An innovative advanced material coating with superior cooling performance across all wavelengths that is crucial for energy consumption and heat management applications.

Something that is chiral cannot be superposed over its mirror image, no matter how it is shifted (ex. our hands). These two mirror images, called enantiomers, rotate plane-polarized light in opposite directions.Chiral nanostructures have unique materials properties that can be used in many applications. In pharmaceutical research and development, chiral analysis is critical, as one enantiomer may be more effective than the other. Researchers at UC Santa Cruz have developed new ways of performing enantiomeric analyses using the plasmonic circular dichroism absorption qualities of nanostructures. 

      State-of-the-art scanning probe microscopy (SPM) systems, including microwave impedance microscopy (MIM) and near-field scanning microscopy (NSOM), typically operate in a dynamic, non-contact “tapping” mode. Lock-in detection at the probe cantilever’s resonant mechanical oscillation frequency mitigates effects of drift and achieves high measurement sensitivity of local material characteristics. Electrical, mechanical, or other material properties can be measured down to the nanoscale. However, a full time-domain tip-sample response would yield a much richer data set. Unfortunately, existing methodologies require moving the entire scan head to sweep the tip-sample separation at rates far below the resonant frequency of the cantilever or tuning fork—yielding slow scan speeds and outputs vulnerable to drift, 1/f noise, and stray coupling.       To overcome these challenges, UC Berkeley researchers have leveraged high-speed data acquisition, wideband detection electronics, and modern real-time computing to acquire hyperspectral datasets at twice the mechanical resonant frequency of the probe. The invention captures up to hundreds of thousands of curves per second, without sacrificing scan speed, resolution, or stability. It can be straightforwardly integrated on most commercial SPM platforms, and for a wide range of resonantly driven probes, including cantilevers, quartz tuning forks, and qPlus sensor. Among other benefits, the technique enables novel post-processing capabilities, including retrospective enhancement of spatial resolution.

A breakthrough in growing long single-walled carbon nanotubes (CNTs) with direct electrical contact and exceptional conductivity.

This technology addresses the challenge of selectively separating precious and high-value metals from various fluid streams. Researchers at UC Berkeley have developed novel polymer absorbents and composite membranes for the efficient and selective separation of these metals from samples or fluid streams. This innovation provides a more effective and precise method for metal recovery compared to existing separation techniques.

A revolutionary optical technology designed to restore peripheral vision in patients with eye diseases through the integration of optical metasurfaces on eyewear.

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.

      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.

      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.

Researchers at the University of California, Davis have developed a technology that introduces a novel approach to hardware security using photonic physically unclonable functions for true random number generation and biometric ID.

Researchers at the University of California, Davis have developed a photothermal patterning flow cell that enables precise and efficient patterning of polymer films, compatible with existing cleanroom photolithography equipment.

Current methods for designing metamaterials to achieve a specific, complex physical response curve are often time-consuming, computationally intensive, and struggle with precisely programming nonlinear functional responses. This innovation, developed by UC Berkeley researchers, addresses this by offering a novel, accelerated inverse design method that leverages a hybrid machine learning approach combining imitation learning and reinforcement learning with Monte Carlo tree search (MCTS). This unique combination allows for the rapid and precise generation of metamaterial structures that meet a plurality of target physical response features, significantly outperforming traditional iterative or purely generative design methods in efficiency and programmability. The resulting metamaterial designs exhibit highly programmable and non-intuitive functional properties.

Researchers at the University of California, Davis (“UC Davis”) have developed an optical absorber/emitter for thermophotovoltaics application with a tunable emission wavelength.

Researchers at the University of California, Davis have developed an optical lens module that uses a metalens or a metalens array having a controllable angular field-of-view.

Researchers at the University of California, Davis have developed an algorithm for designing and identifying complex structures having custom release profiles for controlled drug delivery.

      Biologics are antibodies produced by genetically engineered cells and are widely used in therapeutic applications. Examples include pembrolizumab (Keytruda) and atezolizumab (Tecentriq), both employed in cancer immunotherapy as checkpoint inhibitors to restore T- cell immune responses against tumor cells. These biologics are produced by engineered cells in bioreactors in a process that is highly sensitive to the bioreactor environment, making it essential to integrate process analytical technologies (PAT) for closed-loop, real-time adjustments. Recent trends have focused on leveraging integrated circuit (IC) solutions for system miniaturization and enhanced functionality, for example enabling a single IC that monitors O2, pH, oxidation-reduction potential (ORP), temperature, and glucose levels. However, no current technology can directly and continuously quantify the concentration and quality of the produced biologics in real-time within the bioreactor. Such critical measurements still rely on off-line methods such as immunoassays and mass spectrometry, which are time-consuming and not suitable for real- time process control.       UC Berkeley researchers have developed a microsystem for real-time, in-vivo monitoring of antibody therapeutics using structure-switching aptamers by employing an integrator-based readout front-end. This approach effectively addresses the challenge of a 100× reduction in signal levels compared to the measurement of small-molecule drugs in prior works. The microsystem is also uniquely suited to the emerging paradigm of “living pharmacies.” In living pharmacies, drug-producing cells will be hosted on implantable devices, and real-time monitoring of drug production/diffusion rates based on an individual’s pharmokinetics will be crucial.

