UC Berkeley researchers have developed several methods that take advantage of all of the information contained in ion signals in charge detection mass spectrometry (CDMS). Unlike most conventional types of mass spectrometry (MS), which rely on mass-to-charge ratio (m/z) measurements of ensembles of ions, CDMS instead makes direct measurements of the mass of individual ions. CDMS has recently gained significant popularity in the analysis of large biomolecules, nanoparticles, and nanodroplets because it is one of very few methods that can characterize these analytes. State-of-the-art CDMS instruments incorporate ion traps and signals from individual trapped ions are used to find the mass, charge, and energy of these ions. Previously used techniques have used Fourier transform (FT)-based analyses, but only use the fundamental and/or second harmonic frequency and amplitude as the basis of the measurement. The significant additional information contained in the higher order harmonic frequencies and amplitudes of the ion signal is fully utilized in the novel methods comprising this invention and large improvements in measurement uncertainties are realized as a result. 

 The dynamic patterning of 3D optical point clouds has emerged as a key enabling technology in volumetric processing across a number of applications. In the context of biological microscopy, 3D point cloud patterning is employed for non-invasive all-optical interfacing with cell ensembles. In augmented and virtual reality (AR/VR), near-eye display systems can incorporate virtual 3D point cloud-based objects into real-world scenes, and in the realm of material processing, point cloud patterning can be mobilized for 3D nanofabrication via multiphoton or ultraviolet lithography. Volumetric point cloud patterning with spatial light modulators (SLMs) is therefore widely employed across these and other fields. However, existing hologram computation methods, such as iterative, look-up table-based and deep learning approaches, remain exceedingly slow and/or burdensome. Many require hardware-intensive resources and sacrifices to volume quality.To address this problem, UC Berkeley researchers have developed a new, non-iterative point cloud holography algorithm that employs fast deterministic calculations. Compared against existing iterative approaches, the algorithm’s relative speed advantage increases with SLM format, reaching >100,000´ for formats as low as 512x512, and optimally mobilizes time multiplexing to increase targeting throughput. 

Hyperspectral imaging, the practice of capturing detailed spectral (color) information from the output of an optical instrument such as a microscope or telescope, is useful in biological and astronomical research and in manufacturing. In addition to being bulky and expensive, existing hyperspectral imagers typically require scanning across a specimen, limiting temporal resolution and preventing dynamic objects from being effectively imaged. Snapshot methods which eliminate scanning are limited by a tradeoff between spatial and spectral resolution.In order to address these problems, researchers at UC Berkeley have developed a hyperspectral imager which can be attached to the output of any benchtop microscope. The imager is compact (about 6-inches), and can achieve a higher spatial resolution than traditional snapshot imagers. Additionally, this imager needs only one exposure to collect measurements for an arbitrary number of spectral filters, giving it unprecedented spectral resolution.

Dynamic patterning of light is used in a variety of applications in imaging and projection. This is often done by spatial light modulation, in which a coherent beam of input light is modified at the pixel level to create arbitrary output patterns via later interference. Traditional approaches to spatial light modulation suffer from a high operating burden, especially as the number of pixels increases, and incomplete coverage of the optical surface. This results in high device complexity, and cost, as well as enormous real-time computation requirements, reduced optical performance, and optical artifacts.To address these problems, researchers at UC Berkeley have developed a method for wiring groups of pixels, such as annular rings, parallel strips, or radial strips. This takes advantage of the fact that most spatial light modulation tasks can be accomplished by combining a number of simple “primitive phase profiles”, in which not all pixels need be independent of each other. In this co-wiring method, individual optical elements remain at the pixel level, but are wired together in a way that they move in precisely the coordinated manner to produce one of these primitive phase profiles. This allows for high frame rates, high coverage of the optical plane, and a degree of sensitivity impossible to produce with large, geometric optical elements that exist in prior art.

