Researchers at the University of California, Davis in collaboration with Deutsche Telekom AG have developed a system and method for monitoring network traffic by dynamically routing traffic sub-populations over fixed monitoring locations without violating traffic engineering policies. This approach leverages existing routing flexibility to collect high-quality flow data without disrupting normal traffic engineering policies

Researchers at the University of California, Davis have developed a portable apparatus that standardizes digit positioning and applies counter-resistance for improved imaging of the flexor tendon system in the hand.

Researchers at the University of California, Davis have developed a system of reusable, sterilizable 3D-printed surgical tools that enables safe, precise intraoperative deployment of Neuropixels probes within standard neurosurgical workflows.

Researchers at the University of California, Davis have developed a solid-state X-ray imager with high temporal resolution.

The implementation of cost-effective and reliable energy storage solutions, such as redox flow batteries, is often hindered by the complexity and expense of accurately monitoring their state of charge (SOC) and state of health (SOH). To address this, a novel approach using low-cost management systems and methods has been developed for electrochemical cells based on viologen, particularly pyridinium redox flow batteries. This innovation centers on pH signaling and regulation to enable real-time SOC and SOH monitoring. The viologen species' electrochemical processes naturally induce localized pH changes, and by monitoring and regulating the pH within the cell, researchers can obtain immediate, actionable data on the battery's operating condition. This pH-based system offers a simple, integrated, and economical alternative to conventional, often more complex, monitoring techniques.

Researchers at the University of California, Davis have developed an advanced multi x-ray source array system employing dual cathode designs that enhance computed tomography (“CT”) imaging by enabling pulsed, spatially multiplexed x-ray emission with reduced artifacts.

The deployment of robotic systems in real-world environments is often limited by the "sim-to-real gap," where policies trained in digital simulations fail to account for the complex, multisensory feedback of physical reality. Researchers at UC Berkeley have developed a novel method for training multimodal sim-to-real robot policies by integrating generative audio models with traditional physics-based simulators. This framework uses a generative model to synthesize realistic audio data that corresponds to simulated physical interactions, creating a rich, multimodal dataset for policy learning. By training on both simulated physics and generated sensory data, the system enables robots to develop more robust and adaptive behaviors that translate seamlessly from virtual training environments to complex real-world tasks.

         Dust accumulation on solar panels, particularly in desert regions, can cause significant power losses without frequent water-based cleaning. With the global solar capacity rising, current cleaning methods yield high operational costs, consume billions of gallons of water annually, and pose sustainability and resource challenges.         To overcome these challenges, UC Berkeley researchers have developed a passive anti-soiling coating, which can effectively repel dust particles without energy or resources. The anti-soiling performance can be triggered by an onset temperature as low as 40 °C—common in most operating environments—and has been demonstrated to repel nearly all dust particles in preliminary studies. The approach is practical and highly promising for large-scale deployment.

      The widespread occurrence of nutrient-rich and metal-contaminated wastewater presents an environmental challenge and untapped economic opportunity. Ammonia, copper, and phosphorous are prime targets. For example, ammonia is industrially produced by the Haber-Bosch process, a highly energy-intensive (~12.5 kWh/kg-N to convert N2 to ammonia, consuming 1-2% of global energy usage) and greenhouse gas-emitting (~1.2% of global CO2 emissions) technique. After use, primarily as fertilizer, nearly 50% of all U.S.-consumed ammonia ends up in municipal wastewater and animal feedlot retention systems. Technologies presently proposed for recovering critical nutrients and metals from wastewater are limited in scalability by high energy demands, costly chemicals or membrane requirements, low efficiencies, or fouling challenges.       UC Berkeley researchers have developed and demonstrated a low-cost, robust, and near-zero-energy reactor that simultaneously recovers ammonia and other valuable ions (e.g., P and Cu) from wastewater streams. The reactor is driven by sunlight or low-grade waste heat, such that it eliminates the need for external pumping—further cutting energy consumption and capital cost. The functional material is an inexpensive cloth that is also roll-to-roll compatible, making it economically scalable and easy to manufacture. The reactor can be implemented within wastewater streams including municipal wastewater, animal feedlot wastewater, and organic waste digestate. It may further be adapted to recover other valuable resources, such as lithium, from sources like mining wastewater and landfill leachate. It may even be extended beyond nutrient and metal recovery to separation or pre-concentration of volatile organic compounds such as ethanol and methanol from aqueous solutions.

