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Embodiments of the invention relate to a novel class of polymers with superior mechanical properties and chemical stability, as compared to known polymers. These polymers are particularly well suited for use in anion exchange membranes (AEMs), including those employed in fuel cells. Novel methods for the manufacture of these polymers are also described.
Embodiments of the invention relate to a novel class of polymers with superior mechanical properties and chemical stability, as compared to known polymers. These polymers are particularly well suited for use in anion exchange membranes (AEMs), including those employed in fuel cells. Novel methods for the manufacture of these polymers are also described.
Embodiments of the invention relate to a novel class of polymers with superior mechanical properties and chemical stability, as compared to known polymers. These polymers are particularly well suited for use in anion exchange membranes (AEMs), including those employed in fuel cells. Novel methods for the manufacture of these polymers are also described.
A physically and statistically accurate method to perform binary, pairwise Coulomb collisions for arbitrarily-weighted macroparticles in particle-in-cell simulations is described. The method is shown to conserve global energy and momentum on average and to produce physically accurate scattering frequencies. This is justified using the statistically expected values of the method and through a series of numerical tests showing that the method is valid independently of macroparticle weight. This novel method is required due to an error in previous work on binary scattering, initially by Nanbu and Yonemura [J. Comp. Phys. 145, 639 (1998)] and applied to subsequent work, that causes inaccuracies in the scattering frequencies for unequally weighted simulations; a problem that occurs both for scattering within the group and between different groups.
For the next multi-megawatt accelerator generation, targets and other beam-intercepting components will face even more severe challenges due to the higher power densities, higher energy, and higher radiation. A comprehensive research and development program must be implemented to address the challenges. International collaborations such as RaDIATE or High Power Targetry Workshop should be leveraged and expanded to accelerate knowledge gain and avoid duplicated efforts. The next generation of high power targets will need novel designs allowing better high heat flux cooling methods, novel materials, advanced simulations, and better instrumentation. Four main R&D tracks were identified and discussed for targets. For electron sources, current state of art and challenges we are facing for key components such as cathodes, guns and injector design for sources R&D are presented for highly polarized electrons and unpolarized high current high brightness beams. For ion sources such as electron cyclotron resonance ion source (ECRIS), laser ion source (LIS), charge breeders, status of development are reviewed, and areas require investment for future R&D are discussed separately. Facilities critical for various source development are recommended.
To accurately compute data-based prediction of Hamiltonian systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a particularly challenging case of systems with interacting parts that do not reduce to pure momentum evolution. Such systems are essential in scientific computations, such as discretization of a continuum elastic rod, which can be viewed as the group of rotations and translations $SE(3)$. The evolution involves not only the momenta but also the relative positions and orientations of the particles. The presence of Lie group-valued elements, such as relative positions and orientations, poses a problem for applying previously derived methods for data-based computing. We develop a novel method of data-based computation and complete phase space learning of such systems. We follow the original framework of SympNets (Jin et al., 2020) and LPNets (Eldred et al., 2024), building the neural network from phase space mappings that preserve the Lie–Poisson structure. We derive a novel system of mappings that are built into neural networks describing the evolution of such systems. We call such networks Coupled Lie–Poisson Neural Networks, or CLPNets. We consider increasingly complex examples for the applications of CLPNets, starting with the rotation of two rigid bodies about a common axis, progressing to the free rotation of two rigid bodies, and finally to the evolution of two connected and interacting $SE(3)$ components, describing the discretization of an elastic rod into two elements. Our method preserves all Casimir invariants to machine precision, preserves energy to high accuracy, and shows good resistance to the curse of dimensionality, requiring only a few thousand data points for all cases studied (three to eighteen dimensions). Additionally, the method is highly economical in memory requirements, requiring only about 200 parameters for the most complex case considered.
A novel method for determining the number of independent slip systems for any family or a combination of families of slip systems is proposed, which is more direct than previous approaches. This technique makes it possible to easily determine, from the known operative slip systems, if the material is slip-system-deficient. The method also makes it possible to determine if twinning may contribute additional deformation modes.
This invention provides novel methods for treating or ameliorating symptoms of demyelinating diseases such as multiple sclerosis. The methods involve administering to subjects in need of treatment a pharmaceutical composition that contains a therapeutically effective amount of taurine and also a compound that induces oligodendrocyte precursor cell (OPC) differentiation (e.g., T3, benztropine, clemastine or miconazole). Some of the methods additionally involve administration to the subject a known agent for treating demyelinating diseases (e.g., SIP receptor agonists) or a known disease modifying drug. The invention also provides methods for increasing myelination and methods for promoting OPC differentiation into oligodendrocytes. These methods entail contacting a population of OPCs with a combination of taurine and a known OPC differentiation-inducing agent such as T3, clemastine, benztropine or miconazole.
