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.

      Pebble bed reactors (PBRs) are an emerging advanced nuclear reactor design where fuel pebbles constantly circulate through the core, as opposed to housing static fuel assemblies, generating numerous advantages including the ability for online refueling versus expensive shutdowns. Online refueling is overall beneficial but poses an operation challenge in that the pebbles must be measured and analyzed for burnup characteristics very quickly (in under 40 seconds), without much time to cool down, challenging the high Purity Germanium (HPGe) detectors historically used for burnup measurements. HPGe detectors can normally only be operated up to tens of thousands of counts per second, far below radiation rates from freshly discharged fuel, and are therefore operated at large distances from sources, with significant shielding. Only a small fraction of detected counts comes from burnup markers, yielding high uncertainty, or can be completely masked by effects of Compton scattering within the detectors.      To overcome the challenges of using HGPe detectors to measure burnup in continuously fueled reactors, UC Berkeley researchers have developed a novel technology capable of measuring gamma rays within a fine energy ranges and without the interference of Compton scattering. The device is also significantly cheaper than HPGe detectors and offers a reduced detector footprint. Nuclides including but not limited to Np-239, Eu-156, and Zr-95 can be measured and analyzed for burnup, path information through the core, and fast and thermal fluence. Furthermore, precise measurement of the Np-239 content provides better data for reactor safeguard purposes. The technology offers meaningful improvements in measurement accuracy, footprint, and cost, for PBRs and other continuously fueled reactors, such as molten salt reactors (MSRs).

      Goniophotometers measure the luminance distribution of light emitted or reflected from a point in space or a material sample. Increasingly there is a need for such measurements in real-time, and in real-world situations, for example, for daylight monitoring or harvesting in commercial and residential buildings, design and optimization of greenhouses, and testing laser and display components for AR/VR and autonomous vehicles, to name a few. However, current goniophotometers are ill-suited for real-time measurements; mechanical scanning goniophotometers have a large form factor and slow acquisition times. Parallel goniophotometers take faster measurements but suffer from complexity, expense, and limited angular view ranges (dioptric angular mapping systems) or strict form factor and sample positioning requirements (catadioptric angular mapping systems). Overall, current goniophotometers are therefore limited to in-lab environments.      To overcome these challenges, UC Berkeley researchers have invented an optical sensor  for parallel goniophotometry that is compact, cost-effective, and capable of real-time daylight monitoring. The novel optical design addresses key size and flexibility constraints of current state-of-the-art catadioptric angular mapping systems, while maximizing the view angle measurement at 90°. This camera-like, angular mapping device could be deployed at many points within a building to measure reflected light from fenestrations, in agricultural greenhouses or solar farms for real-time monitoring, and in any industry benefitting from real-time daylight data.

      Probabilistic Seismic Hazard Analysis (PSHA) has become a foundational method for determining seismic design levels and conducting regional seismic risk analyses for insurance risk analysis, governmental hazard mapping, critical infrastructure planning, and more. PSHA traditionally relies on two computationally intensive approaches: Riemann Sum and conventional Monte Carlo (MC) integration. The former requires fine slices across magnitude, distance, and ground motion, and the latter demands extensive synthetic earthquake catalogs. Both approaches become notably resource intensive for low-probability seismic hazards, where achieving a COV of 1% for a 10−4 annual hazard probability may require 108 MC samples.       UC Berkeley researchers have developed an Adaptive Importance Sampling (AIS) PSHA, a novel framework to approximate optimal importance sampling (IS) distributions and dramatically reduce the number of MC samples to estimate hazards. Efficiency and accuracy of the proposed framework have been validated against Pacific Earthquake Engineering Research Center (PEER) PSHA benchmarks covering various seismic sources, including areal, vertical, and dipping faults, as well as combined types. Seismic hazards are calculated up to 3.7×104 and 7.1×103 times faster than Riemann Sum and traditional MC methods, respectively. Coefficients of variation (COVs) are below 1%. Enhanced “smart” AIS PSHA variants are also available that outperform “smart” implementations of Riemann Sum by a factor of up to 130.

      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. 

