Comprehensive radiation detection across the spectral range requires distinct systems for ionizing and non-ionizing imaging because each technology faces unique architectural hurdles. Modern visible light detection has successfully transitioned from passive plates to digital Active Pixel Sensors (APS) by leveraging Complementary Metal-Oxide-Semiconductor (CMOS) technology to provide every pixel with its own dedicated amplifier and active circuitry. Ionizing radiation detection like X-ray and gamma-ray has relied on exotic scintillators to convert radiation into light, a process prone to lateral light scattering and degraded spatial resolution. Recent advancements in ionizing radiation have shifted toward direct conversion materials like amorphous selenium (a-Se), which transform X-rays directly into electrical charges. However, these direct-conversion devices do not scale to larger areas without significant noise being a factor. This is primarily due to thin-film transistor (TFT) backplanes which, unlike their CMOS counterparts, lack the local amplification necessary to maintain a high signal-to-noise ratio.

Reactive oxygen species (ROS), including hydrogen peroxide and peroxynitrite, play dual roles as essential signaling molecules and high-stress markers of cellular damage. However, imaging these volatile species in live biological systems is often hindered by diffusion and poor signal localization. Researchers at UC Berkeley have developed a "tandem" activity-based sensing and labeling strategy that overcomes these challenges. This technology utilizes selective chemical probes that, upon reacting with a specific ROS, undergo a transformation that simultaneously triggers a fluorescent signal and anchors the probe to nearby cellular proteins. By "trapping" the signal at the site of its production, this dual-action mechanism allows for high-resolution, localized imaging of oxidative stress and signaling events within complex cellular environments.

A lightweight, head-mounted OCT system enabling real-time, high-resolution brain imaging in freely moving small animals.

Organoid/brain slice immobilization for microelectrode arrays (MEAs) and organoid-on-chip platforms have traditionally depended on hydrogels, harp-style grids, or microfluidic confinement, each with its own set of pros and cons with respect to stability, standardization, and impact on electrophysiology. Hydrogels (e.g., Polyethylene glycol or PEG, extracellular matrix like Matrigel) are widely used to immobilize 3D neural tissues on MEAs. These are known to swell, drift, and alter mechanical microenvironments, which in turn modulate network firing, synchrony, and bursting behavior. Mechanical retention via harp slice grids or similar harp devices is a long-standing practice in acute brain slice and organoid electrophysiology. These devices are typically standardized, fragile, and poorly matched to diverse well and tissue geometries. ​Microfluidic organoid chips and specialized 3D MEAs (e.g., e-Flower, organoid-on-chip platforms) have recently emerged to enable hydrogel-free trapping/encapsulation of organoids for imaging and recordings, but they often require bespoke chip designs and overly complex flow control setups. There is a lack of geometry-agnostic devices for mechanically immobilizing diverse organoids on commercial MEAs that feature consistent stability, uniform and/or tailored contact, and with minimal perturbation of electrophysiological readouts.

Achieving seamless robotic interaction with physical environments requires a sophisticated blend of sensory perception and logical reasoning. UC Berkeley researchers have developed "RealWorldPlay," a physical artificial intelligence system designed to enhance robotic action through a unified multimodal reasoning framework. The system integrates a visuo-tactile policy—combining sight and touch—with a large language model (LLM) that provides real-time verification feedback and strategic planning. By utilizing a "world model" to generate self-training data, the platform allows robots to autonomously set goals and learn from simulated scenarios, ensuring that their physical actions are both reasoned and verified before execution.

Determining the exact direction of a sound source typically requires large microphone arrays and significant computational power. Researchers at UC Berkeley have developed an intelligent acousto-electrical metamaterial system that simplifies this process. The technology utilizes a specialized acoustic transducer divided into multiple interconnected sections. Each section contains a unique arrangement of piezoelectric metamaterials designed to generate specific electric signals when stimulated by sound waves. Crucially, these sections possess distinct acoustic beam patterns—geometric sensitivities to sound—that allow the system to differentiate between incoming angles. Because the sections are in physical contact, they work in tandem to provide highly accurate "direction of arrival" (DOA) data within a compact, hardware-efficient form factor.

Monitoring the structural integrity of buildings traditionally requires expensive, specialized sensor networks that are difficult to deploy at scale. UC Berkeley researchers have developed a novel approach that leverages the existing network of smartphones equipped with the MyShake earthquake early warning application. By utilizing the highly sensitive accelerometers within millions of consumer devices, the system measures the natural frequencies and damping ratios of buildings through ambient vibrations. This crowdsourced data provides a real-time, large-scale assessment of structural health across entire urban environments. The platform effectively transforms everyday mobile devices into a distributed seismic monitoring array, allowing for continuous observation of building performance without the need for dedicated hardware installations.

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.

Quantifying greenhouse gas emissions is a critical component of climate change research and environmental management. To facilitate long-term, high-frequency monitoring, UC Berkeley researchers have developed a fully autonomous methane flux chamber system. This continuously and remotely operable technology integrates a specialized methane sensor and an automated pump system within a flux chamber to measure gas exchange between the ground and the atmosphere. The system features a controller that manages evacuation and fresh air intake cycles based on real-time sensor data. Equipped with its own power source, data storage, and network connectivity, the device can operate in remote locations and transmit measurement data to external servers without the need for manual intervention.

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 imaging techniques often rely on bulky hardware or complex computational methods to resolve depth. UC Berkeley researchers have developed a three-dimensional imaging system that utilizes piezoelectric micromachined ultrasound transducers to capture high-resolution spatial data with an integrated approach that allows for compact, high-performance imaging that can be used in a variety of environments where traditional optical or radar systems might be limited.

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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