Researchers at the University of California, Davis have developed a method and apparatus for precise, label-free measurements of reactions at a molecular or near atomic level using an oblique-incidence optical analysis technique.

Researchers at the University of California, Davis have developed a method to design optimized current profiles for lithium-ion batteries using analytic sensitivity functions. By leveraging a reduced electrochemical model, the approach enables fast and accurate identification of key parameters, improving battery management systems and reducing testing time.

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.

The use of standing surface acoustic waves (SSAWs) in microfluidic channels gained significant momentum when researchers demonstrated size-based cell separation (acoustophoresis) using lateral acoustic forces. Using interdigitated transducers (IDTs) positioned on piezoelectric substrates, SSAWs were found to create pressure nodes along the channel width, allowing larger particles to experience greater acoustic radiation forces and migrate toward these nodes faster than smaller particles. Acoustic-based microfluidic devices were successfully applied to circulating tumor cell (CTC) isolation from clinical blood samples in ~2015, demonstrating recovery rates >80% using tilted-angle standing surface acoustic waves, though these systems relied primarily on size-based separation principles. The integration of acoustic methods with microfluidics offered key advantages including label-free operation, biocompatibility, non-contact manipulation, and preservation of cell viability, addressing limitations of earlier methods like centrifugation, FACS, and magnetic separation that could damage cells or require labeling. Despite these advances in acoustic microfluidics, significant challenges persist in affinity-based rare cell isolation, particularly mass transport limitations in microfluidic channels operating at high Peclet numbers (Pe>10⁶) where convective flow dominates over diffusion. In traditional microfluidic affinity capture systems, cells flow predominantly in the center of laminar flow channels where fluid velocity is highest, resulting in minimal interaction with capture agents immobilized on channel walls and requiring extremely long channels or impractically slow flow rates to achieve adequate capture efficiency. The extremely low concentration of CTCs , combined with their phenotypic heterogeneity and the low diffusion coefficients of cells creates a "needle in a haystack" challenge that existing acoustic separation methods based solely on size discrimination cannot adequately address.

Position-sensitive radiation detection has been used in semiconductor detector development for decades. Traditional approaches have relied on segmented electrodes to achieve spatial resolution. Conventional semiconductor radiation detectors utilize segmented electrodes where each electrode segment is physically separated and individually read out to determine the position of radiation interactions. Traditional segmented electrode designs have long suffered from highly non-uniform electric fields within the detector volume, particularly at electrode edges and corners. These field concentrations can cause premature breakdown and inconsistent charge collection. This non-uniformity can also lead to position-dependent signal variations, pulse time dispersion, and potential electrical connections between adjacent electrodes from radiation damage. Moreover, common approaches to manufacturing of segmented electrodes requires precise mask alignment and complex fabrication processes, resulting in higher production costs and reduced yields.

The primary challenge in non-invasive brain stimulation, such as Transcranial Focused Ultrasound Stimulation (TFUS), is providing precise, localized, and mechanistically distinct control over neural activity. Standard TFUS is believed to function primarily through mechanical deformation of tissue, limiting the ability to selectively enhance or separate different types of neural modulation. Addressing this, UC Berkeley researchers have developed a novel system for the Activation of Neural Tissue by FUS in the Presence of a Magnetic Field Gradient. This unique mechanism, which generates electromagnetic induction from acoustic motion, provides a new physical mechanism to activate or modulate nervous tissue entirely separate from the mechanical effects of the ultrasound alone, offering a higher degree of experimental control and therapeutic precision compared to conventional FUS.

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.

Scaled neural sensing has been pursued for decades. Physical limitations associated with electrical (electrode-based) field recordings hinder advances in both field of view and spatial resolution. Electrochromic plasmonics (electro-plasmonics) has emerged as a rapidly advancing field combining traditional electrochromic materials with plasmonic nanostructures, including recent demonstrations of electrochromic-loaded plasmonic nanoantennas for optical voltage sensing. Existing optical electrophysiology techniques face critical limitations including poor signal-to-noise ratios due to low photon counts from genetically encoded voltage indicators, which have small cross-sections and low quantum yields. Fluorescent voltage indicators suffer from photobleaching, phototoxicity, and require genetic modifications that limit their clinical applicability. Current electrochromic devices also struggle with limited cycling stability, slow switching times, and restricted color options, and conventional plasmonic sensors exhibit inherently low electric field sensitivity due to high electron densities of metals like gold and silver. Current approaches to electro-plasmonics lack stable, high-contrast optical modulators that can operate at sub-millisecond speeds while maintaining human biocompatibility.

An advanced system leveraging machine learning and computer vision for real-time, smart control of flow boiling processes to optimize thermal management.

This technology revolutionizes hydrogen production by using induction heating for catalytic methane decomposition, significantly increasing hydrogen yield.

