Researchers at the University of California, Davis have developed a nanoparticle system combining photothermal therapy and chemotherapy for enhanced cancer treatment.

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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. 

         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.

      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.

Oxygen Evolution Reaction (OER) is crucial for various renewable energy applications, but current electrocatalysts often face issues with stability, efficiency, and cost. This invention addresses these challenges by introducing a novel method for synthesizing robust oxygen evolution electrocatalysts. The technology, developed by UC Berkeley researchers, utilizes calixarene-templated iridium compositions. This approach yields highly stable and efficient electrocatalysts, offering significant advantages over traditional iridium-based catalysts. Specifically, this innovation provides superior performance and durability, making it a valuable tool for energy systems like electrolyzers and fuel cells.

This technology introduces a novel Jones tube design utilizing nanopillars to significantly reduce biofilm formation, enhancing patient comfort and safety.

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

       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.

A novel manufacturing process that shapes complex components from nanostructured materials using a combination of pressure, heat, and electricity.

An innovative Palladium hydride catalyst that significantly enhances the electroreduction of carbon dioxide (CO2) to formate with exceptional tolerance for carbon monoxide (CO).

      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.

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 have developed a solid-state device that uses Cherenkov Radiation to emit light at a tunable wavelength in the THz to IR range.

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 algorithm for designing and identifying complex structures having custom release profiles for controlled drug delivery.

      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).

      Raman spectroscopy, the inelastic scattering of light off molecular vibrations or solid- state phonons, is a critical method in chemical analytics, biological imaging, and materials or even art characterization. A common method for signal enhancement is surface enhanced Raman spectroscopy (SERS), where noble metal or dielectric nanostructures locally enhance the incoming and/or scattered field. SERS has found wide-spread applications in bio- analytics, fundamental science, viral and bacterial classification, and the study of tissue samples. Yet, obstacles towards more wide-spread adoption with wider scope are poor SERS substrate reproducibility and local hotspot fluctuations of metallic SERS substrates, and background emission from molecules, analytes, hot electrons, plasmons, or carriers in dielectrics that can significantly interfere with small signals of target analytes in SERS.       UC Berkeley researchers have developed an improved method for SERS that simultaneously minimizes spurious background emission, minimizes local heating even under high excitation powers, and maximizes the Raman signal enhancement of dielectric SERS substrates. Together these advantages render the method a powerful contender for sought after quantitative SERS and reliable analyte and single- molecule detection without fluctuations or other perturbations from SERS substrates. This enables commercially relevant usage, particularly in the biosciences and diagnostics, DNA/RNA sequencing, protein sequencing, determination of biomolecular binding constants, interconversion kinetics between biomolecular conformers, post-translational modifications, determination of molecular folding statuses, and classification of different proteoforms. It further has commercial potential in environmental monitoring, food safety, semiconductor inspection, polymer quality control and research, quality control in pharmaceuticals – including vesicles for drug delivery-, materials science, and physical science research.

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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.

         While nuclear magnetic resonance (NMR) is one of the most universal synthetic chemistry tools for its ability to measure highly specific kinetic and structural information nondestructively/noninvasively, it is costly and low-throughput primarily due to the small sample-size volumes and expensive equipment needed for stringent magnetic field homogeneity. Conversely, zero-to-ultralow field (ZULF) NMR is an emerging alternative offering similar chemical information but relaxing field homogeneity requirements during detection. ZULF NMR has been further propelled by recent advancements in key componentry, optically pumped magnetometers (OPMs), but suffers in scope due to its low sensitivity and its susceptibility to noise. It has not been possible to detect most organic molecules without resorting to hyperpolarization or 13C enrichment using ZULF NMR.         To overcome these challenges, UC Berkeley researchers have developed a multi-channel ZULF spectrometer that greatly improves on both the sensitivity and throughput abilities of state-of-the art ZULF NMR devices. The novel spectrometer was used in the first reported detection of organic molecules in natural isotopic abundance by ZULF NMR, with sensitivity comparable to current commercial benchtop NMR spectrometers. A proof-of-concept multichannel version of the ZULF spectrometer was capable of measuring three distinct chemical samples simultaneously. The combined sensitivity and throughput distinguish the present ZULF NMR spectrometer as a novel chemical analysis tool at unprecedented scales, potentially enabling emerging fields such as robotic chemistry, as well as meeting the demands of existing fields such as chemical manufacturing, agriculture, and pharmaceutical industries.

      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.