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At least 1,045 records · Page 58

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗

Scanning Probe Microscopy in the Information Age

Scanning probe microscopy (SPM) has brought scientific exploration, visualization, and control to nanoscale. A plethora of measurement approaches and techniques devised around SPM enabled probing structure and functionalities crucial for virtually all areas of science. While high-resolution electron microscopy techniques preceded scanning probes, SPM effectively opened the door to probing the structure–property relationship on length scales spanning 100s of micrometers down to a single atom. As the complexity of the material systems and the scientific questions increased, so too did the SPM techniques. Substantial efforts have been dedicated toward the development of better instrumentation platforms, probes, and novel spectroscopic measurements; however, many opportunities related to data processing and acquisition in SPM are just being realized now. This chapter will describe the recent progress in data-driven SPM by giving a broad overview of the field, the foundation of the scanning tunneling microscope (STM) and the atomic force microscope (AFM), as well as a description behind the operating principles and dynamic modes. A comprehensive overview of the multi-frequency techniques, Band excitation, and general acquisition mode SPM is given, setting the context for the application of big data, machine learning, and artificial intelligence in the field of dynamic SPM.

Collins, Liam↗

Portable fiber optic sensor for rare earth elements and other critical metals using photoluminescence methods

Rare earth elements and other metals are vital to a range of technologies that are used in the energy and defense sectors. However, monopolistic market conditions have caused significant concern over the stability of the critical metal supply chain, and this has spurred extensive efforts in many nations to produce these metals domestically, both from conventional sources such as mining and well as from unconventional sources such as coal and its utilization byproducts. Slow and expensive characterization methods pose a significant barrier for both metals prospecting and process monitoring. A promising solution to this challenge is the development of highly sensitive luminescent sensors for metals, which can offer low costs, portability, and sensitivity. Anionic zinc adeninate metal-organic frameworks (BioMOFs) are known to distinguishing and detect part-per-billion levels of terbium, europium, samarium, and dysprosium in water by sensitizing the narrow, element-specific emission bands from these lanthanides. Here, a BioMOF material is immobilized onto a large diameter, solarization-resistant fiber optic tip integrated with a portable, low-cost spectrometer for rare earth element sensing. Immobilizing the sensing material on fiber instead of dispersing the sensing material in solution offers several advantages: it facilitates solvent removal, which enhances luminescent signal from the sensitized lanthanides, and it also allows the BioMOF to be recycled for multiple uses. The sensing system was deployed on a simulated process stream and exhibited qualitative agreement with inductively-coupled plasma mass spectrometry for terbium and europium detection, highlighting the potential for the sensing system to be deployed for real-world applications. By using different sensing materials, the same portable sensor may be deployed to detect other energy relevant metals such as cobalt, providing a cost-effective and sensitive platform for critical metal characterization.

Crawford, Scott↗

Scalable foundation models for numerical simulations on HPC platforms

In recent years, foundation models (FMs) have begun to reshape numerical simulations on high-performance computing (HPC) platforms. These large, pre-trained AI models enable rapid predictions across a broad range of physical domains, including Earth system modeling, fluid dynamics, materials science, as well as complex multi-modal simulations in aerospace engineering and fusion research. By training on diverse datasets, FMs learn intricate relationships and underlying physical behavior while also enabling the quantification of uncertainty in their predictions. This capability allows simulations that once required days of numerical calculation to be completed in minutes (FM inference), supporting real-time design optimization, uncertainty-aware decision making, and more comprehensive exploration of complex scenarios.

AI↗

Powering the Blue Economy: Foundational Research and Development

With increased use of the oceans for transportation, extraction of food, fibre, and minerals, recreation and tourism, and other blue economy industries, there is a need for additional power at sea. In the face of climate change, the increased power must come from renewable sources; often marine energy is the most energy-dense power source available. But we have little experience with adapting wave and tidal devices from large scale grid power to the specialized and small devices needed to power blue economy applications, nor the engineering solutions to support codesign of emerging marine industries. As directed by the U.S. Department of Energy (DOE), two DOE national laboratories have developed foundational research and development projects to replace conventional power or batteries at sea (ocean observation platforms), emerging industry power needs (offshore aquaculture operations), as well as examining common challenges for using marine power (efficient power systems, minimizing interference, optimizing materials and manufacturing).