         Recent mass evacuation events, including the 2018 Camp Fire and 2023 Maui Fire, have demonstrated shortcomings in our communication abilities during natural disasters and emergencies. Individuals fleeing dangerous areas were unable to obtain fast or accurate information pertaining to open evacuation routes and faced traffic gridlocks, while nearby communities were unprepared for the emergent situation and influx of persons. Climate change is increasing the frequency, areas subject to, and risk-level associated with natural hazards, making effective communication channels that can operate when mobile network-based systems and electric distribution systems are compromised crucial.         To address this need UC Berkeley researchers have developed a mobile network-free communication system that can function during natural disasters and be adapted to most communication devices (mobile phones and laptops). The self-organized, mesh-based and low-power network is embedded into common infrastructure monitoring device nodes (e.g., pre-existing WSN, LoRa, and other LPWAN devices) for effective local communication. Local communication contains dedicated Emergency Messaging and “walkie-talkie” functions, while higher level connectivity through robust gateway architecture and data transmission units allows for real-time internet access, communication with nearby communities, and even global connectivity. The system can provide GPS-free position information using trilateration, which can help identify the location of nodes monitoring important environmental conditions or allowing users to navigate.

Cellulose, the major component of plant biomass, is considered the most abundant biopolymer. Certain microorganisms are able to convert the monomer of cellulose, glucose, into various products useful in the production of biofuels and other methods. Cellulose is highly stable, has a high storage potential, low cost, and plentiful supply. Based on these and other properties, cellulose and enzymes capable of degrading and hydrolyzing it are useful in the sequestration, storage, and production of bioenergy.  Crystalline cellulose is composed of linear polymers of β1-4 linked glucose, held in a tightly crosslinked crystalline lattice by a high degree of intermolecular hydrogen bonding. This structure confers stability but also hinders efficient deconstruction of cellulose. Strategies for commercial depolymerization of cellulose typically combine pretreatment to disrupt the crystalline structure, followed by enzymatic hydrolysis. Disruption of the crystalline structure and chemical hydrolysis typically requires high temperatures and low pH. Enzymatic hydrolysis generally occurs under milder conditions. The degree of pretreatment required and the expense of subsequent cleanup steps are affected by properties of the enzymes used. Bacteria capable of degrading cellulose include those belonging to the genera Aquifex, Rhodothermus, Thermobifida, Anaerocellum, and Caldicellulosiruptor. A recombinant thermostable endoglucanase of Aquifex aeolicus produced in E. coli showed maximal activity at 80° C. and pH 7.0 with a half-life of 2 h at 100° C.  UC Berkeley investigators have engineered a polypeptide having cellulase activity for hydrolysis and degradation of cellulose-containing biomass.

This invention pertains to novel compositions comprising hydroxamate-based metal-organic frameworks (MOFs). These frameworks are synthesized using hydroxamate ligands that coordinate with metal ions to form porous, crystalline structures. The unique properties of these MOFs make them highly versatile and applicable in various industrial and environmental processes. By enabling efficient pollutant removal and water harvesting, hydroxamate-based MOFs contribute to sustainable environmental practices. This disclosure highlights the potential of hydroxamate-based metal-organic frameworks to revolutionize various industrial and environmental applications through their unique properties and versatile uses.

Plastics are essential for the modern world but are also non-sustainable products of the petrochemical industry that negatively impact our health, environment, and food chain. Natural biogenic plastics, such as polyhydroxyalkanoates (PHA), are readily biodegradable, can be produced more sustainably, and offer an attractive alternative. The global demand for bioplastics is increasing with the 2019 market value of $8.3B expected to reach a compound annual growth rate of 16.1% from 2020-2027 (https://www.grandviewresearch.com/industry-analysis/bioplastics-industry). However, current PHA production is constrained by the underlying physiology of the microorganisms which produce them, meaning bioplastic production is currently limited to inefficient, batch fermentation processes that are difficult to scale.To address this problem, UC Berkeley researchers have developed a new system for PHA production wherein the PHA are generated continuously throughout microorganism growth lifecycles. The invention allows these sustainable bioplastics to be produced via precision continuous fermentation technology, a scalable and efficient approach.

For decades, researchers have been developing “cool roof” materials to cool buildings and save on energy usage from air conditioning. Cool roof materials are engineered to maximize infrared thermal emission, allowing heat to be effectively radiated into outer space and the building to cool down. Conventional cool roof materials emit heat even when it is cold outside, which exacerbates space heating costs and can outweigh energy-saving benefits. A temperature adaptive radiative coating (TARC) material was developed in 2021 that adapts its thermal emittance to ambient temperatures using metal-insulator transitions in vanadium oxide. TARC is projected to outperform existing roof materials in most climate areas, but the complicated structure required high-cost fabrication techniques such as photolithography, pulsed laser deposition, and XeF2 etching, which are not scalable.To address this problem, UC Berkeley researchers have developed a new scalable temperature-adaptive radiative coating (STARC). STARC has the same thermal emittance switching capability as TARC, allowing the thermal emittance to be switched between high- and low- emittance states at a preset temperature. However, STARC can be produced using high-throughput, roll-to-roll methods and low-cost materials. The STARC material also has an improved lifetime. As an added benefit, while cool roof materials are often engineered with uniformly low solar-absorption, the color and solar absorption of STARC can be tuned for aesthetic purposes or to meet local climate-specific needs.

