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Strong gravitationally lensed quasars provide powerful means to study galaxy evolution and cosmology. Current and upcoming imaging surveys will contain thousands of new lensed quasars, augmenting the existing sample by at least two orders of magnitude. To find such lens systems, we built a robot, Chitah, that hunts for lensed quasars by modeling the configuration of the multiple quasar images.
Higher throughput methods to mount and collect data from multiple small and radiation-sensitive crystals are important to support challenging structural investigations using microfocus synchrotron beamlines. Furthermore, efficient sample-delivery methods are essential to carry out productive femtosecond crystallography experiments at X-ray free-electron laser (XFEL) sources such as the Linac Coherent Light Source (LCLS). To address these needs, a high-density sample grid useful as a scaffold for both crystal growth and diffraction data collection has been developed and utilized for efficient goniometer-based sample delivery at synchrotron and XFEL sources. A single grid contains 75 mounting ports and fits inside an SSRL cassette or uni-puck storage container. The use of grids with an SSRL cassette expands the cassette capacity up to 7200 samples. Grids may also be covered with a polymer film or sleeve for efficient room-temperature data collection from multiple samples. New automated routines have been incorporated into the Blu-Ice / DCSS experimental control system to support grids, including semi-automated grid alignment, fully automated positioning of grid ports, rastering and automated data collection. Specialized tools have been developed to support crystallization experiments on grids, including a universal adaptor, which allows grids to be filled by commercial liquid-handling robots, as well as incubation chambers, which support vapor-diffusion and lipidic cubic phase crystallization experiments. Experiments in which crystals were loaded into grids or grown on grids using liquid-handling robots and incubation chambers are described. As a result, crystals were screened at LCLS-XPP and SSRL BL12-2 at room temperature and cryogenic temperatures.
Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. Here, this work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.
Periodic actuation of multiple soft, pneumatic actuators requires coordinated function of multiple, separate components. This work demonstrates a soft, pneumatic ring oscillator that induces temporally coordinated periodic motion in soft actuators using a single, constant-pressure source, without hard valves or electronic controls. The fundamental unit of this ring oscillator is a soft, pneumatic inverter (an inverting Schmitt trigger) that switches between its two states (“on” and “off”) using two instabilities in elastomeric structures: buckling of internal tubing and snap-through of a hemispherical membrane. An odd number of these inverters connected in a loop produces the same number of periodically oscillating outputs, resulting from a third, system-level instability; the frequency of oscillation depends on three system parameters that can be adjusted. These oscillatory output pressures enable several applications, including undulating and rolling motions in soft robots, size-based particle separation, pneumatic mechanotherapy, and metering of fluids. The soft ring oscillator eliminates the need for hard valves and electronic controls in these applications.
Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a “one-pot” process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.
Since the beginning of commercial nuclear power generation in the 1960s, the ability of researchers to understand and control the isotopic content of spent fuel has improved. It is therefore not surprising that both fuel assembly design and fuel assembly irradiation optimization have improved over the past 50+ years. It is anticipated that the burnup and isotopics of the spent fuel should exhibit less variation over the decades as reactor operators irradiate each assembly to the optimum amount. In contrast, older spent fuel is anticipated to vary more in burnup and resulting isotopics for a given initial enrichment. Modern fuel therefore should be more uniform in composition, and thus, measured safeguards results should be easier to interpret than results from older spent fuel. With spent fuel ponds filling up, interim and long-term storage of spent fuel will need to be addressed. Additionally after long periods of storage, spent fuel is no longer self-protecting and, as such, the IAEA will categorize it as more attractive; in approximately 20 years many of the assemblies from early commercial cores will no longer be considered self-protecting. This study will assess how more recent changes in the reactor operation could impact the interpretation of safeguards measurements. The status quo for spent fuel assay in the safeguards context is that the overwhelming majority of spent fuel assemblies are not measured in a quantitative way except for those assemblies about to be loaded into a difficult or impossible to access location (dry storage or, in the future, a repository). In other words, when the assembly is still accessible to a state actor, or an insider, when it is cooling in a pool, the inspectorate does not have a measurement database that could assist them in re-verifying the integrity of that assembly. The spent fuel safeguards regime would be strengthened if spent fuel assemblies were measured from discharge to loading into a difficult or impossible to access location. The primary driver for suggesting this shift in approach is the change in robotic technology and information technology in general. It should be possible, with minimal impact to the facility, to measure each assembly every time that it is moved in the pool, with the first measurements being made at discharge. The following conclusions were reached: The total neutron count rate can be accurately predicted at any future moment in time based upon the measured count rate at discharge, provided the initial enrichment and burnup of the assembly is known at discharge. It is expected that the total neutron count rate measured at discharge will be indicative of the initial enrichment and burnup of that assembly. If the automated robot were to focus on measuring the assemblies in the rack without moving them, the time available would increase immensely.