Dynamic patterning of light is used in a variety of applications in imaging and projection. This is often done by spatial light modulation, in which a coherent beam of input light is modified at the pixel level to create arbitrary output patterns via later interference. Traditional approaches to spatial light modulation suffer from a fundamental restriction on frame rate which has led manufacturers to seek the diminishing returns of continually increasing pixel number, resulting in impractical device sizes, complexity, and cost, as well as enormous real-time computation requirements. Additionally, these devices inherently produce monochromatic and speckled frames due to the requirement that the input beam be coherent.To address these problems, researchers at UC Berkeley have developed a device which can perform spatial light modulation with a frame rate ~20 times higher than existing technologies. This allows for a smaller number of pixels to produce high resolution, full color images by interleaving images of different colors and scanning rapidly across a screen in a similar way to the operation of CRT televisions Researchers have also developed an efficient and robust fabrication method, which combined with the smaller pixel number of these devices could cause them to be much more cost effective than existing technologies.

Measurement of electrical activity in nervous tissue has many applications in medicine, but the implantation of a large number of sensors is traditionally very risky and costly. Devices must be large due to their necessary complexity and power requirements, driving up the risk further and discouraging adoption. To address these problems, researchers at UC Berkeley have developed devices and methods to allow small, very simple and power-efficient sensors to transmit information by backscatter feedback. That is, a much more complex and powerful external interrogator sends an electromagnetic or ultrasound signal, which is modulated by the sensor nodes and reflected back to the interrogator. Machine learning algorithms are then able to map the reflected signals to nervous activity. The asymmetric nature of this process allows most of the complexity to be offloaded to the external interrogator, which is not subject to the same constraints as implanted devices. This allows for larger networks of nodes which can generate higher resolution data at lower risks and costs than existing devices.

Charge detection mass spectrometry (CDMS) effectively bridges the gap in mass measurement technologies and is well suited to the analysis of aerosol-borne viruses and even bacteria such as tuberculosis. CDMS can provide mass measuring accuracies for ions with masses above 500 kDa that are comparable to more expensive conventional instruments and, most importantly, this technology can be applied to ions that are too large (10+ MDa) or heterogeneous to measure using conventional MS. Single pass CDMS instruments have been used to measure masses of large polymers, nanodroplets, dust, and bacterial spores. Mass measurements of MDa-sized PEG molecules and polystyrene nanoparticles (50–110 nm diameter) using an array of 4 detection tubes positioned between the trapping electrodes of an electrostatic ion trap (EIT) have been previously reported. However, no commercial CDMS instrumentation yet exists that can measure masses in the range of 10’s to 1000’s of MDa. UC Berkeley researchers have developed a charge detection mass analyzer which is designed to enable mass measurements of individual ions at rates greater than 10,000 ions per second, ~1000x faster than current state-of-the-art charge detection mass spectrometry instrumentation and other methods that measure molecules >1 MDa in size. 

Cardiovascular disease is among the leading causes of death for citizens in affluent nations, and the most significant cause of morbidity in those with cardiovascular disease is hypertension. Often called the “silent killer” because it has few clinical signs in its early stages, elevated blood pressure is often in an advanced stage before it is treated, leading to a substantially worse prognosis than if it had been detected earlier.In order to address this problem, researchers at UC Berkeley have developed a wearable device which continuously monitors diastolic blood pressure, transmitting data to a portable device such as a cell phone, where it can be stored and analyzed. The device utilizes piezoelectric transducers to perform the measurement, which allows the wearable device to remain small while containing a large number of sensors in order to reduce noise.

Short-time Fourier transforms with short segment lengths are typically used to analyze single ion charge detection mass spectrometry (CDMS) data either to overcome effects of frequency shifts that may occur during the trapping period or to more precisely determine the time at which an ion changes mass, charge or enters an unstable orbit. The short segment lengths can lead to scalloping loss unless a large number of zero-fills are used, making computational time a significant factor in real time analysis of data.    To address the foregoing deficiencies in prior approaches, UC Berkeley researchers have developed an apodization specific fitting that can lead to a 9-fold reduction in computation time compared to zero-filling to a similar extent of accuracy. This makes possible real-time data analysis using a standard desktop computer and capable of separating ions with similar frequencies.  