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.

 This invention is a method includes mixing a polymer with one or more dehydrogenating reagent(s), thereby forming the dehydrogenated polymer.  Such a dehydrogenated polymer can then be made into a alkene or a dehydrogenating polymer.

In this technology, the inventors introduce additives to purposely change the morphology of polycaprolactone (PCL) by increasing the bending and twisting of crystalline lamellae. These morphological changes immobilize chain-ends preferentially at the crystalline/amorphous interfaces and limit chain-end accessibility by the embedded processive enzyme. This chain end redistribution reduces the polymer-to-monomer conversion from >95% to less than 50%, causing formation of highly crystalline plastic pieces including microplastics. By synergizing both random chain scission and processive depolymerization, it is feasible to navigate morphological changes in polymer/additive blends and to achieve near complete depolymerization. The random scission enzymes in the amorphous domains create new chain ends that are subsequently bound and depolymerized by processive enzymes. Present studies further highlight the importance to consider host polymer morphological effects on the reactions catalyzed by embedded catalytic species.This is part of a patent family in compostable plastics.  

The inventors have developed ion-selective potentiometric sensors for monitoring soil analytes with naturally degradable substrate, conductor, electrode, and encapsulant materials that minimize pollution and ecotoxicity. This novel sensor-creation method uses printing technologies for the measurement of nitrate, ammonium, sodium, calcium, potassium, phosphate, nitrite, and others. Monitoring soil analytes is key to precision agriculture and optimizing the health and growth of plant life. 

In developed countries, buildings demand a large percentage of a region's energy-generating requirements. This has led to an urgent need for efficient buildings with reduced energy requirements. In office buildings, lighting takes up 20% to 45% of the total energy consumption. Furthermore, the adoption of smart lighting control strategies such as daylight harvesting is shown to reduce lighting energy use by 30% to 50%.For most closed-loop lighting control systems, the real-time data of the daylight level at areas of interest (e.g., the office workbench) are the most important inputs. Current state-of-the-art solutions use dense arrays of luxmeters (photosensors) to monitor the daylight environment inside buildings. The luxmeters are placed on either workbenches, or ceilings and walls near working areas. Digital cameras are used in controlled laboratory environments and occasionally in common buildings to evaluate glare resulting from excessive daylight. The disadvantage of these sensor-based approaches is that they're expensive to install and commission. Additionally, the sample area of these sensors is limited to either the area of the luxmeters or the view of the cameras. Consequently, many sensors are needed to measure the daylight in a large office space.To address this situation, researchers at UC Berkeley developed a portable cyber-physical system for real time, daylight evaluation in buildings, agriculture facilities, and solar farms (collectively referred to as "structures").

 This invention is a wave powered desalination system and, in particular , a wave powered desalination systems with a low speed, high pressure rotary pump.

The inventor has developed systems and methods for performing continuous 3D roll-based additive manufacturing. This invention is distinct from roll-based micro/nanomanufacturing methods such as imprint lithography, gravure printing, and photo-roll lithography because it enables production of high aspect ratio reentrant features and voids in a single step that are difficult or even impossible with the existing methods.

The inventor has developed novel algorithms and metrology methodologies, including real-time in-situ imaging of part formation, in computed axial lithography printing (CALP). CALP is a form of continuous 3D roll-based additive manufacturing which is distinct from roll-based micro/nanomanufacturing methods such as imprint lithography, gravure printing, and photo-roll lithography because it enables production of high aspect ratio reentrant features and voids in a single step that are difficult or even impossible with the existing methods.

The power demands on data centers are large and increasing rapidly. This is straining data center economic and environment impacts, and in turn driving improvements in data center power efficiencies. Data centers have been widely adopting 48 V intermediate bus architectures due to higher efficiency, good flexibility, and reduced cost. However, a major challenge in such systems is the conversion from the 48 V bus to the extreme low voltage and high current operating levels of server CPUs and GPUs.To address this challenge, UC Berkeley researchers developed a multi-phase hybrid power converter architecture. The Berkeley design uses hybrid converter topologies. A switched-capacitor network is smartly merged with a switched-inductor network, resulting in circuit component number reduction and soft-charging operation of the capacitors. Furthermore, the Berkeley architecture integrates a multi-phase control technique to achieve a higher conversion ratio of the switched-capacitor network, which can further improve the overall system efficiency without increasing the circuit size.  