The properties of molten semiconductors are good indicators for material structure transformation and hysteresis under temperature variations. Viscosity, as one of the most important properties, is difficult to measure because of high temperature, high pressure, and vapor toxicity of melts. Recently, a novel method was developed by applying a rotating magnetic field to the melt sealed in a suspended quartz ampoule, and measuring the transient torque exerted by rotating melt flow on the ampoule wall. The method was designed to measure viscosity in short time period, which is essential for evaluating temperature hysteresis. This paper compares the theoretical prediction of melt flow and ampoule oscillation with the experimental data. A theoretical model was established and the coupled fluid flow and ampoule torsional vibration equations were solved numerically. The simulation results showed a good agreement with experimental data. The results also showed that both electrical conductivity and viscosity could be calculated by fitting the theoretical results to the experimental data. The transient velocity of the melt caused by the rotating magnetic field was found reach equilibrium in about half a minute, and the viscosity of melt could be calculated from the altitude of oscillation. This would allow the measurement of viscosity in a minute or so, in contrast to the existing oscillation cup method, which requires about an hour for one measurement.
Platinum group metal (PGM) catalysts are the major electrocatalysts for oxygen reduction reaction (ORR) in the polymer electrolyte membrane fuel cells (PEMFCs). The cost becomes unaffordable if the PEMFC is in massive application. The PGM-Free catalyst shows very promising activity in rotation disk electrode (RDE) testing. The half-wave potential could reach 0.91 V versus standard hydrogen electrode (SHE). However, in a membrane electrode assembly (MEA), the performance of PGM-Free catalysts is not good enough to replace the PGM catalysts. Since the PGM-free catalysts are so different from the PGM catalysts in terms of catalytic activity, stability, surface conditions, particle size, etc., the fabrication of PGM-Free catalyst MEA cannot simply copy the method of making PGM MEA. Here we proposed a novel method of fabricating PGM-Free catalyst MEA, so that the intrinsic catalyst activity from RDE can be translated into MEA performance. The method is based on the catalyst coated membrane (CCM) method using optimized ionomer to carbon (I/C) ratio and solvent mixture of catalyst ink. Using this method, the PGM-free catalyst MEA achieved the current density 44.9 mA cm -2 at 0.9 V iR-free in H 2 /O 2 and 150 mA cm -2 at 0.8 V in H 2 /air, which surpassed the performance targets of US Department of Energy (DOE)for PGM-Free catalyst MEA. The property (solvent composition, dispersion of catalyst and ionomer in an ink), structure (pore structure) and the MEA performance have been characterized using, mercury intrusion porosimetry (MIP), MEA testing. A property-structure-performance relationship has been established.
Each grain is a single crystal of differing orientation. ASTM E112 is a standardized method for measuring and communicating grain size, which is suitable for equiaxed grains, but less so for columnar grains. The stretch project goal is to propose a method to measure and evaluate AM grains using a novel method.
Here, we present a novel method for simulating unsteady, variable density, fluid flows in membrane desalination systems. By assuming the density varies only with concentration and temperature, the scheme decouples the solution of the governing equations into two sequential blocks. The first solves the governing equations for the temperature and concentration fields, which are used to compute all thermophysical properties. The second block solves the conservation of mass and momentum equations for the velocity and pressure. We show that this is computationally more efficient than schemes that iterate over the full coupled equations in one block. We verify that the method achieves second-order spatial-temporal accuracy, and we use the method to investigate buoyancy-driven convection in a desalination process called vacuum membrane distillation. Specifically, we show that with gravity properly oriented, variations in temperature and concentration can trigger a double-diffusive instability that enhances mixing and improves water recovery. We also show that the instability can be strengthened by providing external heating.
In this paper we present a novel method for solving optimization problems governed by partial differential equations. Existing methods are gradient information in marching toward the minimum, where the constrained PDE is solved once (sometimes only approximately) per each optimization step. Such methods can be viewed as a marching techniques on the intersection of the state and costate hypersurfaces while improving the residuals of the design equations per each iteration. In contrast, the method presented here march on the design hypersurface and at each iteration improve the residuals of the state and costate equations. The new method is usually much less expensive per iteration step since, in most problems of practical interest, the design equation involves much less unknowns that that of either the state or costate equations. Convergence is shown using energy estimates for the evolution equations governing the iterative process. Numerical tests show that the new method allows the solution of the optimization problem in a cost of solving the analysis problems just a few times, independent of the number of design parameters. The method can be applied using single grid iterations as well as with multigrid solvers.