Traditional environmental policymaking often struggles to efficiently target interventions to achieve multiple, complex air quality goals simultaneously across a geographic area. This innovation, developed by UC Berkeley researchers, addresses this challenge by providing a sophisticated, multi-objective optimization method for targeted reduction of air pollution. The method generates a comprehensive mitigation pathway by integrating several modules: a forward module to model pollutant concentrations, a target concentration surface that defines the policy goals, a prioritization module to assess uncertainty and importance via a prioritization covariance matrix, and a Bayesian inversion module to estimate optimum emissions required to meet the target. This systematic, data-driven approach culminates in a mitigation pathway that guides the performance of specific pollution control measures, offering a significant advantage over conventional, less targeted policy-making by ensuring resources are directed where they will have the maximum environmental impact.

UC Berkeley researchers have developed a novel dispersion system for agricultural and environmental payloads, including seeds, soil amendments, miniature soil sensors, and so forth. Dispersive packages are biodegradable and biomimetically designed with similarities to natural seeds. Aerodynamic properties control large-area dispersions, while importantly, tunable gyroscopic properties are programmed for penetration parameters, such as depth, upon impact. Payload distribution can be fine-tuned accounting for local soil moisture and grain-size.

Current sustainable building materials often lack the high structural strength needed for demanding applications, limiting their use in load-bearing construction. Addressing this opportunity, UC Berkeley researchers have developed SEA-BOARD, a novel structural panel fabricated from marine-derived polysaccharides. This innovation utilizes a proprietary, stepwise process involving polysaccharide extraction, nanofiber alignment, and thermal densification to configure the macroalgal biomass into a high-strength, hot-pressed panel. This engineered material is structurally superior and potentially more environmentally sustainable than many traditional wood-based or synthetic alternatives.

High-conversion-ratio power converters used in compact Point-of-Load (PoL) applications, such as data centers or portable electronics, often face the challenge of large size and weight due to the necessary energy-storage components, particularly flying capacitors, while also struggling with switching losses that reduce efficiency. This innovation, developed by UC Berkeley researchers, addresses these issues with a novel regulating hybrid switched-capacitor (HSC) power converter topology referred to as a "Dual Inductor Switching Bus Converter" (DISB converter). The DISB converter combines an initial 2:1 switched-capacitor conversion stage with a Symmetric Dual-Inductor Hybrid (SDIH) conversion stage, capitalizing on the benefits of both. The initial 2:1 voltage reduction significantly reduces the overall volume and weight of the flying capacitors, while the SDIH stage contributes a reduced component count and an excellent switch stress figure of merit. Crucially, a proposed auxiliary circuit block enables near-zero-voltage conditions (partial soft-switching) within the initial 2:1 stage, which significantly improves the converter's overall efficiency.

Flying Capacitor Multilevel Converters (FCMLCs) are widely used in high-power applications, but they present significant control challenges, particularly in maintaining stable and balanced voltages across the numerous flying capacitors while achieving continuous and fast output voltage regulation. This innovation, developed by UC Berkeley researchers, discloses a novel current-programmed modulator with smooth bin transitions that inherently addresses these challenges. The modulator achieves continuous full-range output voltage regulation and, critically, fast flying-capacitor voltage-balancing dynamics . By programming the current and ensuring smooth transitions between the modulator's operational bins, the technology overcomes the limitations of traditional control methods, resulting in a more reliable, efficient, and robust converter topology suitable for demanding high-power applications.

Researchers at the University of California, Davis have developed a technology that introduces a highly adaptable, lightweight robotic end effector designed for complex manipulation tasks in automation.

The efficient and selective recovery of high-value metals, such as precious metals, from complex fluid streams or industrial waste is a significant challenge in metallurgy and environmental remediation. Existing separation methods often lack sufficient selectivity, resulting in inefficient recovery and high processing costs. This innovation, developed by UC Berkeley researchers, addresses this problem by providing novel polymer sorbents and composite membranes designed for the selective separation and absorption of precious metals in a fluid stream or sample. The disclosure relates to the use of these specially engineered absorbents and composite membranes, which offer superior selectivity for high-value metals. This technology provides a significantly more efficient and environmentally sound method for metal recovery and purification compared to traditional, less-selective chemical or physical separation processes.

Accurate automatic pronunciation assessment, particularly the core subtask of phonetic error detection, is significantly hampered by speech variability stemming from accents and dysfluencies, which current models fail to capture effectively. This innovation, developed by UC Berkeley researchers, addresses this by disclosing a verbatim phoneme recognition framework specifically designed to transcribe what speakers actually say rather than what they are supposed to say . The framework uses multi-task training combined with novel phoneme similarity modeling. The present disclosure also includes the development and open-sourcing of VCTK-accent, a simulated dataset containing phonetic errors, and proposes two novel metrics for assessing pronunciation differences. This work establishes a new, more accurate benchmark for phonetic error detection, enabling more precise and effective articulatory feedback for pronunciation training.

