Lab Partnering Service Discovery
Use the LPS faceted search filters, or search by keywords, to narrow your results.
Computers and automated systems have accelerated productivity and improved quality and reliability for nearly everything in our modern world, and are destined to take on increasing roles as time moves on. One major limiting factor for automated systems is their inability to categorize and recognize objects, particularly under changing lighting or other conditions. Examples of how this could be useful include automatically detecting manufacturing defects, analyzing changes between two images (e.g. medical scans), noise filtering in radio frequency communications, and extracting weak signals or images from various sources.
Current approaches to making “smart” systems generally build custom solutions for every problem with very specific outcomes. Examples include self-driving car systems and facial recognition software, which have very specific features and approaches built in that usually do not translate to other applications very well. Other examples, such as automatic defect detection, require tightly controlled lighting and often require the object being inspected to be in the same position to be able to identify problems. Generally, these automated systems can, when conditions match the programmed expectations, identify that there is a problem, but have very limited ability when measurement conditions are dynamic or the situation changes in unanticipated ways.
Researchers at INL have developed an analysis system known as MorphoHawk for automatic feature detection and classification across a host of applications in changing environmental conditions. In general, MorphoHawk can be trained to identify features of interest, and will then group features in a scene (e.g. image, signal, etc) and categorize them according to the rules it was conditioned with. After it has categorized an image or other multi-dimentional data set, it can compare the features it has identified with subsequent data sets, allowing it to detect changes (e.g. manufacturing quality control) or detect the introduction of new features (e.g. a tumor in medical scans or a person entering a scene monitored by a camera). It has shown that it can discern between an object and its shadow, meaning it can handle differences in registration and light conditions in dynamic environments. This is possible because MorphoHawk algorithms characterize and compare morphological features, rather than conducting a binary analysis (e.g. light vs. dark).
MorphoHawk has shown utility as a signal filtering tool to differentiate between noise and meaningful data in analysis of digital images and electronic signals, resulting in sharp, cleaned images and clearly extracting the message of the signals while removing the noise. MorphoHawk can be applied to analyze images for manufacturing defects, enhancing the capability of existing inspection systems. Feature extraction is another unique capability of MorphoHawk. For example, metal surface topology can be separated into effects of rolling and grinding, allowing discrepancies to be assigned to the appropriate process. It has even been used to identify a facture path in materials and examine structural changes in battery electrodes to predict battery lifetime.INL Technology ID: BA-481
ISU and Ames Laboratory scientists have developed a metal chalcogenide material for use as a water electrolysis catalyst for the generation of hydrogen.
Hydrogen is a unique energy carrier in that it can be produced from a number of diverse pathways utilizing a variety of domestically available feedstock, including natural gas, biomass, and water. The electrochemical splitting of water (electrolysis) is among the most versatile and greenest methods of hydrogen generation that will play a significant role in long-term, high-volume hydrogen gas production. Iowa State University and Ames Laboratory scientists have developed a catalyst to assist in the generation of hydrogen from water electrolysis. The mixed-metal chalcogenide catalyst shows promise as a cathode material, able to operate in highly acidic conditions. When compared to other non-precious metal catalysts, such as Molybdenum Sulfide, these catalysts offer far superior performance, able to operate far more efficiently. http://isurftech.technologypublisher.com/technology/31310 This technology is related to ISURF 4629: Preparation of mixed metal chalcogenides by mechanochemical processing and exfoliation https://isurftech.technologypublisher.com/techcase/4629
Computer engineers have developed a new design to support construction of large computer systems that perform closer to their theoretical peak. This approach emphasizes scalable throughput rather than attempting to tailor systems around the highest performing accelerators, and allows selection of individual components that maximize performance against energy draw or cost. The design makes use of commodity components that are modest in computing power and energy consumption.
Most supercomputer applications require some non-local communication. As a result, the relatively high-latency and low-bandwidth interconnection network becomes a limiting factor on the machine’s efficiency. In addition, designers are extending the peak performance of supercomputers by adding multi-core accelerators such as Cell processors or Graphics Processing Units (GPUs). This introduces another high-latency and low-bandwidth bottleneck, at the point where data moves into and out of the accelerator, as well as another dimension of complexity in software.