The inventors have developed a scalable laser aperture that emits light perpendicular to the surface. The aperture can, in principal, scale to arbitrarily large sizes, offering a universal architecture for systems in need of small, intermediate, or high power. The technology is based on photonic crystal apertures, nanostructured apertures that exhibit a quasi-linear dispersion at the center of the Brillouin zone together with a mode-dependent loss controlled by the cavity boundaries, modes, and crystal truncation. Open Dirac cavities protect the fundamental mode and couple higher order modes to lossy bands of the photonic structure. The technology was developed with an open-Dirac electromagnetic aperture, known as a Berkeley Surface Emitting Laser (BKSEL).  The inventors demonstrate a subtle cavity-mode-dependent scaling of losses. For cavities with a quadratic dispersion, detuned from the Dirac singularity, the complex frequencies converge towards each other based on cavity size. While the convergence of the real parts of cavity modes towards each other is delayed, going quickly to zero, the normalized complex free-spectral range converge towards a constant solely governed by the loss rate of Bloch bands. The inventors show that this unique scaling of the complex frequency of cavity modes in open-Dirac electromagnetic apertures guarantees single-mode operation of large cavities. The technology demonstrates scaled up single-mode lasing, and confirmed from far-field measurements. By eliminating limits on electromagnetic aperture size, the technology will enable groundbreaking applications for devices of all sizes, operating at any power level. BACKGROUND Single aperture cavities are bounded by higher order transverse modes, fundamentally limiting the power emitted by single-mode lasers, as well as the brightness of quantum light sources. Electromagnetic apertures support cavity modes that rapidly become arbitrarily close with the size of the aperture. The free-spectral range of existing electromagnetic apertures goes to zero when the size of the aperture increases. As a result, scale-invariant apertures or lasers has remained elusive until now.  Surface-emitting lasers have advantages in scalability over commercially widespread vertical-cavity surface-emitting lasers (VCSELs). When a photonic crystal is truncated to a finite cavity, the continuous bands break up into discrete cavity modes. These higher order modes compete with the fundamental lasing mode and the device becomes more susceptible to multimode lasing response as the cavity size increases. 

This technology utilizes inter-subject measurement of brain activity for the purpose of matching individuals. In particular, the invention measures the similarity and differences in neural activity patterns between interacting individuals (either in person or online) as a signature measurement for their matching capabilities. Relevant applications can be in the world of human resources (e.g., building collaborative teams), patient-therapist matching and others. The application relies on the utilization of both custom and commercial devices for measuring brain activity.

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").

Researchers at UC Berkeley have developed techniques for optical voltage sensing of power grids as well voltage sensing within a human or animal subject. The safe, accurate and economical measurement of time-varying voltages in electric power systems poses a significant challenge. Current systems for measuring power grid voltages typically involve instrument transformers. Although these systems are accurate and robust to environmental conditions, they are bulky, heavy, and expensive, thus limiting their use in microgrids and sensing applications. An additional drawback is that some designs explode when they fail. Optical methods for direct measurement of high voltages have gained attention in recent years, mainly due to the high available bandwidth, intrinsic electrical isolation, and the potential for low cost and remote monitoring. Stage of Research The inventors have developed a low-Q resonant optical cavity-based voltage sensor based on a piezoelectric AIN thin film that transduces a voltage applied across the piezo terminals into a change in the resonant frequency of the cavity. This sensor can be fabricated with high yield and low cost (<$1), which makes it uniquely well-suited to reduce the cost of grid voltage measurement.

There is a wide range of applications for gyroscopes, including: inertial navigation, stabilization, maintaining direction. Many of these applications require low noise. One approach to reducing noise is to increase the mass of the gyroscope transducer. However, this generally comes with increased size and cost. Mode-matched gyroscopes avoid these penalties. These gyroscopes are based on transducers with high quality factor Q. Provided that the resonance frequencies of the drive and sense axes are equal, the noise is suppressed by the quality factor Q. The Q-factor of typical gyroscopes ranges from 1000 to several million, offering dramatic noise reduction. The required precision of mode-matching, which is on the order of 1/Q, presents an implementation challenge. For example, in a mode-matched gyroscope with Q=106, the relative deviation of the frequencies of oscillation of the drive and sense mode must be 10 6. This level of precision is not attainable by typical transducer fabrication techniques such as MEMS or trimming.This innovation presents an alternative approach for continuously monitoring the split between the resonances of the drive and sense modes. While also based on a periodic calibration signal, it does not suffer from corruption of or from the rate measurement. Consequently, the frequency of the calibration signal can be chosen independently of the bandwidth of the rate input and instead set by the required tracking bandwidth of the mode split estimate. The latter is typically dominated by environmental variations such as temperature and on the order of 1Hz or less in typical implementations.