Dwarf galaxies are small, dark-matter-dominated galaxies, some of which are embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and gas) makes them perfect test beds for probing the properties of dark matter—understanding the spatial dark matter distribution in these systems can be used to constrain microphysical dark matter interactions that influence the formation and evolution of structures in our Universe. Here, we introduce a new method that leverages simulation-based inference and graph-based machine learning in order to infer the dark matter density profiles of dwarf galaxies from observable kinematics of stars gravitationally bound to these systems. Our approach aims to address some of the limitations of established methods based on dynamical Jeans modeling. We show that this novel method can place stronger constraints on dark matter profiles and, consequently, has the potential to weigh in on some of the ongoing puzzles associated with the small-scale structure of dark matter halos, such as the core-cusp discrepancy.
Current practices and future requirements for the storage, transmission and distribution of hydrogen are reviewed in order to identify inadequacies to be corrected before hydrogen can achieve its full potential as a substitute for fossil fuels. Consideration is given to the storage of hydrogen in underground solution-mined salt caverns, portable high-pressure containers and dewars, pressure vessels and aquifers and as metal hydrides, hydrogen transmission in evacuated double-walled insulated containers and by pipeline, and distribution by truck and internal distribution networks. Areas for the improvement of these techniques are indicated, and these technological deficiencies, including materials development, low-cost storage and transmission methods, low-cost, long-life metal hydrides and novel methods for hydrogen storage, are presented as challenges for research and development.
Some astronauts have suffered degradation of vision during long-duration space flight, suffering from a condition that has come to be known as Spaceflight Associated Neuro-ocular Syndrome (SANS). While related morphological changes can be observed with imaging technologies such as optical coherence tomography (OCT), it may be useful to have a rapid method for functional vision assessment. In this paper, we compare three tablet-based methods for rapid assessment of contrast sensitivity. First, a relatively novel method developed expressly for touch screens, in which the subject ”swipes” a frequency/contrast sweep grating to indicate the boundary between visible and invisible patterns; second, a method-of-adjustment task in which the subject adjusts the contrast of a grating patch up and down to bracket the visual threshold; and third, a traditional temporal two-alternative forced choice (2AFC) task, in which the subject is presented with a near-threshold stimulus in one of two intervals, and must report the interval containing the stimulus. The swipe method shows variability comparable to the 2AFC method, and shows good agreement in estimates of the spatial frequency of peak sensitivity. The absolute sensitivity estimated with the swipe method is higher than that of the other methods, perhaps because subjects are biased to trace outside of the visible pattern region, or perhaps due to stimulus differences.
We present ionization as a novel method for improving perovskite solar cells performance. The ionic HTMs give higher efficiency than the non-ionic derivative due to improved interfacial properties, defect passivation, and ideal surface topographies.
Graph-based representations of building metadata using ontologies like Brick are vital for smart building applications, but querying them remains a challenge for practitioners. Knowledge Graph Question Answering (KGQA) systems, meant to retrieve answers from natural language questions, traditionally require large-scale training data, making them ill-suited for the specialized and data-scarce building domain. The advent of Large Language Models (LLMs) offers a paradigm shift, enabling zero-shot natural language querying without building/domain-specific training. Yet, there is no standardized benchmark for building-specific KGQA which can guide and validate research in this area. To address this gap, our work makes three primary contributions. First, we introduce the BuildingQA Benchmark Dataset, constructed through a multi-stage process of collecting practitioner data, augmenting it with LLMs for linguistic diversity, and curating a final set of 188 questions across 4 buildings. Second, we characterize the benchmark's complexity and ambiguity, introducing a novel method to quantify its "lexical gap" and providing a four-stage diagnostic framework for analyzing how systems fail. Third, we benchmark zero-shot LLM-powered KGQA systems to establish baseline performance and analyze their failure modes. Our evaluation reveals that top-performing systems achieve a maximum F1 score of only 0.38. This result does not indicate a failure of these powerful systems, but rather underscores the unique challenges posed by our benchmark. It demonstrates a critical performance gap, showing that current methods successful on general KGs struggle with the specific lexical and structural nuances of the building domain. BuildingQA1 thus provides the benchmark dataset and foundational analysis needed to drive the development of novel, domain-aware methods required to unlock the use of semantic data in buildings.