This presentation provides a high level summary of current research activities at the Los Alamos National Laboratory (LANL)-University of California Jacobs School of Engineering (UCSD) Engineering Institute that will be presented at Korea's Agency for Defense Development (ADD). These research activities are at the basic engineering science level with different level of maturity ranging from initial concepts to field proof-of-concept demonstrations. We believe that all of these activities are appropriate for collaborative research activities with ADD subject to approval by each institution. All the activities summarized herein have the common theme that they are multi-disciplinary in nature and typically involved the integration of high-fidelity predictive modeling, advanced sensing technologies and new development in information technology. These activities include: Wireless Sensor Systems, Swarming Robot sensor systems, Advanced signal processing (compressed sensing) and pattern recognition, Model Verification and Validation, Optimal/robust sensor system design, Haptic systems for large-scale data processing, Cyber-physical security for robots, Multi-source energy harvesting, Reliability-based approaches to damage prognosis, SHMTools software development, and Cyber-physical systems advanced study institute.
This 56-second time-lapse video shows three different camera views of assembly work for the Dark Energy Spectroscopic Instrument’s Focal Plane. First, it shows the process of joining separate lengths of fiber-optic cables, known as fusion splicing. More than 5,000 fiber-optic cables, each measuring over 50 meters, will extend from near the top of the Mayall Telescope down to devices known as spectrographs that will be used to measure the properties of light from millions of distant galaxies. The Mayall Telescope is at Kitt Peak National Observatory near Tucson, Arizona. The middle portion of the video shows another view of the focal plane assembly area, and the final camera view shows the installation of robotic positioners into a wedge-shaped “petal.” The focal plan will be composed of 10 of these petals, each carrying 500 robotic positioners. (Credit: Berkeley Lab)
DESI, the Dark Energy Spectroscopic Instrument, will mobilize 5,000 swiveling robots – each one pointing a thin strand of fiber-optic cable – to gather the light from about 35 million galaxies. The little robots are designed to fix on a series of preselected sky objects that are as distant as 12 billion light-years away. By studying how these galaxies are drifting away from us, DESI will provide precise measurements of the accelerating rate at which the universe is expanding. This expansion rate is caused by an invisible force known as dark energy, which is one of the biggest mysteries in astrophysics and accounts for about 68 percent of all mass and energy in the universe. In this video, DESI project participants share their insight and excitement about the project and its potential for new and unexpected discoveries. Dark Energy Instrument’s Lenses See the Night Sky for the First Time http://bit.ly/DESIlight The Making of the Largest 3D Map of the Universe http://bit.ly/3Duniverse
The maintenance of the prefilters of the condensate demineralizers in a BWR reactor nuclear plant, could represents a real problem given the substances (radioactive particles) captured by the filtering elements (called 'candles'). This trapped material can produce high doses on the personnel participating in this maintenance work. Thus, it is desirable to automate the maintenance activities required to remove the used filtering elements. Then we designed a robotic manipulator capable of detaching, one by one, the filtering elements from their fastening structure. The manipulator deposits the 'candles' into a container as they are detached, and at the end, it is automatically transported, to a storage area. The work here presented, proposes the application of the 'visual servoing control' technique to automate the operation of the robotic arm. This would minimize the intervention of human operators, reducing maintenance times and radiation dose to the personnel. The methodology is based on techniques of artificial vision and uses, in addition to a video camera, a device capable of generating a laser line, which when projected onto 3D space, creates what is known as a laser plane. This plane upon striking the candles will generate circumferential arcs in the video camera images, and these figures will be used to detect the 3D point that will guide the manipulator to take the filtering element. (authors)
