Development of a piezo stack – laser doppler vibrometer sensing approach for characterizing shear wave dispersion and local viscoelastic property distributions
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Cai, Bowen (ORCID:0000000262931041)↗
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Results for “Distributed Sensing”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic
Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.
54 ENVIRONMENTAL SCIENCES↗
Deep learning models map rapid plant species changes from citizen science and remote sensing data
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.
Gillespie, Lauren E.↗
Brillouin Sensing with PCA, and PCA-Based Neural Networks for Efficient Temperature Monitoring
This work explores peak estimation techniques in Brillouin Optical Time Domain Analysis (BOTDA), emphasizing both accuracy and efficiency. Euclidean distance measurement method is applied to principal components derived from Brillouin Gain Spectrum data. It offers a major speed advantage being 180 170 times faster than traditional curve fitting methods such as Lorentzian curve fitting, while maintaining similar accuracy. Additionally, a PCA- based neural network model shows significant reduction of peak estimation time compared to Lorentzian fitting. Results show Brillouin frequency shift errors lie under 0.75 MHz in both Euclidean distance-based and neural network-based methods, both of which utilize PCA components. For large data sets and long length fibers, PCA- assisted neural network for peak estimation would be an efficient solution.
Distributed optical fiber sensing↗
Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring
Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.
03 NATURAL GAS↗
Development of a practical fiber optic system for in-situ, online monitoring of power cable degradation
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24 POWER TRANSMISSION AND DISTRIBUTION↗
Quality Investigation of Pultruded Carbon Fiber Panels Subjected to Four-Point Flexure via Fiber Optic Sensing
Pultruded carbon fiber-reinforced composites are attractive to the wind energy industry due to the rapid production of highly aligned unidirectional composites with enhanced fiber volume fractions and increased specific strength and stiffness. However, high volume carbon fiber manufacturing remains cost-prohibitive. This study investigates the feasibility of a pultruded low-cost textile carbon fiber-reinforced epoxy composite as a promising material in spar cap production was undertaken based on mechanical response to four-point flexure loading. As spar caps are primarily subjected to flexural loading, large-span four-point flexure was considered, and coupon testing was restricted to tensile modulus and compression strength assessment. High-resolution spatial fiber optic strain sensing was utilized to determine spatial strain distribution during four-point flexure, revealing consistent strain along the length of the part and proved to be an excellent option for process manufacturing quality examination. Additionally, holes with diameters of 2.49 mm, 5.08 mm, and 1.93 mm were drilled through the thickness of full-width parts to determine the feasibility of structural health monitoring of pultruding parts internal to wind blades via fiber optic strain sensing.
Chemistry↗
Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity
Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).
14 SOLAR ENERGY↗
Topography and functional traits shape the distribution of key shrub plant functional types in low-Arctic tundra
The expansion of shrubs in the Arctic tundra fundamentally modifies land-atmosphere interactions. However, it remains unclear how shrub distribution and expansion differ across key species due to challenges with discriminating tundra plant species at regional scales. Here, we combined multi-scale, multi-platform remote sensing and in situ trait measurements to elucidate the distribution patterns and primary controls of two representative deciduous-tall-shrub (DTS) genera, Alnus and Salix, in low-Arctic tundra. We show that topographic features were a key control on DTSs, creating heterogeneous, but predictable distributions of Alnus and Salix fractional cover (fCover). Alnus was more tolerant of elevation and slope and was found on hilly uplands (slope >10°) within a specific elevational band (200–400 m above sea level [MSL]). In contrast, Salix occurred at lower elevations (50–300 m MSL) on gentler slopes (3-10°) and required adequate soil moisture associated with its profligate water use. We also show that niche differentiation between Alnus and Salix changed with patch size, where larger patches were more specialized in resource requirements than individual plants of Alnus and Salix. To understand what constrains the growth of DTSs at locations with low fCover, we developed environmental limiting factor models, which showed that topography limits the upper bound of Alnus and Salix fCover in 69.2% and 48.7% of the landscape, respectively. These findings highlight a critical need to better understand and represent topography-controlled processes and functional traits in regulating shrub distribution, as well as a need for more detailed species classification to predict shrubification in the Arctic.