These factors limit the kinds of applications that can run effectively on supercomputers, and increase the cost of developing or porting those applications. Algorithms that require intercommunication result in underutilized components, wasting energy and the potential of the machine. Furthermore, there appear to be some problems which perform poorly on these architectures, regardless of optimization.
Los Alamos National Laboratory (LANL) researchers have developed a new design to support construction of large machines, allowing the machines to perform closer to their theoretical peak. This approach emphasizes scalable throughput rather than attempting to tailor machines around the highest performing accelerators, and allows selection of individual components that maximize performance against energy draw or cost. The design makes use of commodity components that are modest in computing power and energy consumption.
The LANL hardware is being co-designed along with a powerful and expressive high-level programming language, adapted from a well-studied body of research languages. It is expected that applications written in this language will require no other system-level or low-level programming in order to run efficiently, but diagnostic feedback could allow selection of more efficient idioms.
LANL’s design supports the data-intensive applications currently encountered in scientific computing, while opening the door to new levels of capability for communication-intensive and throughput-intensive applications such as molecular dynamics and signal correlation. In addition, researchers expect the LANL design can support transparent fail-over, allowing failed nodes to be replaced on-the-fly without stopping ongoing computations.
Sandia’s neutron scatter camera is an innovative design which combines the benefits of gamma ray imaging with fast neutron imaging. The camera detects special nuclear material (SNM) and rejects backgrounds from naturally occurring radiation sources that can produce false alarms. Additionally, the camera can detect and localize neutrons at greater distances and through shielding since fast neutrons are more penetrating than gamma rays. One of the key advantages is higher signal to background over non imaging detectors.
Sandia’s neutron camera design is sensitive, has good angular resolution, portable, and non hazardous. The design is scalable for shorter dwell times and longer stand-off detection.
Iowa State University and Ames Laboratory researchers have developed a method to create gadolinium silicide nanoparticles which retain ferromagnetic properties at room temperature.
This innovative method creates Gd5Si4 nanoparticles that retain the ferromagnetic properties of the bulk material at room temperature. These nanoparticles may be useful as a MRI contrast agent or for other applications that would benefit from materials that highly respond to a magnetic field, such as transcranial magnetic stimulation, MRI thermometry, and hyperthermic cancer treatment. The gadolinium-based ferromagnetic particles are produced using ball milling in an inert atmosphere. The resultant particles retain an order of magnitude greater magnetization compared to conventionally prepared gadolinium particles. Ordinary preparation methods destroy the ordered structure required for ferromagnetism, resulting in materials with the much weaker paramagnetic properties - ferromagnetic materials have a high susceptibility to magnetization when subjected to a magnetic field and retain that magnetization after the field is removed; paramagnetic materials respond to a magnetic field but do not retain any magnetization when removed from the field.
Iowa State University and Ames Laboratory researchers have developed an improvement to KBBF crystal systems for the generation of ultraviolet laser light by creating an alternative prism geometry that eliminates the need for contacting fluid or optical coupling devices.
Lasers consisting of light from the ultra-violet portion of the spectrum have both scientific and commercial applications. Scientifically, vacuum ultraviolet (VUV) lasers can be used in angle resolved photoemission spectroscopy to study the electronic parameters of solids. Commercially VUV lasers are of interest in semiconductor manufacturing, as the wavelength of the higher frequency spectra could produce much finer structures using photolithography. One source for generation of VUV lasers is passing a lower frequency laser beam through potassium beryllium fluoroborate (KBBF) crystals, resulting in a harmonic frequency laser. For economic reasons, KBBF crystals are grown very thin; as incident light upon the crystals is at a very acute angle, the resultant VUV laser has a low efficiency as most of the light is subsequently reflected off the surface of the crystal.
Iowa State University and Ames Laboratory researchers have developed a modular sample stage and thermal conductivity measurement device that is compatible with a variety of cryogenic and magnetic field apparatus. This modular device allows for easy switching between apparatus to perform a variety of measurements without sample or thermometer remounting.