      On-chip ultrafast light devices with a compact footprint and low cost would provide a practical platform for applications such as optical signal processing, molecular sensing, microwave generation and nonlinear optical processes. With the help of recent advances in nanofabrication techniques, the ability to reach low propagation loss on-chip has driven the development of high-quality (Q) factor microresonators. These microresonators allow for microcomb and pulse generation under intense continuous wave (CW) pumping. However, low nonlinear conversion efficiencies and high repetition rates, fixed by the resonator geometry, make achieving ultrashort pulses with high peak power remains an ongoing challenge.       To overcome these challenges, UC Berkeley researchers have demonstrated the integration of an electro-optic-comb system and dispersion-engineered nonlinear waveguides on a thin-film lithium niobate platform. The compact, on-chip device can achieve 35-fs pulse generation, corresponding to 6.7 cycles at 1550 nm, via higher-order soliton compression. The present invention facilitates development of ultrafast nano-optics and nano-electronics.

Researchers at the University of California, Davis have developed a reversed feedback amplifier design for enhanced mm-wave signal amplification.

Traditional methods for monitoring a building's structural health, particularly its natural frequencies and damping ratios, typically rely on expensive, permanently installed sensor systems, which are not widely accessible. This innovation, developed by UC Berkeley researchers, provides a highly scalable and cost-effective method for Monitoring Building Structural Health using Smartphones and Ambient Vibrations. The method leverages smartphones equipped with the MyShake earthquake early warning application to measure the ambient vibrations of a building. By analyzing these vibrations, the application can accurately determine key structural health parameters, namely the building's natural frequencies and damping ratios. This technique transforms readily available personal devices into powerful structural monitoring tools, offering a vastly more accessible and lower-cost solution than existing dedicated sensor networks.

Researchers at the University of California, Davis have developed a technology that introduces an alternative topology for Fibonacci switched-capacitor converters that significantly reduces switch losses and improves efficiency.

Researchers at the University of California, Davis have developed method for separating quasi-periodic mixed-signals using a single data trace, enhancing wearable sensor applications.

Researchers at the University of California, Davis have developed a technology that introduces new electrolyte compositions that significantly enhance the stability and efficiency of non-aqueous flow batteries.

       Spectral machine vision collects both the spectral and spatial dependence (x,y,λ) of incident light, containing potentially useful information such as chemical composition or micro/nanoscale structure.  However, analyzing the dense 3D hypercubes of information produced by hyperspectral and multispectral imaging causes a data bottleneck and demands tradeoffs in spatial/spectral information, frame rate, and power efficiency. Furthermore, real-time applications like precision agriculture, rescue operations, and battlefields have shifting, unpredictable environments that are challenging for spectroscopy. A spectral imaging detector that can analyze raw data and learn tasks in-situ, rather than sending data out for post-processing, would overcome challenges. No intelligent device that can automatically learn complex spectral recognition tasks has been realized.       UC Berkeley researchers have met this opportunity by developing a novel photodetector capable of learning to perform machine learning analysis and provide ultimate answers in the readout photocurrent. The photodetector automatically learns from example objects to identify new samples. Devices have been experimentally built in both visible and mid-infrared (MIR) bands to perform intelligent tasks from semiconductor wafer metrology to chemometrics. Further calculations indicate 1,000x lower power consumption and 100x higher speed than existing solutions when implemented for hyperspectral imaging analysis, defining a new intelligent photodetection paradigm with intriguing possibilities.

      Microwave impedance microscopy (MIM) is an emerging scanning probe technique that enables non-contact, nanoscale measurement of local complex permittivity. By integrating an ultrasensitive, phase-resolved microwave sensor with a near-field probe, MIM has made significant contributions to diverse fundamental and applied fields. These include strongly correlated and topological materials, two-dimensional and biological systems, as well as semiconductor, acoustic, and MEMS devices. Concurrently, notable progress has been made in refining the MIM technique itself and broadening its capabilities. However, existing literature has focused exclusively on linear MIM based on homodyne architectures, where reflected or transmitted microwave is demodulated and detected at the incident frequency. As such, linear MIM lacks the ability to probe local electrical nonlinearity, which is widely present, for example, in dielectrics, semiconductors, and superconductors. Elucidating such nonlinearity with nanoscale spatial resolution would provide critical insights into semiconductor processing and diagnostics as well as fundamental phenomena like local symmetry breaking and phase separation.       To address this shortcoming, UC Berkeley researchers have introduced a novel methodology and apparatus for performing multi-harmonic MIM to locally probe electrical nonlinearities at the nanoscale. The technique achieves unprecedented spatial and spectral resolution in characterizing complex materials. It encompasses both hardware configurations enabling multi-harmonic data acquisition and the theoretical and calibration protocols to transform raw signals into accurate measures of intrinsic nonlinear permittivity and conductivity. The advance extends existing linear MIM into the nonlinear domain, providing a powerful, versatile, and minimally invasive tool for semiconductor diagnostics, materials research, and device development.