alder↗
Fe-Coated Optical Fiber for Distributed Corrosion Monitoring in Soil and Aqueous Environments
Natural gas pipeline corrosion represents a substantial cost and safety concern during normal operations. The effective and real-time monitoring of corrosion is important to detect and mitigate pipeline risks before corrosion-related catastrophic events happen. Here, we describe the use of iron (Fe) coated optical fiber sensors (OFS) for distributed corrosion monitoring where Fe acts as a corrosion proxy. By using an optical backscattering reflectometer (OBR), corrosion was monitored based on the increase in the backscattered intensity amplitude of the light being passed as Fe underwent corrosion. The Fe-coated OFSs were prepared with a film thickness between 25–225 nanometers (nm) by an electroless plating approach and corroded by a carbon dioxide (CO2)-saturated acidic electrolyte. The corrosion rate (CR) was approximately 2.5 millimeters (mm)/year for Fe with a film thickness of 30 nm and increased with film thickness. Backscattering-based CRs were supported by visible light transmission measurements, which have been previously demonstrated. Additionally, a correlation between the intermittent transmission and residual Fe film thickness during the corrosion of Fe was established. The corrosion was measurable out to > 100 meters (m), which is a significant improvement over our previous work, which showed corrosion sensing at < 10 m. The corrosion sensor was also tested in soil at > 1 foot depth.
corrosion↗
Quantum Frequency Combs with Path Identity for Quantum Remote Sensing
Quantum sensing promises to revolutionize sensing applications by employing quantum states of light or matter as sensing probes. Photons are the clear choice as quantum probes for remote sensing because they can travel to and interact with a distant target. Existing schemes are mainly based on the quantum illumination framework, which requires quantum memory to store a single photon of an initially entangled pair until its twin reflects off a target and returns for final correlation measurements. Existing demonstrations are limited to tabletop experiments, and expanding the sensing range faces various roadblocks, including long-time quantum storage and photon loss and noise when transmitting quantum signals over long distances. We propose a novel quantum sensing framework that addresses these challenges using quantum frequency combs with path identity for remote sensing of signatures (“qCOMBPASS”). The combination of one key quantum phenomenon and two quantum resources—namely, quantum-induced coherence by path identity, quantum frequency combs, and two-mode squeezed light—allows for quantum remote sensing without requiring quantum memory. The proposed scheme is akin to a quantum radar based on entangled frequency-comb pairs that uses path identity to detect, range, or sense a remote target of interest by measuring pulses of one comb in the pair that never traveled to the target but that contains target information “teleported” by quantum-induced coherence by path identity from the other comb in the pair that traveled to the target but is not detected. We develop the basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce the qCOMBPASS equation—a quantum analog of the well-known LIDAR equation in classical remote sensing. We also describe an experimental scheme to demonstrate the concept using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various applications in remote quantum sensing, imaging, metrology, and communications. These applications include detection and ranging of low-reflectivity objects, measurement of small displacements of a remote target with precision beyond the standard quantum limit (SQL), standoff hyperspectral quantum imaging, discreet surveillance from space with low detection probability (detect without being detected), very-long-baseline interferometry, quantum Doppler sensing, quantum clock synchronization, and networks of distributed quantum sensors. Published by the American Physical Society 2024
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Analysis of Niobium Electropolishing Using a Generalized Distribution of Relaxation Times Method
Using electrochemical impedance spectroscopy, we have devised a method of sensing the microscopic surface conditions on the surface of niobium as it is undergoing an electrochemical polishing (EP) treatment. The method uses electrochemical impedance spectroscopy (EIS) to gather information on the surface state of the electrode without disrupting the polishing reaction. The EIS data is analyzed using a so-called distribution of relaxation times (DRT) method. Using DRT, the EIS data can be deconvolved into discrete relaxation time peaks without any a priori knowledge of the electrode dynamics. By analyzing the relaxation time peaks, we are able to distinguish two distinct modes of the EP reaction. As the polishing voltage is increased, the electrode transitions from the low voltage EP mode, characterized by a single relaxation time peaks, to the high voltage EP mode, characterized by two relaxation time peaks. We theorize that this second peak is caused by the formation of an oxide layer on the electrode. We also find that this oxide induced peak transitions from to a negative relaxation time, which is indicative of a blocking electrode process. By analyzing EPed samples, we show that samples polished in the low voltage mode have significantly higher surface roughness due to grain etching and faceting. We find that the surface roughness of the samples only improves when the oxide film peak is present and in the negative relaxation time region. This shows that EIS combined with DRT analysis can be used to predict etching on EPed Nb. This method can also be performed before or during the EP, which could allow for adjustment of polishing parameters to guarantee a smooth cavity surface finish.