The U.S. Department of Energy (DOE) and the Hanford Site Tank Operations Contractor Washington River Protection Solutions, LLC (WRPS) are sponsoring a non-destructive evaluation (NDE) technology development program to identify and mature volumetric (non-visual) NDE technology that will enable the examination of Hanford double-shell tank (DST) bottoms. NDE technology for Hanford under-tank examination will be made possible through technology maturation that includes adaptation of NDE sensor technology to overcome access challenges presented by tank risers and refractory pad air-slots, followed by integration with robotic delivery systems, and ultimately qualification and deployment of the inspection systems. The stages of technology maturation and the testing and evaluation required to support the process are organized under three program phases: Phase I - NDE Capability Testing: Identify and down-select NDE technologies based on flaw detection and characterization abilities only; Phase 2 - NDE Delivery System Testing: Mature promising NDE technologies to adapt transducer hardware and robotic delivery system to address access challenges; Phase 3 - Full-scale Demonstration of Integrated NDE System: Demonstrate adapted NDE technologies in a cold test platform to challenge flaw detection and navigation abilities. Phase I of the program, which was initiated and completed in FY2017, focused on completing the following five key activities: 1. Identifying a broad cross section of potentially applicable NDE volumetric inspection technologies for under-tank examination. 2. Down-selecting the broad set of NDE technologies to a set of strong candidates based on initial flaw detection performance and potential to be adapted/matured to overcome primary tank access challenges posed by access risers and/or refractory pad air-slots. 3. Evaluating the flaw detection performance of the candidate sensors through testing. 4. Using the test results to baseline sensor performance against the flaw detection requirements established for high-level waste tanks. 5. Assessing the trade-offs and vulnerabilities of the candidate sensors that would negatively impact sensor performance reliability or complicate tank inspection operations. The completion of the first three key activities satisfied the first two Phase I test objectives, which were to complete Technology Screening and Sensor Effectiveness Testing. Completion of the fourth key activity satisfied the final test objective of Phase I, which was to determine the extent to which the sensors can satisfy high-level waste tank flaw detection requirements. Sensor flaw detection performance testing yielded several suitable sensor technology options, although each has specific risks or challenges that would need to be accepted or overcome. Thus, the fifth key activity was performed to evaluate sensor risks and challenges for consideration with flaw detection performance. The Phase I evaluation outcomes will be used to support the Phase I programmatic objective, which is to identify one or more NDE sensor technologies for adaptation and maturation for the DST environment. Selection of one or more sensors will mark the culmination of Phase I of the NDE Technology Development Program and allow progression to Phase II.
Over the summer of 2018, the Savannah River National Laboratory (SRNL) formed and hosted a robotics and sensor intern team and tasked them with developing a proof of concept for a modular sensor suite to be deployed in the next H-Canyon Air Exhaust (HCAEX) Tunnel inspection in spring 2019. The developed sensor suite includes a planar Lidar sensor for depth imaging, two Panospheric 4K cameras, and an inertial measurement unit to aid in base localization. The Lidar is actuated to rotate about the vertical axis, which allows for the team to gather 3D mapping data. These rotating planar scans can also be fused with the camera and IMU data to generate colorized internal maps of the tunnel. The supporting software was developed iteratively over the summer and leveraged prior research conducted at the University of Texas at Austin, which facilitated the stitching of Lidar scans, the fusion of the sensor data, and even algorithms to provide in-situ navigation feedback to the operators of the crawler. This software simultaneously represents huge leaps forward in the mapping and degradation measurement abilities of the crawler over past inspection units, while also allowing the operator much greater situational awareness. The improvement in awareness reduces risks of the robot becoming stuck or disabled inside the tunnel, which would jeopardize the success of both this and future inspections. (authors)
We estimate the snow albedo forcing and direct radiative forcing (DRF) of black carbon (BC) in the Tibetan Plateau using a global chemical transport model in conjunction with a stochastic snow model and a radiative transfer model. Our best estimate of the annual BC snow albedo forcing in the Plateau is 2.9 W m-2 (uncertainty: 1.5–5.0 W m-226 ). We find that BC-snow internal mixing increases the albedo forcing by 40-60% compared with external mixing and coated BC increases the forcing by 30-50% compared with uncoated BC, whereas Koch snowflakes reduce the forcing by 20-40% relative to spherical snow grains. Our best estimate of the annual BC DRF at the top of the atmosphere is 2.3 W m-2 (uncertainty: 0.7–4.3 W m-230 ) in the Plateau after scaling the modeled BC absorption optical depth to Aerosol Robotic Network (AERONET) observations. The BC forcings are attributed to emissions from different regions.
