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 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.

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).

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      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.

The present invention describes a novel method for acid-free pyrolytic synthesis of metal-nitrogen-carbon (M-N-C) catalysts for use in fuel cell/energy conversion applications. This method allows for rapid production of M-N-C catalysts that exhibit high activity and selectivity for CO2 electroreduction without needing harsh acids or bases.

Researchers from UC San Diego have invented a new form of materials, polymer-integrated crystals (PIX), which combine the structural order of protein crystals with the dynamic, stimuli-responsive properties of synthetic polymers. The inventors have shown that the crystallinity, flexibility, and chemical tunability of PIX can be exploited to encapsulate guest proteins with high loading efficiencies. And, the electrostatic host-guest interactions enable reversible, pH-controlled uptake/release of guest proteins as well as the mutual stabilization of the host and the guest, thus creating a uniquely synergistic platform toward the development of functional biomaterials and the controlled delivery of biological macromolecules.

Metamaterials are constructed from regular patterns of simpler constituents known as unit cells. These engineered metamaterials can exhibit exotic mechanical properties not found in naturally occurring materials, and accordingly they have the potential for use in a variety of applications from running shoe soles to automobile crumple zones to airplane wings. Practical design using metamaterials requires the specification of the desired mechanical properties based on understanding the precise unit cell structure and repeating pattern. Traditional design approaches, however, are often unable to take advantage of the full range of possible stress-strain relationships, as they are hampered by significant nonlinear behavior, process-dependent manufacturing errors, and the interplay between multiple competing design objectives. To solve these problems, researchers at UC Berkeley have developed a machine learning algorithm in which designers input a desired stress-strain curve that encodes the mechanical properties of a material. Within seconds, the algorithm outputs the digital design of a metamaterial that, once printed, fully encapsulates the desired properties from the inputted stress-strain curve. This algorithm produces results with a fidelity to the desired curve in excess of 90%, and can reproduce a variety of complex phenomena completely inaccessible to existing methods.

         Positron emission tomography (PET) is a powerful tool both in biomedical research and clinical patient care, particularly in the diagnosis of cancer, search for metastases, cancer treatment monitoring, diagnosis of diffuse diseases causing dementia, or metabolic blood flow imaging. However, the poor efficiency of current PET detectors (1-2%) requires large radiotracer doses and integration times, driving both cost and patient exposure per scan. High detector capital cost also renders PET scanners prohibitively expensive. Finally, while time-of-flight PET can enhance the spatial resolution of PET by measuring temporal correlation of detected gamma photons, the modality is limited by the latency of current gamma radiation detectors (timing resolutions of ~200-500 ps). Overall, the expense and inefficiency of available gamma radiation detectors hinder the full technological capabilities of PET and its affordable use in patient care.         To address these problems, researchers at UC Berkeley have developed a new gamma radiation detector architecture with the potential for an order of magnitude improvement in both time resolution (down to 10 ps) and efficiency. The design uses novel perovskite nanomaterials and well-established nanotechnology manufacturing methods to produce a detector at a fraction of the cost of current offerings. Together, the high efficiency and timing resolution of the nanophotonic detector design should drastically improve the spatial resolution (including by time-of-flight measurements) of PET scanners and dose-suitability for elderly patients. Benefits in affordability are multiple, lowering detector cost and as well as required radiotracer dose.

Widespread metal and oxyanion contaminants in groundwater due to industrial activities, land use, and natural geology have resulted in a scarcity in potable water in California and worldwide. These contaminants can be carcinogenic and highly toxic at low concentrations, presenting an urgent need for innovative water purification technologies. However, existing technologies for treating groundwater and brackish water are often energy intensive, non-selective, or not suitable for recovery. Therefore, advances in oxyanion removal technologies could significantly improve the potential of safely using groundwater as an alternative drinking water resource. To address this opportunity, researchers at UC Berkeley have developed a novel multifunctional water filter that exploits the high removal efficiency of toxic metal ions and oxyanions by using two-dimensional (2D) molybdenum disulfide (MoS2) nanosheets. MoS2 exhibits multiple removal pathways towards oxyanions such as Cr (VI) and Se (VI), including adsorption, reduction, and physical filtration. The multifunctionality of the MoS2 filters allows in-situ detoxification of the oxyanions, which could greatly reduce the pressure on waste/waste stream treatment. Moreover, MoS2 filters can be integrated into existing water treatment processes (e.g., low-pressure micro/ultrafiltration and adsorption). This integration allows for the treatment of a wide selection of non-traditional water resources, including groundwater and industrial wastewater, and also reduces the costs of the additional steps required for the removal of toxic metals in traditional water treatment processes. The innovation is more efficient, and more selective in targeting oxyanion species, in comparison to currently available technologies, such as reverse osmosis, nanofiltration, adsorption, ion exchange, and coagulation-precipitation. This novel multifunctional filter could potentially reduce operational costs, simplify maintenance, and minimize the impacts of environmental factors compared to other oxyanion treatment technologies.

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