43 PARTICLE ACCELERATORS↗
Continuous surface-to-distributed acoustic sensor snapshots explain reactivation of individual natural fractures during an unconventional reservoir stimulation
ABSTRACT Fiber-optic sensing technologies allow petroleum engineering teams to detect hydraulic fracture interaction with boreholes during unconventional reservoir stimulation. In combination with high-repeatability seismic sources, the same distributed acoustic sensors (DASs) enable vertical seismic profiling (VSP) of the fracture evolution away from the boreholes. We discovered clear signatures of seismic scattering on activated fractures during nine days of continuous seismic monitoring of the fracturing stages at the Austin Chalk/Eagle Ford Field Laboratory. The present study applies a novel approach for quantitative analysis of the scattering events in terms of the evolution of the geometry and elastic stiffness of individual fractures. Our characterization strategy sequentially refines the fracture models: from a stack of 1D soft layers to 3D rectangular inclusions. First, we estimate the number of fracture locations and reflectivity using a modified sparse-spike deconvolution of the stacked VSP traces. The fracture set consists of five fractures spaced by 15–30 m with a reflectivity of approximately 1%. Then, we develop a scattering integral method to refine these estimates along with an inversion of the fracture top and bottom for each monitoring vintage. We find that, initially, some of the fractures are located above the monitoring fiber with the height of approximately 100 m. Then we integrate the seismic interpretation with the low-frequency DAS and pressure and microseismic monitoring to reconstruct the activation process of the fractures. Most likely, some of the natural fractures slowly grew downward to the monitoring fiber as a result of fluid injections in the stimulated well. This led to bright strain anomalies but did not trigger seismicity. The top of the fractures remained almost constant and were limited by a lithologic boundary/stress barrier. To our knowledge, this is the first time VSP data enabled tracking of the fracture evolution with such high spatial and temporal resolution, which was previously only available for crosswell surveys and at a much smaller scale.
Glubokovskikh, Stanislav↗
Template‐Directed Growth of Palladium Nanoparticles in Holey Single‐Layer Graphene for High‐Performance Room‐Temperature Hydrogen Sensing
Holey graphene (HG), formed by introducing nanoscale perforations into graphene sheets, combines the structural advantages of continuous sp 2 conjugation with the beneficial effects of high surface area and enhanced chemical reactivity associated with nanoscopic holes. Conventional HG fabrication methods often rely on harsh oxidative treatments that compromise graphene's intrinsic electronic properties by disrupting its sp 2 conjugation. A new approach is introduced to fabricate HG directly from single-layer graphene (SLG), using two-dimensional covalent organic frameworks (COFs) as a template followed by controlled oxygen plasma etching. This method preserves the integrity of the SLG's sp 2 network while producing well-defined holes with an average diameter of 2.7 nm. These hole edges act as reactive sites that facilitate the confined, autoreductive growth of palladium nanoparticles (PdNPs) without external reducing agents. The confined hole geometry prevents NP agglomeration and ensures a uniform size distribution. The resulting Pd@HG hybrid exhibits exceptional chemiresistive hydrogen sensing characteristics, including ultrahigh sensitivity, low detection limits, rapid response and recovery, and long-term stability under both dry and humid conditions. Mechanistic investigations reveal a two-step sensing process involving surface redox interactions and hydrogen absorption into the PdNP lattices. This strategy presents a scalable platform for integrating metal NPs within conductive carbon frameworks for advanced sensing applications.
chemical vapor deposition↗
A recyclable self-healing composite with advanced sensing property
Polymer-based composites frequently encounter damage, often lurking beneath the surface and proving challenges to their early detection and repair. While material-based sensors show promise for encoding self-sensing properties within these composites, their in situ healing and reprocessability remain significant challenges. Therefore, the overarching goal of this study is the creation of a reprocessable polymeric composite encoded with self-healing attributes and the ability to autonomously sense damage. At the core of this innovation are vitrimers, a polymeric material characterized by a covalently adaptive dynamic network responsive to external factors such as heat. They combine thermoset-like resilience with thermoplastic-like flowability on demand under external stimuli. We nanoengineer a polyester-based vitrimeric polymer by incorporating piezoresistive carbon nanotubes (CNTs) as reinforcing elements that not only enhance its mechanical strength but also create a percolation network within the composite, thereby enabling piezoresistive self-sensing properties, all the while preserving the intrinsic self-healing capabilities offered by the vitrimeric matrix. The fabrication process of the composite involves a solvent-free in situ polymerization method that combines epoxy and anhydride-containing monomers with ~ 0.1 wt.% of CNTs. Once it was established that the introduction of CNTs into the polymeric matrix did not compromise the mechanical properties of the composite, their strain-sensing properties were characterized by applying cyclic loading while measuring their electrical resistance. Strikingly, CNT-enhanced vitrimer composite consistently retains its mechanical and sensing properties through repeated cycles of reshaping and reprocessing, underscoring its potential as a robust distributed strain sensor. This polyester-based vitrimeric composite is also easily recyclable without harsh chemical treatments. Preliminary findings from this study conclusively demonstrate that the bulk composite boasts both self-sensing capabilities and in situ detect healing properties, charting a promising course towards the development of a mechanically resilient multifunctional composite that seamlessly integrates selfsensing and healing capabilities.
