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Unzipping Polymers For Enhanced Energy Release

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(SD2023-036) Matrix-insensitive approach for protease detection

Researchers at UC San Diego have developed a dipeptide composed of two arginine (Arg-Arg) that is capable of inducing the assembly of citrate-capped gold nanoparticles (AuNPs-citrate). Surprisingly, the resulting Arg-Arg-AuNPs are stable over time as the peptide protects the particles from degradation. The assemblies can even be dried without any loss of particles. The assembly of AuNPs-citrate changes their optical properties and the color of the suspension turns from red to blue. Importantly, the assemblies can be dissociated with thiolated polyethylene glycol (HS-PEGs) molecules which leads to the recovery of the initial optical properties of the AuNPs, i.e. the red color of the suspension. Surprisingly, we have observed that such dissociation of AuNPs assemblies is not sensitive to the composition of the medium. It can thus be performed in biological fluids such as pure plasma, saliva, urine, bile, cell lysates or even sea water.

Mitochondria Targeting Photosensitizer for Photodynamic Therapy

Researchers at the University of California, Davis have developed a self-assembling, fibrous photosensitizer that targets mitochondria in tumor cells for destruction via photodynamic therapy with enhanced localization and potency.

Field-Programmable Ising Machines (FPIM)

Certain difficult optimization problems, such as the traveling salesman problem, can be solved using so-called analog Ising machines, in which electronic components (such as certain arrangements of diodes or electronic switches) implement an analog of a well-studied physical system known as an Ising machine. The problem is recast so that its solution can be read off from the lowest-energy configuration of the analog Ising machine, a state which the system will naturally evolve towards. While promising, this methodology suffers major drawbacks. Firstly, the number of subunits, known as “spins”, in the analog Ising machines, as well as the number of connections between these subunits, can grow substantially with problem size. Secondly, existing implementations of this principle rely on chip constructions which are optimized for one or a few problems, and are not sufficiently reprogrammable to be repurposed efficiently for other applications. To address these problems, researchers at UC Berkeley have developed a device known as a Field-programmable Ising machine which can be adapted to implement an analog Ising machine using a variety of hardware designs, such as the diodes and switches mentioned above. These Ising machines can be effectively reprogrammed to efficiently solve a wide array of problems across various domains. The inventors have shown that this design can be applied to SAT (“Satisfiability”) problems, a class known to be similar to the traveling salesman problem, in that the number of spins needed and their level of connectivity do not grow too quickly with problem size.

Method To Inverse Design Mechanical Behaviors Using Artificial Intelligence

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.

Polysaccharide A-Based Particulate Systems For Attenuation Of Autoimmunity, Allergy and Transplant Rejection

Researchers at the University of California, Davis have developed a customizable polysaccharide that can be added to nanoparticles to reduce their rejection by the human immune system.

Sequential Targeting and Crosslinking Nanoparticles for Tackling the Multiple Barriers to Treat Brain Tumors

Researchers at the University of California, Davis have developed an approach to improve drug delivery to tumors and metastases in the brain. Their multi-barrier tackling delivery strategy has worked to efficiently impact brain tumor management while also achieving increased survival times in anti-cancer efficacy.

(SD2022-180) Method of viral nanoparticle functionalization for therapy and imaging applications

Plant viral nanoparticles (plant VNPs) are promising biogenetic nanosystems for the delivery of therapeutic, immunotherapeutic, and diagnostic agents. The production of plant VNPs is simple and highly scalable through molecular farming in plants. Some of the important advances in VNP nanotechnology include genetic modification, disassembly/reassembly, and bioconjugation. Although effective, these methods often involve complex and time-consuming multi-step protocols.

Magnetochromatic Spheres

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Methods For Growing Nanofibers/Nanotubes On High Aspect Ratio Carbon Microstructures

See patent information below. C-MEMS architecture having carbon structures with high surface areas due to high aspect ratios and nanoscale surface enhancements, and improved systems and methods for producing such structures are provided. Specifically, high aspect ratio carbon structures are microfabricated by pyrolyzing a patterned carbon precursor polymer. Pyrolysing the polymer preferably comprises a multi-step process in an atmosphere of inert and forming gas at high temperatures that trail the glass transition temperature (Tg) for the polymer. The surface area of the carbon microstructures is increases by nanotexturing the surface through oxygen plasma exposure, and by integrating nanoscale structures with the carbon microstructures by exposing the carbon microstructures and a catalyst to hydrocarbon gas. In a preferred embodiment, the carbon microstructures are the source of carbon gas.

Carbon Nanotube Infrared Detector

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Chromium Complexes Of Graphene

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Silicon Nanofiber Paper Battery

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Porous Silicon Nanosphere Battery

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These technologies are part of the UC QuickStart program.