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.

Scanning tunneling microscopy (STM) techniques and associated spectroscopic (STS) methods, such as dI/dV point spectroscopy, have been widely used to measure electronic structures and local density of states of molecules and materials with unprecedented spatial and energy resolutions. However, the quality of dI/dV spectra highly depends on the shape of the probe tips, and atomically sharp tips with well-defined apex structures are required for obtaining reliable spectra. In most cases, STS measurements are performed in ultra-high vacuum  and low temperature (4 K) to minimize disturbances. Advance tip preparation and constant in situ tip conditioning are required before and during the characterization of target molecules and materials. A common way to prepare STM tips is to repetitively poke them on known and bare substrates (i.e. coinage metals or silicon) to remove contaminations and to potentially coat the tip with substrate atoms. The standard dI/dV spectra of the substrate is then used as a reference to determine whether the tip is available for further experiments. However, tip geometry changes during the poking process are unpredictable, and consequently tip conditioning is typically slow and needs to be constantly monitored. Therefore, it restricts the speed of high-quality STM spectroscopic studies. In order to make efficient use of instrument idle time and minimize the research time wasted on tip conditioning, UC Berkeley researchers developed software based on Python and machine learning that can automate the time-consuming tip conditioning processes. The program is designed to do tip conditioning on Au(111) surfaces that are clean or with low molecular coverage with little human intervention. By just one click, the program is capable of continued poking until the tip can generate near-publication quality spectroscopic data on gold surfaces. It can control the operation of a Scienta Omicron STM and automatically analyze the collected topographic images to find bare Au areas that are large enough for tip conditioning. It will then collect dI/dV spectra at selected positions and use machine learning models to determine their quality compared to standard dI/dV spectra for Au20 and determine if the tip is good enough for further STS measurements. If the tip condition is not ideal, the program will control the STM to poke at the identified positions until the machine learning model predicts the tip to be in good condition.

Radiation detectors are typically instrumented with low noise preamplifiers that generate voltage pulses in response to energy deposits from particles (x-rays, gamma-rays, neutrons, protons, muons, etc.). This preamplifier signal must be further processed in order to improve the signal to noise ratio, and then subsequently estimate various properties of the pulse such as the pulse amplitude, timing, and shape. Historically, this “pulse processing” was carried out with complex, purpose-built analog electronics. With the advent of digital computing and fast analog to digital converters, this type of processing can be carried out in the digital domain.There are a number of commercial products that perform “hardware” digital pulse processing. The common element among these offerings is that the pulse processing algorithms are implemented in hardware (typically an FPGA or high performance DSP chip). However this hardware approach is expensive, and it's hard to tailor for a specific detector and application.To address these issues, researchers at UC Berkeley developed a solution that performs the pulse processing in software on a general purpose computer, using digital signal processing techniques. The only required hardware is a general purpose, high speed analog to digital converter that's capable of streaming the digitized detector preamplifier signal into computer memory without gaps. The Berkeley approach is agnostic to the hardware, and is implemented in such a way as to accommodate various hardware front-ends. For example, a Berkeley implementation uses the PicoScope 3000 and 5000 series USB3 oscilloscopes as the hardware front-end. That setup has been used to process the signal from a number of semiconductor and scintillator detectors, with results that are comparable to analog and hardware digital pulse processors.In comparison to current hardware solutions, this new software solution is much less expensive, and much more easily configurable. More specifically, the properties of the digital pulse shaping filter, trigger criteria, methods for estimating the pulse parameters, and formatting/filtering of the output data can be adjusted and tuned by writing simple C/C++ code.

