hotspot cooling require active, energy-intensive methods like pumping of coolants and complex thermal architecture design.       To overcome these challenges, UC Berkeley researchers present a transformative passive method for localized, autonomous cooling of hotspots. The cooling system delivers effective, localized cooling across various device surfaces and geometries, including those geometries wherein cooling media must move against gravity. The benefits of the present system will be appreciated for computer chip and other electronics cooling, microgravity applications, battery thermal management. Beyond thermal management, the underlying system may also open novel avenues in fluid manipulation and energy harvesting.

Conventional methods for dehydrogenation often require harsh conditions and produce harmful by-products. This invention introduces a novel approach using semiconductor-based photo redox catalysts to facilitate sustainable dehydrogenation reactions. The technology, developed by researchers at UC Berkeley, offers a more efficient and environmentally friendly alternative to existing processes by using light energy to drive the dehydrogenation of alcohols and amines. This process not only operates under milder conditions but also promotes the production of valuable chemical products while minimizing waste.

This innovation addresses the limitations of producing proteins with non-natural monomers (NNMs), which have valuable applications in drug discovery and materials science. Researchers at UC Berkeley have developed novel PylRS-tRNAPyl pairs that enable the efficient incorporation of NNMs into proteins. This technology provides a significant advantage over existing methods by offering a broader range of NNM incorporation with high specificity and efficiency.Provided are compositions and methods for creating proteins that contain non-natural monomers (NNMs) using new PylRS-tRNAPyl pairs.  This technology works by introducing a subject PylRS, a tRNA, and an NNM into a host system, such as a bacterial cell, eukaryotic cell, or an in vitro translation system, allowing the tRNA to be acylated with the NNM by the PylRS.

The DALMSI framework is a novel method for inferring a user's latent mental states from their digital activity. Researchers at UC Berkeley developed this technology to address the limitations of traditional, intrusive methods like surveys or physical sensors. The system works by receiving and segmenting a stream of digital interaction data, and then uses neural encoding to transform these segments into representations, which a machine learning model maps to specific internal states like cognitive load or emotional state. This offers a non-intrusive, real-time, and scalable solution for understanding user experience without requiring a user's conscious effort or special hardware.

An electrolyte containing a compound with a unique molecular structure is disclosed for use in symmetric, air-tolerant and membraneless all-organic flow batteries. The innovation addresses challenges in large-scale energy storage, offering a safer and more efficient alternative to conventional batteries that rely on metal-based active materials, which can be toxic or have limited availability. The novel technology, developed by researchers at UC Berkeley, features a single active compound in the electrolyte that functions as both the anolyte and catholyte, eliminating the need for a costly and failure-prone membrane. This design simplifies the battery's architecture, improves its resilience to air exposure, and enhances its overall efficiency and longevity.

The rapid and accurate analysis of real-time quantitative polymerase chain reaction (qPCR) data is critical for precise disease diagnostics, genetic research, and pathogen detection. However, manual interpretation is prone to human error, and current automated systems often struggle with noise and variability, leading to misdiagnosis or inaccurate results. Researchers at UC Berkeley have developed a Deep Learning System for Enhanced qPCR Data Analysis that addresses these challenges. The system utilizes an advanced deep learning model to analyze raw qPCR data in real-time, significantly improving diagnostic accuracy by identifying subtle patterns and anomalies that are difficult for human experts or conventional software to detect. This innovative approach leads to more reliable and faster results compared to traditional methods.

The Generalized Apparatus for Behavioral Assessment (GABA) is an automated system designed to precisely deliver liquid and/or air stimuli to a subject while holding them in a fixed position. The system comprises a base, a platform, and one or more restraining arms to position the subject. A translational manipulator holds a plurality of spouts, which are connected to a liquid delivery system and an air delivery system. A controller orchestrates the delivery of stimuli, allowing for highly controlled and repeatable behavioral assessments. The system's modular design and use of a translational manipulator for multiple spouts enable a wide range of experimental setups and protocols.

This invention describes a novel method for the inhibition of specific potassium ion channels, particularly TWIK-related arachidonic acid-activated K+ channels (TRAAK), using cannabinoid compounds. The research demonstrates that these compounds can be used to modulate the function of these channels, which are implicated in various neurological and physiological disorders, including epilepsy. This approach presents a new pharmacological strategy for targeting these channels and developing treatments for associated conditions.