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(SD2021-377) Pressure-stabilized dual inlet gas mass spectrometry

Mass spectrometers for high precision gas isotope measurements (e.g., noble gases, carbon, nitrogen) are typically equipped with a dual inlet system in which one side contains the unknown sample gas and the second side contains a known standard. Repeated comparisons of the two gases allows precise determination of differences in the gas composition. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Biodegradable Potentiometric Sensor to Measure Ion Concentration in Soil

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. 

Fumigant Detoxification via Reusable Cotton Material

Researchers at the University of California, Davis have developed wearable, highly adsorptive, cotton fabrics that can neutralize fumigants in both open-air and sequestered environments.

Method For Rapid In Situ Detection Of Ammonia

This invention, a simple and robust method for ammonia detection, offers high-speed in situ quantification of ammonia concentrations with high sensitivity. The ammonia detection system does not require complex instrumentation, analysis, or labeling, which would allow for widespread adoption in chemistry-based fields and surrounding disciplines.

Deep Learning Techniques For In Vivo Elasticity Imaging

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.

Particle-Sorting Device for Isolation, and Enrichment of Particles at Ultra-Low Concentrations

The ability to detect and sort particles by type is important to many fields, such as medical diagnostics, environmental monitoring, and food safety.UCI researchers have developed a platform to sort and isolate particles from a turbid medium with minimal pre-processing. The platform is very desirable for applications in which enrichment of particles or biological substances at low concentrations is necessary.

Guided-Wave Powered Wireless Sensors

UCLA researchers in the Department of Electrical and Computer Engineering have developed a wirelessly powered, flexible sensor that detects pipe leaks over long distances.

Autonomous Comfort Systems Via An Infrared-Fused Vision-Driven Robotic Systems

Robotic comfort systems have been developed which use fans to deliver heated/cooling air to building occupants to provide greater levels of personal comfort.  However, current robotic systems rely on surveys asking individuals about their comfort state through a web interface or app.  This reliance on user feedback becomes impractical due to survey fatigue on the part of the user.  Researchers at the University of California, Berkeley have developed a system which uses a visible light camera located on the nozzle of a robotic fan to detect human facial features (e.g., eyes, nose, and lips).  Images from a co-located thermal camera are then registered onto the visible light image and temperatures of different facial features are captured and used to infer the comfort state of the individual.  Accordingly, the fan/heater system blows air with a specific velocity and temperature toward the occupant via a closed-loop feedback control.  Since the system can track a person in an environment, it addresses issues with prior data collection systems that needed occupants to be positioned in a specific location.

Predictive Controller that Optimizes Energy and Water Used to Cool Livestock

Researchers at the University of California, Davis have developed a controller that applies environmental data to optimizing operations of livestock cooling equipment.

Ultra-Sensitive Polybrominated Diphenyl Ether (PBDE) Detector

Polybrominated diphenyl ethers (PBDEs) are a common brominated flame retardant, which are commonly found in consumer products. Because they are not chemically bound to polymers, PBDEs are blended in during formation and have the ability to migrate from products into the environment.  Studies suggest that PBDEs pose potential health risks such as hormone disruptors, adverse neurobehavioral toxins and reproductive or developmental effects.  For this reason it is important to have the capability to sense the presence of PBDEs even in low concentrations.

Metal Triazolites

UCLA researchers in the Department of Chemistry and Biochemistry have developed a novel metal-organic framework (MOF) using triazole ligands that allows for facile modification with a variety of metals, which has unique gas separation and adsorption properties.

Multi-Tone Continuous Wave LIDAR

Object detection and ranging is a fundamental task for several applications such as autonomous vehicles, atmospheric observations, 3D imaging, topography and mapping. UCI researchers have developed a light detection and ranging (LIDAR) system which makes use of frequency modulated continuous waves (FMCW) with several simultaneous radiofrequency tones for improved speed of measurement while maintaining robust spatial information. 

Automatic Fine-Grained Radio Map Construction and Adaptation

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.

Spectro-Temporal Lidar

UCLA researchers in the Department of Electrical and Computer Engineering have developed a LIDAR sensor that collects high frame-rate 3D measurements for autonomous vehicle and robotics applications.

Automated Immersion Mode Ice Spectroscopy

Ice nucleating particles (INPs) suspended in the Earth’s atmosphere influence cloud properties and can affect the overall precipitation efficiency and predictability of cloud systems worldwide. INPs induce freezing of cloud droplets at temperatures above their normal freezing-point (~-38 C), and at a relative humidity (RH) below the normal freezing RH of aqueous solution droplets at lower temperatures. These INP induced variabilities influence cloud lifetime, phase, as well as cloud optical and microphysical properties. Developing a relational model of INPs in global climate models has proven challenging as existing instrumentation systems either require too much air volume (in real-time flow instruments) or exhibit too much temperature variability (in off-line frozen assay based instruments).  Thus, there is a real urgency to address this unmet need.

