This technology enables precise, selective manipulation of magnetically barcoded materials, distinguishing them from background magnetic materials

Dr. René Riedel and Stephen Lepore from the University of California, Riverside have developed an NMR tube/reactor that enables in-situ irradiation to photo-initiate reactions in an inert or controlled atmosphere. It allows for the data acquisition of air, moisture, and temperature-sensitive liquid samples by nuclear magnetic resonance (NMR) spectroscopy without needing to remove the sample from the spectrometer for irradiation. This technology is advantageous because it makes photochemical reactions and kinetic measurements of sensitive samples more reproducible, and it enables the previously impossible maintenance of a controlled environment during photochemical NMR investigations.

 Professors Richard Schrock, Matthew Conley, and colleagues from the University of California, Riverside have developed a new Schrock catalysts for olefin metathesis that can be produced in fewer synthetic steps, activated with perfluorinated alcohols, and reactivated using light or heat. The method provides a more convenient route to a variety of Schrock catalysts that avoid corrosive triflic acid and reactive Grignard reagents to yield Schrock catalysts, which can then be converted readily into other catalyst variations. This technology is advantageous because it is a safer and less expensive way to synthesize and activate Schrock catalysts for industrial and research applications.  

       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.

A novel manufacturing process that shapes complex components from nanostructured materials using a combination of pressure, heat, and electricity.

A novel technique for the safe and efficient production of neodymium oxide microspheres, serving as a non-radioactive surrogate for americium oxide synthesis.

Researchers at the University of California, Davis (“UC Davis”) have developed an optical absorber/emitter for thermophotovoltaics application with a tunable emission wavelength.

      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.

An innovative approach to joining materials that enhances strength and toughness at interfaces, inspired by natural structures.

A groundbreaking technology that mimics the dynamic color-changing functionality of the blue-ringed octopus for applications in camouflage, signaling, and beyond.

A revolutionary low-CO2 cement produced at room temperature, offering a sustainable alternative to traditional Portland cement.

Professors Richard Schrock, Matthew Conley, and colleagues from the University of California, Riverside have developed new Schrock catalysts in the form of tungsten cyclohexylidenes that can be produced in as few as three synthetic steps, using inexpensive and non-corrosive reagents. This technology forms metathesis-relevant alkylidenes from an olefin through a novel thermal mechanism that avoids a protonation/deprotonation mechanism. This technology is advantageous because it can enable a cost-effective access to metathesis active Schrock catalysts for industrial and research applications. 

      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).

Researchers have translated a medical computational procedure into creating interactive 3D printed construction units.

A scalable and less toxic underwater adhesive developed from two small molecule precursors, providing fast and stable adhesion.

      While chromium (Cr) is recognized as an essential micronutrient, its hexavalent form, Cr(VI), is one of the ubiquitous metal contaminants prevalent in groundwater with toxicity and carcinogenic risks. After years of debate and analysis, California regulators adopted a limit of 10 ppb for Cr(VI) in drinking water in April 2024, which should lead to more stringent regulation of Cr(VI) nationwide and attract up to hundreds of millions of dollars in investment. Electroreduction of Cr(VI) to Cr(III) is a promising strategy for detoxication of Cr(VI), but the noble-metal-based and nanomaterial-based electrodes typically used for Cr(VI) reduction are expensive or require a complicated preparation process. Moreover, the majority of flat-sheet electrodes used in flow-by operation mode are constrained by surface area, which causes low mass transport, detoxication efficiency, and current efficiency, and generates high energy consumption.       To meet these challenges, UC Berkeley researchers have developed a stainless-steel filter with the capability of selectively reducing Cr(VI) to Cr(III) in-situ during a single pass filtration process. The filter doesn’t require chemical inputs or generate waste sludge. It has demonstrated minimal electric energy consumption while removing Cr(VI) from real groundwater samples (Coachella Valley Water District, California, calculated at 0.00076 $/m3 –beating other techniques by several orders of magnitude). The water flux of the filter is adjustable to meet specific, realistic water treatment requirements, and it can furthermore be regenerated in-situ for long-term performance without off-site chemical-dependent cleaning procedures. This environmentally friendly filter efficiently removes Cr(VI) from traditional and non-traditional water sources using minimal energy input and with zero discharge, addressing critical issues in water scarcity and Cr(VI) contamination.

The plant species Banksia speciosa relies on wildfires to propagate its seeds. The specialized coating on the seeds, along with the follicle structure, can protect seeds from temperatures over 1,000°C. Inspired by this coating on the seeds of the Banksia plants, researchers at UC Irvine have developed novel, bioinspired coatings, materials, and structures for thermal management, enabling development of cost-effective and ecological thermal management systems.

A novel method for producing dissolvable alginate microfibers critical for advanced tissue engineering and microfluidic network fabrication.

Researchers at the University of California, Davis (“UC Davis”) have developed a unisex swimwear garment designed to prevent swimming-related injuries and to assist in injury recovery during training.

         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. 

A breakthrough technology utilizing chemically treated leaves which retain their insect-entrapping properties, providing an effective and less expensive solution for pest control without the use of chemical insecticides.

Invention of a new platform for creating active supramolecular materials using electrical energy as the fuel.

Researchers at the University of California, Davis have developed a method for preparing a glycan product containing a nonulosonic acid moiety by means of legionaminic acid transferase fusion proteins