An innovative approach to joining materials that enhances strength and toughness at interfaces, inspired by natural structures.

      Molecular dynamics (MD) is a computational materials science modality widely used in academic and industrial settings for materials discovery and more. A critical aspect of modern MD calculations are machine learning interatomic potentials (MLIPs), which learn from reference quantum mechanical calculations and predict the energy and forces of atomic configurations quickly. MLIPs allow for more accurate and comprehensive exploration of material/molecular properties at-scale. However, state-of-the-art MLIP methods mostly use a short-range approximation, which may be sufficient for describing properties of homogeneous bulk systems but fail for liquid-vapor interfaces, dielectric response, dilute ionic solutions with Debye-Huckel screening, and interactions between gas phase molecules. Short-range MLIPs neglect all long-range interactions, such as Coulomb and dispersion interactions.      To address the current shortcoming, UC Berkeley researchers have developed a straightforward and efficient algorithm to account for long-range interactions in MLIPs. The algorithm can predict system properties including those with charged, polar or apolar molecular dimers, bulk water, and water-vapor interfaces. In these cases standard short-range MLIPs lead to unphysical predictions, even when utilizing message passing algorithms. The present method eliminates artifacts while only about doubling the computational cost. Furthermore, it can be incorporated into most existing MLIP architectures, including potentials based on local atomic environments such as HDNPP, Gaussian Approximation Potentials (GAP), Moment Tensor Potentials (MTPs), atomic cluster expansion (ACE), and MPNN (e.g., NequIP, MACE).

Brief description not available

Brief description not available

Electrodermal activity (EDA) has traditionally been used for monitoring mental activity by measuring skin conductance (SkinG) at locations with high sweat gland density. However, EDA has not been considered useful for physical activity due to baseline shifts caused by sweat accumulation at the skin/electrode interface.

      Quantum sensing is rapidly reshaping our ability to discern chemical processes with high sensitivity and spatial resolution. Many quantum sensors are based on nitrogen-vacancy (NV) centers in diamond, with nanodiamonds (NDs) providing a promising approach to chemical quantum sensing compared to single crystals for benefits in cost, deployability, and facile integration with the analyte. However, high-precision chemical quantum sensing suffers from large statistical errors from particle heterogeneity, fluorescence fluctuations related to particle orientation, and other unresolved challenges.      To overcome these obstacles, UC Berkeley researchers have developed a novel microfluidic chemical quantum sensing device capable of high-precision, background-free quantum sensing at high-throughput. The microfluidic device solves problems with heterogeneity while simultaneously ensuring close interaction with the analyte. The device further yields exceptional measurement stability, which has been demonstrated over >103s measurement and across ~105 droplets.  Greatly surpassing the stability seen in conventional quantum sensing experiments, these properties are also resistant to experimental variations and temperature shifts. Finally, the required ND sensor volumes are minuscule, costing only about $0.63 for an hour of analysis. 

UC Santa Cruz investigators initially made a breakthrough discovery by which a gallium-rich alloy of gallium and aluminum containing aluminum nanoparticles that could be formed at relatively low temperatures (between 20 and 40 degrees C) could liberate nearly theoretical quantities of hydrogen in effectively any source of water (NCD 32779) through a chemical reaction requiring no outside electrical input and no corrosive byproducts. One of the eventual useful byproducts of this reaction is alumina (aluminum oxide, Al2O3) a commodity chemical with a wide variety of uses in industry. This technology describes ways of further refining aluminum oxide from the products of this reaction. 

Researchers at UC Irvine have developed a new method to 3D-print free-form silica glass materials which produces products with unparalleled purity, optical clarity, and mechanical strength under far milder conditions than currently available techniques. The novel processing method has potential to radically transform microsystem technology by enabling development of silica-based microsystems.

Researchers at the University of California, Davis have developed layered 2D MoS2 nanostructures that have their light-interactive properties improved by intercalation with transition and post-transition metal atoms, specifically Copper and Tin.