Device-Free Human Identification System

In our electronically connected society, human identification systems are critical to secure authentication, and also enabling for tailored services to individuals. Conventional human identification systems, such as biometric-based or vision-based approaches, require either the deployment of dedicated infrastructure, or the active cooperation of users to carry devices. Consequently, pervasive implementation of conventional human identification systems is expensive, inconvenient, or intrusive to privacy. Recently, WiFi infrastructure, and associated WiFi-enabled mobile and IoT devices have become ubiquitous, and correspondingly, have enabled many context-aware and location-based services. To address the challenges of human identification systems and take advantage of the popularity of WiFi, researchers at UC Berkeley developed a human identification system based on analyzing signals from existing WiFi-enabled devices. This novel device-free approach uses WiFi signal analysis to reveal the unique, fine-grained gait patterns of individuals as the "fingerprint" for human identification.

A New and Cost-Effective Technology to Produce Hybrid-Glass/Optical Bubble Probes

The ability to accurately quantify gas volumes in liquid flows has important applications in environmental science and industry. For example, environmental processes that significantly contribute to changes in earth’s climate, such as methane seeps from the sea floor and the exchange of gases between the ocean and atmosphere at the sea surface, demand precise sensors that are small and sensitive enough to measure the ratio of liquids and gases in these bubbly mixtures. These measurements also play a critical role in the operational efficiency of a wide variety of different engineering processes. Applications include, the monitoring the optimal amount of bubbled oxygen in the treatment of waste water and sewage, and the oil and gas industry, especially in undersea oil pipelines in the Gulf of Mexico alone, have spent billions of dollars annually on added refinement techniques to remove seawater that could be preventable if sensors were able to measure the ratio of crude oil, seawater and gas as the mixture is pumped through pipelines. These challenges exist in both research and industry because the current manufacturing process for making the needed gas/liquid probes have significant cost constraints. Clearly, there is a need for a new and cost-effective technology to produce these probes.

Hydrogen Gas Sensors Based On Patterned Carbon Nanotube Ropes

This is a fabrication method for hydrogen gas sensors; these sensors have more rapid response times and are more sensitive than current detection techniques.

GPS-Based Miniature Oceanographic Wave Measuring Buoy System

Oceanic monitoring helps coastal communities, economies, and ecosystems thrive. The coastlines and open oceans prove to be very important to maritime countries for recreation, mineral and energy exploitation, shipping, weather forecasting and national security. As solar power, GPS, and telecomm improvements have been made, directional wave buoys have emerged and set the standard in wave monitoring. Non-directional and directional wave measurements are of high interest to users because of the importance of wave monitoring for successful marine operations. Wave data and climatological information derived from the data are also used for a variety of engineering and scientific applications.

Combined Greywater-Storm Water System With Forecast Integration

Water is a scarce resource in some part of the United States, and recent droughts in the Midwest and the South have elevated the issue of water scarcity to a national level. Existing water sources will face increasing strain due to population growth and climate change, and financial and regulatory barriers will prevent the development of new sources. One method to alleviate water scarcity is storm water capture. Storm water can be used for non-potable applications such as irrigation, laundry, and toilet flushing to significantly reduce domestic municipal water consumption. However, in arid regions of the US, rain comes in short, intense storms only a few months out of the year, and the duration and intensity of these storms require large storage tank volumes for storm water capture to be financially feasible.    One solution is to integrate storm water capture with greywater capture. Greywater is a reliable source of water for domestic reuse, and includes water from washbasins, laundry, and showers (kitchen sinks and water for toilet flushing are considered blackwater). Combining greywater-storm water in the same collection system allows for a much smaller storage tank. A UC Berkeley researcher, along with other researchers, have developed aforecast-integrated automated control system for combined greywater-storm water storage and reuse. A simple and reliable approach for managing greywater and storm water collection at a household or community level is provided, allowing for the near-continuous monitoring and adjustment of water quantity and quality in a combined greywater-storm water storage tank based on monitored feedback/output from individual, tank-specific sensors and/or sensors located elsewhere in the water collection system.   

Novel Sensor to Transduce and Digitalize Temperature Utilizing Near-Zero-Power Levels

Temperature sensors are routinely found in devices used to monitor the environment, the human body, industrial equipment, and beyond. In many such applications, the energy available from batteries or the power available from energy harvesters is extremely limited, thus the power consumption of sensing should be minimized in order to maximize operational lifetime.

The Flying Wing Autonomous Underwater Glider Technology

The underwater glider can be categorized as an autonomous underwater vehicle (AUV) that does not rely on an electrically driven propeller, but relies on small changes in its buoyancy and wings to move up and down. The pitch and roll is controlled by using an adjustable ballast. The AUV has been quite useful for collecting oceanographic data due to its unique propulsion system that uses very little energy and its ability to be on a sampling mission for weeks to months.

Highly Stretchable & Flexible Electronic Sensors

A new approach to creating highly stretchable electronic devices using twisted conductive microtubules.

Quantification Of Plant Chlorophyll Content Using Google Glass

UCLA researchers in the Department of Electrical Engineering have invented a novel device that can quantify chlorophyll concentration in plants using a custom-designed Google Glass app.

Individual Identity Verified Through Device-Free, WiFi Based Framework

Researchers at the University of California, Davis have developed a device-free, WiFi based framework that can isolate individual identity, from a small group of users, simply by observing variations in WiFi signals through a user’s gait.

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