Imaging the material property distribution of solids has a broad range of applications in materials science, biomechanical engineering, and clinical diagnosis. For example, as various diseases progress, the elasticity of human cells, tissues, and organs can change significantly. If these changes in elasticity can be measured accurately over time, early detection and diagnosis of different disease states can be achieved. Elasticity imaging is an emerging method to qualitatively image the elasticity distribution of an inhomogeneous body. A long-standing goal of this imaging is to provide alternative methods of clinical palpation (e.g. manual breast examination) for reliable tumor diagnosis. The displacement distribution of a body under externally applied forces (or displacements) can be acquired by a variety of imaging techniques such as ultrasound, magnetic resonance, and digital image correlation. A strain distribution, determined by the gradient of a displacement distribution, can be computed (or approximated) from measured displacements. If the strain and stress distributions of a body are both known, the elasticity distribution can be computed using the constitutive elasticity equations. However, there is currently no technique that can measure the stress distribution of a body in vivo. Therefore, in elastography, the stress distribution of a body is commonly assumed to be uniform and a measured strain distribution can be interpreted as a relative elasticity distribution. This approach has the advantage of being easy to implement. The uniform stress assumption in this approach, however, is inaccurate for an inhomogeneous body. The stress field of a body can be distorted significantly near a hole, inclusion, or wherever the elasticity varies. Though strain-based elastography has been deployed on many commercial ultrasound diagnostic-imaging devices, the elasticity distribution predicted based on this method is prone to inaccuracies.To address these inaccuracies, researchers at UC Berkeley have developed a de novo imaging method to learn the elasticity of solids from measured strains. Our approach involves using deep neural networks supervised by the theory of elasticity and does not require labeled data for the training process. Results show that the Berkeley method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy and missing measurements.

Producing meat products using cells grown in culture (instead of via animal husbandry farming) has many benefits and great potential. Current cell-cultured approaches either: (1) use suspension culture to produce homogenous products that don't meet consumer taste expectations for a substitute meat, or (2) organ culture methods to create products that meet consumer taste expectations, but at unacceptably high prices. To address this situation, researchers at UC Berkeley have been developing a process by which cells are grown in free suspension, making possible the economies of scaling that result from using large stirred tanks. After growth, the cells can be assembled into desirable macroscopic structures by controlling the conditions under which the desired multiple cell types and scaffolds are mixed and dewatered. The macroscopic structures include features such as fat marbling and muscle fiber orientation as expected by meat consumers.

UC Berkeley researchers have discovered a novel family of proteins denoted Cas12L within the Type V CRISPR Cas superfamily distantly related to CasX, CasY and other published type V sequences.  These Cas12L proteins utilize a guide RNA to perform RNA-directed cleavage of DNA.

The inventors have constructed conjugative plasmids for intra- and inter-species delivery and expression of RNA-guided CRISPR-Cas transposases for organism- and site-specific genome editing by targeted transposon insertion. This invention enables integration of large, customizable DNA segments (encoded within a transposon) into prokaryotic genomes at specific locations and with low rates of off-target integration.

Mutated versions of Cas12a that remove its non-specific ssDNA cleavage activity without affecting site-specific double-stranded DNA cutting activity. These mutant proteins, in which a short amino acid sequence is deleted or changed, provide improved genome editing tools that will avoid potential off-target editing due to random ssDNA nicking.

The real-time position and mobility of a user is key to providing personalized location-based services (LBSs) – such as navigation. With the pervasiveness of GPS-enabled mobile devices (MDs), LBSs in outdoor environments is common and effective. However, providing equivalent quality of LBSs using GPS in indoor environments can be problematic. The ubiquity of both WiFi in indoor environments and WiFi-enabled MDs, makes WiFi a promising alternative to GPS for indoor LBSs. The most promising approach to establishing a WiFi-based indoor positioning system requires the construction of a high quality radio map for an indoor environment. However, the conventional approach for making the radio map is labor intensive, time-consuming, and vulnerable to temporal and environmental dynamics. To address this situation, researchers at UC Berkeley developed an approach for automatic, fine-grained radio map construction and adaptation. The Berkeley technology works both (a) in free space – where people and robots can move freely (e.g. corridors and open office space); and (b) in constrained space – which is blocked or not readily accessible. In addition to its use with WiFi signals, this technology could also be used with other RF signals – for example, in densely populated and built-up urban areas where it can be suboptimal to only rely on GPS.