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.

      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).

      Current clinical practice for detecting low-concentration molecular biomarkers requires sending samples to centralized labs, leading to high costs and delays. Successful point-of-care (POC) diagnostic technology exist, such as the paper-based lateral-flow assay (LFA) used for pregnancy tests and SARS-CoV-2 rapid antigen tests, or miniaturized instruments such as the Abbot i-Stat Alinity. However, the former provides binary results or limited quantitative accuracy, and the latter is too expensive for in-home deployment. A promising approach for POC diagnostics, offering tailored circuit optimization, multiplexed detection, and significant cost and size reductions, is millimeter-sized CMOS integrated circuits coupled with microfluidics. Recent demonstrations include protein, DNA/RNA, and cell detection. The current complexity of system packaging (e.g., wire/flip-chip bonding) makes integrating microfluidics with more sophisticated functions challenging, and often-required syringe pumps and tubing are operationally unfriendly, limiting current approaches.       UC Berkeley researchers have developed a fully integrated, multi-mode POC device that requires single-step assembly and operates autonomously. Drawing inspiration from RFID technology and implantables, they have introduced inductively-coupled wireless powering and communication functionality into a CMOS bio-analyzer. With the chip being fully wireless, the die can be easily integrated into a substrate carrier, achieving a completely flat surface that allows for seamless bonding with the microfluidic module. In the final product, the device will be sealed in a pouch inside a vacuum desiccator. The user tears the pouch, adds a drop of sample, and the system automatically begins operation. The operation window can last up to 40 minutes, making the process insensitive to time delays. The present CMOS bio-analyzer integrates pH-sensing and amperometric readout circuits for both proton-based and redox-based immunoassays.

      Integrating microelectronics with microfluidics, especially those implemented in silicon-based CMOS technology, has driven the next generation of in vitro diagnostics. CMOS/microfluidics platforms offer (1) close interfaces between electronics and biological samples, and (2) tight integration of readout circuits with multi-channel microfluidics, both of which are crucial factors in achieving enhanced sensitivity and detection throughput. Conventionally bulky benchtop instruments are now being transformed into millimeter-sized form factors at low cost, making the deployment for Point-of-Care (PoC) applications feasible. However, conventional CMOS/microfluidics integration suffers from significant misalignment between the microfluidics and the sensing transducers on the chip, especially when the transducer sizes are reduced or the microfluidic channel width shrinks, due to limitations of current fabrication methods.       UC Berkeley researchers have developed a novel methodology for fabricating microfluidics platforms closely embedded within a silicon chip implemented in CMOS technology. The process utilizes a one-step approach to create fluidic channels directly within the CMOS technology and avoids the previously cited misalignment. Three types of structures are presented in a TSMC 180-nm CMOS chip: (1) passive microfluidics in the form of a micro-mixer and a 1:64 splitter, (2) fluidic channels with embedded ion-sensitive field-effect transistors (ISFETs) and Hall sensors, and (3) integrated on-chip impedance-sensing readout circuits including voltage drivers and a fully differential transimpedance amplifier (TIA). Sensors and transistors are functional pre- and post-etching with minimal changes in performance. Tight integration of fluidics and electronics is achieved, paving the way for future small-size, high-throughput lab-on-chip (LOC) devices.

      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).

      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.

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.

         Recent advancements in nuclear magnetic resonance (NMR) spectroscopy have underscored the need for novel instrumentation, but current commercial instrumentation performs well primarily for pre-existing, mainstream applications. Modalities involving, in particular, integrated electron-nuclear spin control, dynamic nuclear polarization (DNP), and non-traditional NMR pulse sequences would benefit greatly from more flexible and capable hardware and software. Advances in these areas would allow many innovative NMR methodologies to reach the market in the coming years.          To address this opportunity, UC Berkeley researchers have developed a novel high-speed, high-memory NMR spectrometer and hyperpolarizer. The device is compact, rack-mountable and cost-effective compared to existing spectrometers. Furthermore, the spectrometer features robust, high-speed NMR transmit and receive functions, synthesizing and receiving signals at the Larmor frequency and up to 2.7GHz. The spectrometer features on-board, phase-sensitive detection and windowed acquisition that can be carried out over extended periods and across millions of pulses. These and additional features are tailored for integrated electron-nuclear spin control and DNP. The invented spectrometer/hyperpolarizer opens up new avenues for NMR pulse control and DNP, including closed-loop feedback control, electron decoupling, 3D spin tracking, and potential applications in quantum sensing.

      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. 

Class 2 CRISPR-Cas are streamlined versions in which a single Cas protein bound to RNA is responsible for binding to and cleavage of a targeted sequence. Theprogrammable nature of these minimal systems has facilitated their use as a versatile technology for genome editing.  CRISPR-Cas enzymes with reduced requirements for a protospacer-adjacent motif (PAM) sequence adjacent to the target site could improve the breadth of target sites available for genome editing.  UC Berkeley researchers have developed a novel PAM-loose 12a variants, nucleic acids encoding the variant Cas12a proteins and systems using these variants that make the Cas12a-based CRISPR technology much easier to design a DNA target for carrying out genome editing in human cells. 