marine energy↗

Grain Boundary Plane Measurement Using Transmission Electron Microscopy Automated Crystallographic Orientation Mapping for Atom Probe Tomography Specimens

Abstract Grain boundaries are critical in determining the properties of materials, including mechanical stability, conductivity, and corrosion resistance. The specific properties of materials depend not only on the misorientation of the crystals, the three most commonly characterized parameters, but also on the angle of the grain boundary plane between the two crystals, the final two parameters in the five-parameter macroscopic description of the grain boundary. The method presented here allows for the direct measurement of all five parameters of the grain boundary in a transmission electron microscopy specimen of various morphologies. This is especially applicable to atom probe specimens, where only a single-tilt axis is generally available, allowing the crystallographic description to be matched to the detailed chemical data available in the atom probe tomography. This method provides a platform for efficient grain boundary analysis in unique samples, saving operator time and allowing for ease of acquisition and interpretation in comparison with traditional electron diffraction methods.

Materials Science↗

Octave bandwidth photonic fishnet-achromatic-metalens

Planar structured interfaces, also known as metasurfaces, are continuously attracting interest owing to their ability to manipulate fundamental attributes of light, including angular momentum, phase, or polarization. However, chromatic aberration, limiting broadband operation, has remained a challenge for metasurfaces-based optical components and imagers. The limitation stems from the intrinsic dispersion of existing materials and design principles. Here we report and experimentally demonstrate polarization-independent fishnet-achromatic-metalenses with measured average efficiencies over 70% in the continuous band from the visible (640 nm) to the infrared (1200 nm). Results of the scalable platform are enabling for applications requiring broad bandwidth and high efficiency including energy harvesting, virtual reality and information processing devices, or medical imaging.

36 MATERIALS SCIENCE↗

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

Time- and rate-dependent material functions in non-Newtonian fluids in response to different deformation fields pose a challenge in integrating different constitutive models into conventional computational fluid dynamic platforms. Considering their relevance in many industrial and natural settings alike, robust data-driven frameworks that enable accurate modeling of these complex fluids are of great interest. The main goal is to solve the coupled Partial Differential Equations (PDEs) consisting of the constitutive equations that relate the shear stress to the deformation and fully capture the behavior of the fluid under various flow protocols with different boundary conditions. Here, in this work, we present non-Newtonian physics-informed neural networks (nn-PINNs) for solving systems of coupled PDEs adopted for complex fluid flow modeling. The proposed nn-PINN method is employed to solve the constitutive models in conjunction with conservation of mass and momentum by benefiting from Automatic Differentiation (AD) in neural networks, hence avoiding the mesh generation step. nn-PINNs are tested for a number of different complex fluids with different constitutive models and for several flow protocols. These include a range of Generalized Newtonian Fluid (GNF) empirical constitutive models, as well as some phenomenological models with memory effects and thixotropic timescales. nn-PINNs are found to obtain the correct solution of complex fluids in spatiotemporal domains with good accuracy compared to the ground truth solution. We also present applications of nn-PINNs for complex fluid modeling problems with unknown boundary conditions on the surface, and show that our approach can successfully recover the velocity and stress fields across the domain, including the boundaries, given some sparse velocity measurements.

42 ENGINEERING↗

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

An Adsorptive Membrane Platform for Precision Ion Separation: Membrane Design and First‐Principles Studies

One of the key challenges in separation science is the lack of precise ion separation methods and mechanistic understanding crucial for efficiently recovering critical materials from complex aqueous matrices. Herein, first-principles electronic structure calculations and in situ Raman spectroscopy are studied to elucidate the factors governing ion discrimination in an adsorptive membrane specifically designed for transition metal ion separation. Density functional theory calculations and in situ Raman data jointly reveal the thermodynamically favorable binding preferences and detailed adsorption mechanisms for competing ions. How membrane binding preferences correlate with the electronic properties of ligands is explored, such as orbital hybridization and electron localization. The findings underscore the importance of the phenolate group in oxime ligands for achieving high selectivity among competing transition metal ions. In-depth understanding on which specific atomistic site within the microenvironment of metal-ligand binding pockets governs the ion discrimination behaviors of the host will build a solid foundation to guide the rational design of next-generation materials for precision separation essential for energy technologies and environment remediation. In tandem, synthetic controllability is demonstrated to transform 3D micrometer-scale crystals to a 2D crystalline selective layer in membranes, paving the way for more precise and sustainable advances in separation science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantized electronic transitions in electrodeposited copper indium selenide nanocrystalline homojunctions