Abstract The design of robotic soft matter capable of emulating the complex movements of living organisms such as mechanical actuation, shape transformation, and autonomous translation remains a grand challenge in soft materials science. Functionalized hydrogels are excellent candidates for such materials since they can operate in water and are highly responsive to their environment, but their response times can be slow. This work investigates fast photoactuation of hybrid bonding hydrogels composed of peptide amphiphile (PA) supramolecular nanofibers bonded covalently to merocyanine‐based (MCH + ) photoresponsive networks. By incorporating ionizable acrylic acid (AA) co‐monomers in these networks, photoactuation at nearly neutral pH is observed, which in turn enables a new mechanism to accelerate the response by triggering the bundling of supramolecular nanofibers by rapid proton exchange reactions. Furthermore, this rapid response and its consequent large shape transformations lead to hydrogels capable of spontaneously tracking external light sources inspired by pedicellariae, defensive organs present in echinoderms like the starfish and the sea urchin. This work suggests that hybrid bonding polymers (HBPs), which leverage the interplay between supramolecular assemblies and covalent networks, offer novel strategies to design rapidly actuating soft robotic materials.
Hydrogels, exhibiting wide applications in soft robotics, tissue engineering, implantable electronics, etc., often require sophisticately tailoring of the hydrogel mechanical properties to meet specific demands. For examples, soft robotics necessitates tough hydrogels; stem cell culturing demands various tissue-matching modulus; and neuron probes desire dynamically tunable modulus. Herein, a strategy to broadly alter the mechanical properties of hydrogels reversibly via tuning the aggregation states of the polymer chains by ions based on the Hofmeister effect is reported. An ultratough poly(vinyl alcohol) (PVA) hydrogel as an exemplary material (toughness 150 ± 20 MJ m -3 ), which surpasses synthetic polymers like poly(dimethylsiloxane), synthetic rubber, and natural spider silk is fabricated. With various ions, the hydrogel's various mechanical properties are continuously and reversibly in situ modulated over a large window: tensile strength from 50 ± 9 kPa to 15 ± 1 MPa, toughness from 0.0167 ± 0.003 to 150 ± 20 MJ m -3 , elongation from 300 ± 100% to 2100 ± 300%, and modulus from 24 ± 2 to 2500 ± 140 kPa. Importantly, the ions serve as gelation triggers and property modulators only, not necessarily required to remain in the gel, maintaining the high biocompatibility of PVA without excess ions. Overall, this strategy, enabling high mechanical performance and broad dynamic tunability, presents a universal platform for broad applications from biomedicine to wearable electronics.
Soft materials that exhibit compliance, programmability, and reconfigurability can have a transformative impact as electronic skin for applications in wearable electronics/soft robotics. There has been significant progress in soft conductive materials; however, achieving electrically controlled and reversible changes in conductivity and circuit connectivity remains challenging. To overcome this limitation, a soft material architecture with reconfigurable conductive networks of silver flakes embedded within a hydrogel matrix is presented. The conductive networks can be reversibly created/disconnected through various stimuli, including current, humidity, or temperature. Such stimuli affect electrical connectivity of the hydrogel by controlling its water content, which can be modulated by evaporation under ambient conditions (passive dehydration), evaporation through electrical Joule heating (active dehydration), or absorption of additional water (rehydration). The resulting change in electrical conductivity is reversible and repeatable, endowing the composite with on-demand reconfigurable conductivity. To highlight this material's unique properties, it is shown that conductive traces can be reconfigured after severe damage and revert to lower conductivity after rehydration. Additionally, a quadruped robot is demonstrated that can respond to stimuli by changing direction following exposure to excess water, thereby achieving reprogrammable locomotion behaviors.
Access to multimaterial polymers with spatially localized properties and robust interfaces is anticipated to enable new capabilities in soft robotics, such as smooth actuation for advanced medical and manufacturing technologies. Here, orthogonal initiation is used to create interpenetrating polymer networks (IPNs) with spatial control over morphology and mechanical properties. Base catalyzes the formation of a stiff and strong polyurethane, while blue LEDs initiate the formation of a soft and elastic polyacrylate. IPN morphology is controlled by when the LED is turned “on”, with large phase separation occurring for short time delays (≈1–2 min) and a mixed morphology for longer time delays (>5 min), which is supported by dynamic mechanical analysis, small angle X-ray scattering, and atomic force microscopy. Through tailoring morphology, tensile moduli and fracture toughness can be tuned across ≈1–2 orders of magnitude. Furthermore, a simple spring model is used to explain the observed mechanical behavior. Photopatterning produces “multimorphic” materials, where morphology is spatially localized with fine precision (<100 µm), while maintaining a uniform chemical composition throughout to mitigate interfacial failure. As a final demonstration, the fabrication of hinges represents a possible use case for multimorphic materials in soft robotics.