TnpB protein has generated interest as a potential compact genome-editing tool, due to the short amino acid sequence (408 AAs for ISDra2 TnpB), which overlaps with the wRNA sequence in their genomes of origin. There is a need for compositions and methods that provide more efficient TnpB systems. UC Berkeley researchers have created variant TnpB proteins and variant wRNAs that increase cleavage activity and/or DNA binding activity (e.g., revealed as endonuclease activity such as on-target endonuclease activity). These variant TnpB proteins include an amino acid sequence having one or more amino acid substitutions relative to a corresponding wild type TnpB protein. Also provided are variant TnpB wRNAs that can form a complex with a TnpB protein and a second nucleotide sequence that can hybridize to a target sequence of a target nucleic acid, thereby guiding the complex to the target sequence.

Various plasmids from Michael Rape's lab, including but not limited to:pQE-UbcH5c/pET-Ube2D3-6xHispET28-E2NpET28-UEV1ApET28a-UBE2S-6xHISpET28a-E2R1-6xHIS 

Various ubiquitin plasmids from Michael Rape lab, including but not limited to: pCS2-no his-ubiquitin wtpCS2-no his-ubiquitin all RpET30a-ubiquitin (no tag)pET30a-ubiquitin K0 (no tag) 

Various plasmid constructs and cell lines for E3 Ligase project from Julia Schaletzky lab, including but not limited to:  pET28-ubiquitin wtpET28-ubiquitin deltaGGpLentiX1hygropLentiX1 blastpLentiX1 puropLentiX1 neopCS2-6xHIS-Htt-73QpCS2-6xHIS-Htt-23QpInducer Htt-23Q-GFPpInducer Htt-73Q-GFP

To gain a more comprehensive understanding of the contribution of specific cell populations to various physiological phenomena in an organism, it is crucial to control cells’ activity using their native proteins, such as ion channels and GPCRs, while maintaining precise cellular and temporal resolution. UC Berkeley researchers have pioneered a magnetogenetic technique named FeRIC (Ferritiniron Redistribution to Ion Channels), which combines the use of radio frequency (RF) magnetic fields and ion channels coupled with ferritin to control cell activity. The researchers demonstrated that the interaction between RF and ferritin produces ROS and oxidized lipids which ultimately activate the ion channels. 

Volumetric additive manufacturing and vat-polymerization 3D printing methods rapidly solidify freeform objects via photopolymerization, but problematically raises the local temperature in addition to degree-of-conversion (DOC). The generated heat can critically affect the printing process as it can auto-accelerate the polymerization reaction, trigger convection flows, and cause optical aberrations. Therefore, temperature measurement alongside conversion state monitoring is crucial for devising mitigation strategies and implementing process control. Traditional infrared imaging suffers from multiple drawbacks such as limited transmission of measurement signal, material-dependent absorptions, and high background signals emitted by other objects. Consequently, a viable temperature and DOC monitoring method for volumetric 3D printing doesn’t exist.To address this opportunity, UC Berkeley researchers have developed a tomographic imaging technique that detects the spatiotemporal evolution of temperature and DOC during volumetric printing. The invention lays foundations for the development of volumetric measurement systems that uniquely resolve both temperature and DOC in volumetric printing.This novel Berkeley measurement system is envisaged as an integral tool for existing manufacturing technologies, such as computed axial lithography (CAL, Tech ID #28754), and as a new research tool for commercial biomanufacturing, general fluid dynamics, and more.

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.

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.

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.

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.

DP-L4056 is a prophage-cured strain of Listeria monocytogenes based on wild-type strain 10403S. A prophage is a bacteriophage genome that is integrated into a bacterial genome. It remains latent until activation by an external factor, and activation leads to production of new bacteriophage particles that lyse the bacterial cell and spread. Curing the prophages in Listeria monocytogenes strain 10403S, which is ubiquitous in the microbiology community as a wild-type reference strain, allows for more predictable engineering and performance of Listeria monocytogenes.

Cationic, ionizable lipids are a type of lipid that can switch between neutral and positive charges depending on the pH of their environment. They're crucial in lipid nanoparticle (LNP) formulations, particularly for RNA delivery, where they play a key role in encapsulating and releasing the RNA payload. Unfortunately, conventional chemical de novo synthesis of cationic and ionizable lipids is slow, expensive and inefficient. UC Berkeley researchers have developed compositions and methods of synthesizing cationic, ionizable lipids via standard solid phase peptide synthesis protocols, and integration of (i) the solid phase lipid synthesis, (ii) initial cell screening, and (iii) animal organ or cell targeting in an automated robotic system (ARS).