Rohewal, Sargun Singh↗
A new data-driven map predicts substantial undocumented peatland areas in Amazonia
Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km 2 ) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km 2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin.
54 ENVIRONMENTAL SCIENCES↗
Distributed Magnetic Field and Temperature Monitoring for Superconducting Radio Frequency Cavities
The overall objective of the proposed Phase I program was to design, construct and demonstrate a fiber optic sensing system capable of providing temperature and magnetic field measurements with an enhanced spatial resolution that can be implemented over a large surface area (cryomodules) to survey superconducting radio frequency cavities and magnets. A magnetic field sensor capable of detecting fluxes on the order of 1 μT is required to detect the distribution of trapped flux on the cavity surface. A unique distributed magnetic field sensor was successfully designed and constructed to demonstrate the detection of magnetic fluxes less than 500 nT. The sensor leveraged the ultra-high sensitivity of Sentek’s picoDAS to measure the magnetostriction induced vibrations in a commercially available Metglas 2605 SC ribbon that was in physical contact with sensing fiber. Static magnetic fields were detected by applying an alternating current a copper wire proximate to the Metglas 2605SC ribbon to create an AC bias magnetic field. In an alternative approach, an AC bias magnetic field was applied to a special magnetic field sensing fiber with Metglas 2605SC cladding successfully detect a magnetic field flux of a 3 μT. Exhaustive testing was performed to characterize the dependency of sensor response on the direction of the applied magnetic field. Although the special sensing fiber based magnetic field sensor did not exhibit an observable dependence on the direction of the magnetic field, the Metglas 2605SC ribbon sensor exhibited a clear directional dependence. A wide variety of polymer materials were evaluated to enhance the temperature response of an FBG based sensor at cryogenic temperatures. The processing and performance challenges provided the motivation to develop a new simple cryogenic temperature sensor that uses a commercially available fiber optic splice protector. The EVA hot melt tube that becomes adhered to the optical fiber and the polyolefin outer tube that shrinks upon heating in the fusion splicer heater provide the high thermal expansion coefficient necessary to impart a significant strain on the FBG when exposed to cryogenic temperatures. The temperature sensitivity (Δ𝜆𝐵𝑟𝑎𝑔𝑔~ 62 𝑝𝑝𝑚/℃) of the FBG-based sensor was on par with the best reported to date. The simple design, use of readily available cost-effective materials, and well-established processing techniques lends this approach to the creation of hundreds to thousands of temperature sensors on one single optical fiber length. The inherently small form factor also allows for co-location with the distributed magnetic field sensor. In preparation for field testing of the prototype sensing system at the Jefferson Labs in potential Phase II program, several different cable designs were evaluated to package the sensors. The preliminary successful demonstration of fully functional sensing cables provides the foundation for subsequent development efforts to advance the Technology Readiness Level of the technology. The technical feasibility of the proposed approach was successfully demonstrated in this Phase I effort.
43 PARTICLE ACCELERATORS↗
An archaeal genetic code with all TAG codons as pyrrolysine
Multiple genetic codes developed during the evolution of eukaryotes and bacteria, yet no alternative genetic code is known for archaea. We used proteomics to confirm our prediction that certain archaea consistently incorporate pyrrolysine (Pyl) at TAG codons, supporting an alternative archaeal genetic code that we designate the Pyl code. This genetic code has 62 sense codons encoding 21 amino acids. In contrast to monophyletic genetic code distributions in bacteria, the archaeal Pyl code occurs sporadically, indicating that it arose independently in multiple lineages. We discovered that more than 1800 archaeal proteins contain Pyl, increasing the number of such proteins by two orders of magnitude. Additionally, five Pyl transfer RNA (tRNA) pyrrolysyl–tRNA synthetase pairs from Pyl-code archaea were used to introduce Pyl analogs into proteins in Escherichia coli.
Kivenson, Veronika [University of California, Berk↗