Volatile organic compounds (VOCs) are released by various products and during various processes. Ethanol is one such VOC that is released as an important byproduct of alcoholic fermentation. Ethanol emitted during fermentation can be estimated using the amount of liquid lost during storage. The instrumentation needed to accurately quantify ethanol emissions is specialized and costly. Researchers at UC Santa Cruz have developed low-cost VOC sensors that are useful for the wine industry, among others.

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Determining the properties that control fluid flow and pressure migration through rocks is essential for understanding groundwater, energy reservoirs and fault zones. Hydraulic diffusivity is the key parameter that controls pressure migration in reservoirs. There is a need to determine it in situ for energy, groundwater and earthquake applications. Direct measurements of these properties underground generally require expensive and invasive processes such as pumping large volumes of water in or out of the ground. Most current methods rely on either active pumping between wells or proxies such as seismic velocity or the migration time of microseismicity. These conventional methods may change the structure that they are trying to measure and do not resolve variations in space without complex, multiple experiments. Moreover, active pumping is expensive, invasive and sensitive to a limited set of scales, while proxies are difficult to calibrate.

This invention overcomes a limitation of in vivo mutagenesis systems. Some methods of mutagenesis involve treatment of plasmids with mutagenic chemicals or UV light prior to transformation, but these result in biased mutation spectra. Use of error prone DNA polymerases produces a more random set of mutations, but the rate of mutagenesis rapidly declines with continuous culture. As a result, using such polymerasaes separates mutagenesis and selection into multiple steps. Mutant genes in plasmids need to be generated by the error prone polymerase, then the plasmids isolated into libraries and selected in a separate step. What is needed, then is an error prone DNA polymerase that does not result in a decline in the rate of mutagenesis in culture.  

Researchers from UC San Diego have developed an invention that allows the continuous monitoring of atmospheric He, Ne, and H2 at unprecedented precision. This enables important new applications including in the understanding of the global carbon cycle, verifying reported natural gas emissions, and predicting earthquakes.

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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;}

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

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.

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.

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

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

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.

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. 

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.

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.

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.