Engineers from UC San Diego have disclosed a new patent-pending technology (SHARP, VERTICALLY ALIGNED NANOWIRE ELECTRODE ARRAYS, HIGH-YIELD FABRICATION ANDINTRACELLULAR RECORDING) that minimizes the electrode size to an intracellular probe, and is scalable to integrate multiple channels at one platform and overcomes the previous disadvantages such as invasiveness and insensitivity. This newly disclosed improved technology reduces the number of steps and the number of metal layers used to increase the biocompatibility and device yield, as compared to an earlier disclosure for NEAs that were fabricated using a different process.

Scientists at UC Irvine have developed a sensitive, customizable, and user-friendly sensor for (1) strain detection as a result of cellular movement, (2) micro-fluidic device pressure detection, and (3) real-time monitoring of valve statuses in microfluidic chips. This research tool will provide new insights regarding cellular biophysics.

One of the key limitations for devices used in point-of-care diagnostics (POCD) is their limit of detection; patient samples used for POCD devices often contain too low of the target analyte. FlexThrough is a newly developed, centrifugal disk (CD)-based method that utilizes the entirety of a liquid sample via recirculation of the sample for efficient mixing as it iteratively passes through the system.

Researchers at UC Irvine have developed an optical detection system for SARS-CoV-2 and other pathogens that features improvements in screening time, cost, sensitivity, and practicality. As vaccine availability, economic pressure, and mental health considerations has gradually returned society to pre-pandemic activities that require frequent and close interactions, it is imperative that SARS-CoV-2 detection systems remain effective.

Optical chromatography (OC) is an optofluidic technique enabling label-free fractionation of microscopic particles, e.g., bioparticles from heterogenous mixtures. This technique relies on a laser beam along a microfluidic channel to create opposing optical scattering and fluidic drag forces. Variable strength and balance of these forces may be harnessed for selective sorting of bioparticles based on their size, composition, and morphology. OC has been successfully applied to fractionation of blood components such as human erythrocytes, monocytes, granulocytes, and lymphocytes. OC offers unique capabilities as a modern separation technique, especially when combined with multi-stage sequential fractionation and microfluidic network-based purification approaches, and it particularly excels in distinguishing bioparticles with subtle differences. However, there are several key limitations with OC being widely adopted. In order to create strong optical scattering forces along the microfluidic channels, expensive and sophisticated laser sources must be precisely aligned along the fluidic channel with a well-controlled beam waist profile, requiring a complicated optical alignment procedure that employs multiple multi-axis positioners. While microfluidic approaches using OC hold promise for broader use, multiplexed and high throughput systems remain overly complicated and cost-prohibitive.

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.

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.

A fundamental limitation for the implantable brain-machine interfaces (BMI) is the wiring requirements for power transfer and signal transmission. Microelectrode arrays (MEAs), the workhorse technology in neuroscience, offer multiplexed electrophysiological recordings with high temporal resolution. However, their use is inherently limited to a few hundred electrodes as direct electronic measurements suffer from complex wiring requirements and inherent bandwidth (spatial multiplexing) limitations due to electron-electron interactions within conductors. Moreover, electrode arrays can only record from small sections of the brain and require invasive cranial surgical operations. The recent discovery of genetically encoded voltage sensitive fluorescence indicators (GEVI) has created tremendous excitement as light offers unparalleled multiplexing and information carrying capabilities. However, GEVI cannot be used in humans as it requires expression of voltage sensitive molecules by neurons and therefore genetic manipulation. In addition, attenuation of visible light in biological tissue renders much of the brain inaccessable to fluorescence based techniques.  

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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. 

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Researchers at the University of California, Irvine have fabricated a flexible and unobtrusive wearable electrode that can detect glucose at a very low limit of detection.In fact, the detection limits are the lowest ever reported for an enzyme-free sensor. This sensor is applicable for detecting glucose levels in saliva, sweat or tears, and can safely be used at home, especially by diabetic patients without the need to frequently draw blood.

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.

Researchers from the University of California, Irvine have developed a new method and device to efficiently mix and analyze liquid samples on CD-based point of care devices.

Prof. Nosang Myung and colleagues from the University of California, Riverside have developed state-of-the-art gas sensors that may be used to create an electronic nose. This device is known as ChromaNose. ChromaNose is capable of sensing carbon monoxide, hydrogen sulfide, hydrogen gas, oxygen gas, nitrogen dioxide, and ammonia at room temperature. This technology may be used in various applications to detect harmful chemicals that people cannot see or smell. For example, ChromaNose may detect cleaning solvent residue left in masks worn by Air Force personnel. The inhalation of cleaning solvent residue causes the wearer to become ill. It would be desirable to detect and remove any cleaning solvents remaining in a mask to prevent illness. Fig 1: Image of the UCR Pt/SnO2/SWNT hybrid nanostructure sensors.

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.

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.

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.