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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.

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