Pairing semiconductors with electrochemical processing offers an untapped opportunity to create novel nanostructures for practical devices. Here we report the results of one such pairing: the in-situ formation of highly-doped, interface-matched, sharp nanocrystalline homojunctions (NHJs) with single step electrodeposition of two copper-indium-selenide (CISe) compounds on flexible foil. It produces a homogenous film, comprising inherently ordered, 3-dimensional interconnected network of pn -CISe NHJs. These CISe NHJs exhibit surprising non-linear emissions, quantized transitions, large carrier mobility, low trap-state-density, long carrier lifetime and possible up-conversion. They facilitate efficient separation of minority carriers, reduce recombination and essentially function like quantum materials. This approach mitigates the material issues and complex fabrication of incumbent nanoscale heterojunctions; it also overcomes the flexibility and scale-up challenges of conventional planar pn junctions. The self-stabilized CISe NHJ film can be roll-to-roll processed in ambient atmosphere, thus providing a promising platform for a range of optoelectronic technologies. This concept exemplified by CISe compounds can be adapted to create nano-scale pn junctions with other inorganic semiconductors.

36 MATERIALS SCIENCE↗

Local Multimodal Electro-Chemical-Structural Characterization of Solid-Electrolyte Grain Boundaries

Typical models of polycrystalline ionic materials treat the grain boundary properties as single valued, without consideration of the full range of values that define the macroscopically measured average. Here a unique experimental platform suitable for local multimodal characterization of individual grain boundaries in bicrystal fibers is reported. A variation of three orders of magnitude in the grain boundary conductivity of ceria is observed, as measured across six individual bicrystals by both alternating current impedance spectroscopy and direct current (D.C.) linear sweep voltammetry. Nonlinear behavior of the D.C. measurements is consistent with resistance due to a space charge effect. Time-of-flight secondary ion beam spectroscopy reveals a correlation between grain boundary resistance and the concentration of impurities Si, Al, and Ca segregated at the grain boundaries, although the bulk concentrations of these impurities are negligible. Electron backscatter diffraction analysis of the crystal orientations suggests a correlation between the misorientation across the grain boundaries and grain boundary resistance. These correlations point towards a grain boundary resistance that arises from impurity-generated space charge effects and variations in impurity concentration and hence resistivity driven by the energetics of impurity segregation to grain boundaries of differing surface energies.

36 MATERIALS SCIENCE↗

Ta compressibility to 20+ Mbar

Developing techniques for experimentally constraining equation of state (EOS) models for important programmatic materials under extreme conditions is vital for advancing our modeling and predictive capabilities. Tantalum is a frequently used standard material for both calibration and testing, and this report describes our work to both measure Ta compressibility to very high pressures and densities using the Ramp Compression Equation of State platform on the National Ignition Facility (NIF). Using a series of seven shots, increasing in peak pressure with each shot, we have made absolute measurements of the compressibility of Ta along the ramp compression path up to 2.3 TPa. Previous experimental measurements constrained the cold compressibility up to ~400 GPa, and there is a spread in the EOS models above that pressure. To improve communication and collaboration between the experimental team and the EOS development group, these new data, along with other experimental and theoretical constraints, were used to develop a Ta equation of state table, M73000. This report first describes the experimental measurements made at the NIF, and then discusses the construction of the EOS model. The ramp compression data can be found in tabular form in Appendix A. Appendix B contains some additional experimental details. In Appendix C we include some additional details on the EOS modeling. At the end of this document, we have attached a detailed report on the NIF ramp compression platform itself and in the main text referenced specific sections for additional details.

36 MATERIALS SCIENCE↗

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO 3 /Ge, (2) La 0.8 Sr 0.2 FeO 3 thin films, and (3) MoO 3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.