The thermal conductivity of a material is of great importance for determining suitability for a given application. While many techniques have been developed to measure thermal conductivity at moderate temperatures, measurement at low (sub-kelvin) temperatures are difficult to achieve. These low temperature measurements are important to characterize novel materials, particularly in determining the superconducting state while isolating electronic degrees of freedom. As there is no singular cyrogenic solution for measurement of thermal conductivity that can cover broad ranges of temperature, magnetic field strength, and magnetic field direction, thorough characterization requires the sample to be tested in multiple apparatus. A modular and portable sample stage and conductivity measurement device that can be readily moved between apparatus, and is compatible with broad temperature and magnetic field ranges, is desirable to reduce the error introduced by multiple setups as well as different thermometers and calibrations.
Iowa State University and Ames Laboratory researchers have developed a fast solver for the Gutzwiller approximation for electronic structure of atoms.
State of the art computational tools for atomic modeling use the Local Density Approximation Density Functional Theory (LDADFT).However, LDADFT often has issues in properly describing situations which include van der Waals forces, charge transfer and transition states. Simultaneously optimizing the three sets of parameters in the Gutzwiller approximation can address some of these specific situations and produce a more accurate model. ISURF #03958 provides a solver for the Gutzwiller approximation from first principles. ISURF #04135 takes an alternative approach, starting with a set of common parameters for optimization rather than starting from first principles. For the majority of applications, ISURF #04135 produces as an accurate model as does ISURF #03958 but in a much faster computation. This technology is related to ISURF 4135: A General Efficient Gutzwiller Solver for Electronic Structure Simulation Package (software: http://isurftech.technologypublisher.com/techcase/4135).
Microgrids are localized energy grids that provide flexibility through their ability to operate independently from the bulk power grid. Well-designed microgrids support resiliency, security, efficiency, local control, and increased access to renewable resources. Sandia’s Microgrid Design Toolkit (MDT) is a decision support software toolkit that aids designers in creating optimal microgrids.
Employing powerful algorithms and simulation capabilities, MDT searches the trade space of alternative microgrid designs based on user-defined objectives (e.g., cost, performance, and reliability) and produces a set of efficient microgrid solutions. MDT allows designers to investigate the simultaneous impacts of several design decisions and gain a quantitative understanding of the relationships between design objectives and trade-offs associated with alternative technological design decisions. MDT can account for grid-connected and islanded performance, power and component reliability in islanded mode, and dozens of parameters as part of the trade space search, and presents designers with an entire trade space of information from which to base final design decisions. Without MDT, designers rely on engineering judgment and perhaps a quantitative analysis of relatively few candidate designs. MDT allows designers to explore a larger field of options and provides defensible, quantitative evidence for design decisions.
For years, radio frequency identification (RFID) technology has been used in a variety of applications, from passports to inventory tracking in retail environments. Homeland security concerns have heightened the need for sensitive, real-time tracking of thousands of radioactive and hazardous material packages to ensure accountability, safety, security, and worker and public health. Through the support of DOE, Argonne researchers have developed and tested a patented RFID tracking and monitoring technology called ARG-US (which means “watchful guardian”) that will modernize the management of nuclear and
The heart of the ARG-US system is a battery-powered RFID tag that remotely monitors the vital parameters of packages containing sensitive materials in storage and transportation. The ARG-US RFID tag incorporates a suite of sensors for seal integrity, temperature, humidity, shock, radiation and battery strength. New sensors can be added via the tag’s built-in expansion interface. As designed and developed, ARG-US can add an extra layer of security, functionality and savings to the handling, storage and transport of nuclear and radioactive materials and other sensitive items.
The ARG-US system provides continuous, near-real-time tracking and monitoring of packages during transport, in-transit stops and storage by using multi-functional RFID tags attached to each package, in conjunction with RFID readers, control computers, stand-alone and web-based software, and satellite or cellular-based channels. Two specialized software applications—ARG-US TransPort and ARG-US OnSite—have been developed, providing a powerful, customizable platform for full life-cycle materials management during transport and storage. The system incorporates secure communications, databases, and web services. Together, these features can dramatically increase efficiency while reducing costs associated with nuclear material operations and aging management.
The need for ARG-US technology has grown beyond its initial application of managing nuclear materials as a result of demonstrated performance, and now includes its application to civilian fuel cycles, incident responses, and emergency management.