An innovative, AI-driven approach for non-intrusive analysis and defect detection in multi-tiered chiplets, enhancing microelectronics packaging.

Multimode optical fiber was first introduced in astrophotonics applications as “light pipes” to transport light from telescopes to instruments. The integration of multimode optical fiber helped to maximize light collection but offered little control over the propagation modes from the collected light, which affects the quality and speed of light transmission. Single-mode optical fiber used in interferometry proved invaluable for spatial filtering and wavefront correction, providing a stable, reliable, and flexible way to guide light in precision sensing and imaging. Photonic lanterns were conceived in the early 2000s to help bridge a gap between the light-gathering efficiency of multimode optical fiber and the precision of single-mode optical fiber. Photonic lantern devices have reasonably addressed the efficient conversion needs between multimode/ multi-modal and multiple single-mode light paths. However, challenges remain with respect to improving and scaling of photonic lantern devices, including coupling efficiency/losses, bandwidth limitations, and high-order mode (>20) capabilities.

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Researchers at the University of California, Davis have developed a sophisticated soil nitrate sensing system designed to accurately measure soil pore water nitrate concentrations, enhancing sustainable agriculture and environmental monitoring.

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An innovative technique for creating high-sensitivity Whispering Gallery Mode (WGM) sensors through advanced microbubble resonator fabrication.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter "organoid") have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Despite their potential, organoid experiments present several challenges. Organoids require a rigorous, months-long developmental process, demanding substantial resources and meticulous care to yield valuable data on aspects of biology such as neural unit electrophysiology, cytoarchitecture, and transcriptional regulation. Traditionally the data has been difficult to collect on a more frequent and consistent basis, which limits the breadth and depth of modern organoid biology. Generating and measuring organoids depend on media manipulations, imaging, and electrophysiological measurements. Historically are labor- and skill-intensive processes which can increase risks associated with experimental validity, reliability, efficiency, and scalability.

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.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of brain development, providing an exceptional tool for studying the complexities of biology. Among these, cortical organoids, comprising in part of neurons, have been instrumental in providing early insights into brain formation, function, and pathology. Functional characteristics of cortical organoids, such as cellular morphology and electrophysiology, provide physiological insight into cellular states and are crucial for understanding the roles of cell types within their specific niches. And while progress has been made studying engineered neuronal systems, decoding the functional properties of neuronal networks and their role in producing behaviors depends in part on recognizing neuronal cell types, their general locations within the brain, and how they connect.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter "organoid") have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Modern methods of interfacing with organoids involve any combination of encoding information, decoding information, or perturbing the underlying dynamics through various timescales of plasticity. Our knowledge of biological learning rules has not yet translated to reliable methods for consistently training neural tissue in goal-directed ways. In vivo training methods commonly exploit principles of reinforcement learning and Hebbian learning to modify biological networks. However, in vitro training has not seen comparable success, and often cannot utilize the underlying, multi-regional circuits enabling dopaminergic learning. Successfully harnessing in vitro learning methods and systems could uniquely reveal fundamental mesoscale processing and learning principles. This may have profound implications, from developing targeted stimulation protocols for therapeutic interventions to creating energy-efficient bio-electronic systems.

A novel dielectrophoresis (DEP)-based microfluidics method for efficient and label-free sorting of plant cells, leveraging unique dielectric properties.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter “organoid”) have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Despite their potential, organoid experiments present several challenges. Organoids require a rigorous, months-long developmental process, demanding substantial resources and meticulous care to yield valuable data on aspects of biology such as neural unit electrophysiology, cytoarchitecture, and transcriptional regulation. Traditionally the data has been difficult to collect on a more frequent and consistent basis, which limits the breadth and depth of modern organoid biology. Generating and measuring organoids depend on media manipulations, imaging, and electrophysiological measurements. Historically these are labor- and skill-intensive processes which can increase risks associated with known human error and contamination.

In PET imaging, patient motion, such as respiratory and cardiac motion, are a major source of blurring and motion artifacts. Researchers at the University of California, Davis have developed a technology designed to enhance PET imaging resolution without the need for external devices by effectively mitigating these artifacts

Current methods for training robotic policies often struggle with efficiently learning from rich, time-varying visual data, leading to brittle and data-intensive solutions. This innovation, developed by UC Berkeley researchers, addresses this challenge by introducing a robotic system that utilizes four-dimensional (4D) representations estimated directly from videos to pre-train and test an auto-regressive machine learning transformer model. By explicitly encoding space and time in a unified representation, the system allows the transformer model to leverage a much richer context than standard 2D image or 3D point cloud approaches, facilitating the learning of complex, long-horizon tasks and improving the generalization capabilities of the resulting policy. The use of 4D representations significantly enhances the policy's understanding of the dynamic environment and object interactions compared to existing alternatives, enabling more robust and efficient training of robotic systems.