36 MATERIALS SCIENCE↗

Observation of multiple nodal lines in SmSbTe

Having been a ground for various topological fermionic phases, the family of ZrSiS-type 111 materials has been under experimental and theoretical investigations. Within this family of materials, the subfamily LnSbTe (Ln = lanthanide elements) is gaining interest in recent times as the strong correlation effects and magnetism arising from the 4f electrons of the lanthanides can provide an important platform to study the link between topology, magnetism, and correlation. In this Letter, we report the systematic study of the electronic structure of SmSbTe—a member of the Ln SbTe subfamily—by utilizing angle-resolved photoemission spectroscopy in conjunction with first-principles calculations, transport, and magnetic measurements. Our experimental results identify multiple Dirac nodes forming the nodal lines along the Γ–X and Z–R directions in the bulk Brillouin zone (BZ) as predicted by our theoretical calculations. Additionally, a surface Dirac-like state is also observed at the X point of the surface BZ. Our study highlights SmSbTe as a promising candidate to understand the topological electronic structure of LnSbTe materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Polymer Coatings on Virus-like Particle Nanoreactors at Low Ionic Strength—Charge Reversal and Substrate Access

Virus-like particles (VLPs) are a class of biomaterials which serve as platforms for achieving the desired functionality through interior and exterior modifications. Through ionic strength-mediated electrostatic interactions, VLPs have been assembled into hierarchically ordered materials. This work builds on predictive models to prepare polymer-coated VLP dusters at very low ionic strength. Zeta potential measurements showed that the clusters carried a strongly positive charge, a complete charge reversal from the VLP building block. SAXS analysis confirmed polymer adsorption onto the VLP exterior. Furthermore, we then studied the activity of an encapsulated enzyme toward small molecular and macromolecular substrates to determine the effect of each component of the hierarchically assembled material. We found that while encapsulation and polymer coating did not have a large effect on access to the enzyme by its native, small molecular substrate, substrate modification with a macromolecule caused the polymer coating and encapsulation to affect the access to the enzyme.

59 BASIC BIOLOGICAL SCIENCES↗

Metal–Organic Framework as a Dual Support for Organic Photosensitizers and Single-Atom Catalysts

Metal–organic framework (MOF)-supported single-atom catalysts (SACs) possess integrated unique capabilities of both MOF and SACs, which represent a promising class of catalysts for photocatalytic applications. Herein, we report the incorporation of nickel (Ni) SACs onto its zirconium node and the functionalization of the organic linker of the zirconium MOF with perylene tetracarboxylic dianhydride (PDA), resulting in the formation of a hybrid MOF (denoted as Ni-PiU) with significantly enhanced light absorption ability in the visible region. Using the combination of time-resolved emission and X-ray absorption spectroscopy, we show that efficient charge separation occurs by electron transfer from incorporated PDA to Ni SACs with super slow charge recombination dynamics. As a result of these important photophysical properties, Ni-PiU MOF exhibits excellent photocatalytic activity and durability for the hydrogen evolution reaction. Finally, this work demonstrates the unique ability of MOFs as a dual platform to incorporate both SACs and a light absorption unit into their framework, providing a promising strategy for rational design of next-generation photocatalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Shock equation of state experiments in MgO up to 1.5 TPa and the effects of optical depth on temperature determination

Laser-driven shock compression enables an experimental study of phase transitions at unprecedented pressures and temperatures. One example is the shock Hugoniot of magnesium oxide (MgO), which crosses the B1–B2-liquid triple point at 400–600 GPa, 10 000–13 000 K (0.86–1.12 eV). MgO is a major component within the mantles of terrestrial planets and has long been a focus of high-pressure research. Here, we combine time-resolved velocimetry and pyrometry measurements with a decaying shock platform to obtain pressure–temperature data on MgO from 300 to 1500 GPa and 9000 to 50 000 K. Pressure–temperature–density Hugoniot data are reported at 1500 GPa. These data represent the near-instantaneous response of an MgO [100] single crystal to shock compression. We report on a prominent temperature anomaly between 400 and 460 GPa, in general agreement with previous shock studies, and draw comparison with equation-of-state models. We provide a detailed analysis of the decaying shock compression platform, including a treatment of a pressure-dependent optical depth near the shock front. We show that if the optical depth of the shocked material is larger than 1 μm, treating the shock front as an optically thick gray body will lead to a noticeable overestimation of the shock temperature.

36 MATERIALS SCIENCE↗