In April 2011, ARG?US was chosen by an industry panel to receive RFID Journal’s prestigious “Most Innovative Use of RFID” Award. ARG?US was also selected as a finalist to present at the 2011 World’s Best Technology Innovation Marketplace, a preeminent technology forum. In February 2012, the system was featured in a case study in the U.S. for the World Institute for Nuclear Security and the World Nuclear Transport Institute Joint International Best Practice Guide on Electronic Tracking for the Transport of Nuclear and Other Radioactive Materials, Revision 1.0.
Scientists at Argonne National Laboratory have created an in vitro, cell-free system and method for producing several types of protein: membrane proteins, membrane-associated proteins, and soluble proteins.
With advances that can be gleaned from the study of high quality samples of this type, this method is expected to drive advances in membrane protein structural biology and deepen our approaches for characterizing biological activity as cellular interfaces.
In most organisms, cell membranes are the vital structures that serve as the interface between an organism and its environment, enabling the creation of compartments where proteins carry out the cell’s basic functions. Proteins in these membranes carry out the essential functions of the cell, such as uptake of nutrients, excretion of wastes, energy generation, and signal transduction. The functions performed by membrane proteins are extremely important for all organisms. Previously, researchers studying these proteins needed to replicate them within cells: a complex, time-consuming process.
Despite the fact that they represent approximately 30% of every genome and comprise more than 60% of all drug targets, only about 100 unique membrane protein structures have been determined to date (compared to about 10,000 unique structures in soluble protein families). One reason for this relatively low number of unique membrane protein structures is that it is difficult to isolate membrane proteins using conventional methods. Also, once isolated, purification is highly protein-specific, is not adaptable to high-throughput methodologies, and rarely yields the amounts of pure membrane proteins needed for extensive biochemical studies and crystallization trials.
The in vitro method is capable of producing membrane proteins, membrane-associated proteins, and soluble proteins. This methodology promises to become an important tool for deepening scientists’ understanding of life and driving advances in molecular biology.
The success of modern industries— especially those that are electricity-intensive—depends on complex engineering systems to ensure safe, productive and efficient operations. System breakdowns can result in millions of dollars in lost time and productivity—and even the loss of life and property. For example, in the utilities industry—where the continuous operation of coolant pumps is essential—the breakdown of a single pump can result in a loss of as much as $10 million in downtime.
Scientists at Argonne National Laboratory devised a unique early-warning system, called the Multivariate State Estimation Technique (MSET), that monitors the performance of sensors, equipment and plant processes in an industrial environment. A highly sensitive, highly accurate tool, MSET monitors the operation of any process that uses multiple sensors, detecting and alerting users of potential
MSET, the winner of a 1998 R&D 100 Award, consists of a unique, patented suite of statistically based pattern recognition modules. It detects and identifies malfunctions that may occur in process sensors, components or control systems; or changes in process operating conditions. The MSET modules interact to provide users with the information needed for the safe, reliable and economical operation of a process by detecting, locating and identifying very subtle changes that could lead to future problems well in advance of actual equipment degradation.
Since it provides continuous calibration validation for all sensors, MSET offers a technical basis for reducing burdensome instrument calibration requirements. It can also help users determine when it is appropriate to continue or extend operation of certain components, or to schedule corrective actions, such as sensor replacement or re-calibration, component adjustment.
MSET uses an ultra-sensitive Sequential Probability Ratio Test (SPRT, which was also developed and patented by the MSET inventors) to discern sensor or system anomalies at the earliest possible time. MSET’s unique capabilities make
it better than conventional approaches—including neural networks—in sensitivity, reliability and computational efficiency.
To use MSET, the user first collects sensor readings (via a digital acquisition system) to characterize the normal operating state of the system. MSET automatically selects an optimal subset of these data and uses it to "train" the system to recognize normal behavior. During monitoring, MSET generates an accurate estimate of what each signal should be based on the latest set of sensor readings and the previously learned correlations among them. Then, SPRT analyzes the difference between this state estimate and the measurement, and quickly detects and alerts the smallest developing faults. If an abnormal condition is detected, the initial diagnostic step identifies the cause as either a sensor degradation or an operational change in the process. When a sensor fault is identified, MSET uses the estimated value of the signal to provide an extremely precise "virtual sensor" that can be used to fully replace the function of the faulted sensor.