An improved method for isothermal nucleic acid detection based on a loop mediated isothermal amplification (LAMP) technique that can be broadly applied for nucleic acid diagnostics.LAMP is an isothermal amplification method that amplifies DNA or RNA. This iteration of LAMP allows for the integration of any short DNA sequence, including tags, restriction enzyme sites, or promoters, into an isothermally amplified amplicon. The technique presented by the inventors allows for the insertion of sequence tags up to 35 nt into the flanking regions of the LAMP amplicon using the forward and backward inner primers (FIP and BIP), and loop primers. The inventors have demonstrated insertion of sequence fragments into the 5’ and middle regions of the FIP and BIP primers, and the 5’ region of the loop primers. In some embodiments, the sequence tag comprises a T7 RNA polymerase promoter, which is then incorporated into the LAMP amplicon (termed RT-LAMP/T7). With the addition of T7 polymerase, the amplicon can be in vitro transcribed, leading to additional amplification of the target molecule into an RNA substrate. This improves the efficiency of the amplification reaction and enables substrate conversion into different nucleic acid types.In other embodiments, the amplified RNA sequence can be detected by CRISPR enzymes, such as RNA-targeting Cas13 systems. 

This invention leverages the nuclease activity of CRISPR proteins for the direct, sensitive detection of specific nucleic acid sequences. This all-in-one detection modality includes an internal Nuclease Chain Reaction (NCR), which possesses an amplifying, feed-forward loop to generate an exponential signal upon detection of a target nucleic acid.Cas13 or Cas12 enzymes can be programmed with a guide RNA that recognizes a desired target sequence, activating a non-specific RNase or DNase activity. This can be used to release a detectable label. On its own, this approach is inherently limited in sensitivity and current methods require an amplification of genetic material before CRISPR-base detection. 

The skin sensory behavior may be mimicked using potentiometric mechanosensation and thermosensation mechanisms of this invention by regulating the potential difference between electrodes at two different electrode/electrolyte interfaces. Basically, when bringing two categories of rationally selected electrode materials into contact with an electrolyte, two different electrode/electrolyte interfaces can be formed with a potential difference measured between the two electrodes. Via structural and component manipulation of the electrolyte, external mechanical and thermal stimuli may be encoded into potential difference variation between the two electrodes, just like skin sensory cells coupling external stimuli into membrane potential variation

Native mass spectrometry (MS), in which electrospray ionization (ESI) is used to transfer large macromolecules and macromolecular complexes directly from solution into the gas phase, is a powerful tool in structural biology.  However, charge-state distributions of individual components in mixtures of macromolecular complexes or synthetic polymers are often unresolved making it impossible to obtain mass information directly from an ESI mass spectrum. Other conventional methods can provide accurate masses of individual ions, but often at the expense of analysis time.     Weighing ions individually with charge detection mass spectrometry (CDMS) has the advantage that fast measurements are possible depending on the accuracy and sensitivity required. However, a limitation of trapping CDMS technology is the need to weigh single ions individually in order to eliminate potential interferences between the signals of multiple ions or ion-ion interactions that can potentially interfere with these measurements. UC researchers have created multiplex charge detection mass spectroscopy, particularly for high throughput single ion analysis of large molecules and measuring the masses of large molecules, macromolecular complexes and synthetic polymers that are too large or heterogeneous for conventional mass spectrometry measurements.  The new multiplexing method makes it possible to measure the masses of many ions simultaneously.  

This invention are flexible pressure sensors with high sensitivity, broad working range and good scalability are highly desired for the next-generation of wearable electronic devices. Embodiments include large-area compliant and cost-effective processes to fabricate high-performance pressure sensors using mesh-molded periodic microstructures and printed side-by-side electrodes. 

 This is a capture and analysis system that efficiently captures plume particles, does not degrade the entrained organic molecules, that can be effectively and efficiently analyzed, that can be readily cleaned to provide low background and forward contamination, and that has high sensitivity for analyzing the trace organics.

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.