My experiences as physicist: from a college degree to fusion research
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A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
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This report details results of a one-year LDRD to understand the dynamics, figures of merit, and fabrication possibilities for levitating a micro-scale, disk-shaped dielectric in an optical field. Important metrics are the stability, positional uncertainty, and required optical power to maintain levitation. Much of the results are contained in a publication written by our academic alliance collaborators. Initial structures were grown at Sandia labs and a test fabrication flow was executed. Owing to our strength in VCSEL lasers, we were particularly interested in calculations and fabrication flows that could be compatible with a VCSEL light source.
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Optimization Online Repository
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For digital twins (DTs) to become a central fixture in mission critical systems, a better understanding is required of potential modes of failure, quantification of uncertainty, and the ability to explain a model’s behavior. These aspects are particularly important as the performance of a digital twin will evolve during model development and deployment for real-world operations.
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Sandia National Laboratories was asked to provide a technical feasibility assessment on whether programmability is a viable means to address the radiation levels in the Export Administration Regulations (EAR). Most (if not all) modern Complementary Metal Oxide Semiconductor (CMOS) technologies will exceed at least one of the EAR radiation criteria inherently, there is no longer anything “special” about domestic parts or technologies that exceed EAR radiation criteria. A modern part today is not “designed” to exceed EAR criteria and in fact a part would need to be “specially designed” to fail to meet all of these criteria. And although it is possible to design a part to fail to meet the EAR radiation criteria in an unprogrammed state, such a part would be difficult to design in some technologies, may not achieve the ultimate radiation levels desired, would fail to meet core reliability principles, and may be unsuitable for high consequence applications.
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This report summarizes a virtual workshop on early lessons from the COVID-19 pandemic as they pertain to proactively addressing future biological threats. Co-hosted by Sandia National Laboratory (Sandia) and the Council on Strategic Risks (CSR) in August 2020, the discussion involved experts who at that time were leading innovative efforts in various U.S. government agencies, industry, and academia sharing observations from their ongoing pandemic response efforts. Based on the input by these expert participants, it is clear that even though the pandemic response is ongoing, the following recommendations will be important to consider for more successfully addressing biological threats in the future: Continue building on the cross-sector collaboration and agility shown in the COVID-19 response; Expand capabilities for detecting biological threats early; Prioritize ways to create and disseminate medical countermeasures even faster; Create the U.S. bio industrial base needed for rapid response to biological threats, and keep it healthy; and, Major government reorganization may not be needed if there is effective work to form coalitions, improve coordination, and expand steady-state and surge capacities.
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Recent trends in the growth of distributed energy resources (DER) in the electric grid and newfound malware frameworks that target internet of things (IoT) devices is driving an urgent need for more reliable and effective methods for intrusion detection and prevention. Cybersecurity intrusion detection systems (IDSs) are responsible for detecting threats by monitoring and analyzing network data, which can originate either from networking equipment or end-devices. Creating intrusion detection systems for PV/DER networks is a challenging undertaking because of the diversity of the attack types and intermittency and variability in the data. Distinguishing malicious events from other sources of anomalies or system faults is particularly difficult. New approaches are needed that not only sense anomalies in the power system but also determine causational factors for the detected events. In this report, a range of IDS approaches were summarized along with their pros and cons. Using the review of IDS approaches and subsequent gap analysis for application to DER systems, a preliminary hybrid IDS approach to protect PV/DER communications is formed in the conclusion of this report to inform ongoing and future research regarding the cybersecurity and resilience enhancement of DER systems.
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Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The material of interest here is 304L stainless steel, and the objective of this work is to calibrate thermal-mechanical models: one for the constitutive behavior and another for failure. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of the models. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10–4 and 103 1/s. It also included notched tension and shear-dominated compression hat tests specifically designed to calibrate the failure model. All test specimens were extracted from a single piece of plate to maintain consistency. The constitutive model adopted was a modular $J_2$ plasticity model with isotropic hardening that included rate and temperature dependence. A criterion for failure initiation based on a critical value of equivalent plastic strain fitted the failure data appropriately and was adopted. Possible ranges of the values of the parameters of the models were determined partially on historical data from calibrations of the same alloy from other lots and are given here. The calibration of the parameters of the models were based on finite element simulations of the various material tests using relatively ne meshes and hexahedral elements. When using the model in structural finite element calculations, however, element formulations and sizes different from those in the calibration are likely to be used. A brief investigation demonstrated that the failure initiation predictions can be particularly sensitive to the element selection and provided an initial guide to compensate for the effect of element size in a specific example.
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Producing a complete and accurate set of signal detections is essential for automatically building and characterizing seismic events of interest for nuclear explosion monitoring. Signal detection algorithms have been an area of research for decades, but still produce large quantities of false detections and misidentify real signals that must be detected to produce a complete global catalog of events of interest. Deep learning methods have shown promising capabilities in effectively characterizing seismic signals for complex tasks such as identifying phase arrival times. We use the PhaseNet model, a UNet-based Neural Network, trained on local distance data from northern California to predict seismic arrivals on data from the International Monitoring System (IMS) global network. We use an analyst-curated bulletin generated from this data set to compare the performance of PhaseNet to that of the Short-Term Average/Long-Term Average (STA/LTA) algorithm. We find that PhaseNet has the potential of outperforming traditional processing methods and recommend the training of a new model with the IMS data to achieve optimal performance.
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There are an estimated 48,745 wells producing oil or gas in New Mexico as of August 8, 2020 and with advances in drilling and oil recovery technology the use of hydraulic fracturing has become more commonplace. With a typical well requiring 1.5 to 16 million gallons of water, there is an increased demand for water in the Permian Basin and concern over the regions ability to meet this demand. This report is an addendum to the 2018 report Water Resource Assessment in the New Mexico Permian Basin (SAND2018-12018) to monitor baseline water level and chemistry data established in the original report. Results from this addendum can be used to further understand regional water supply and demands and aid in the BLMs mission of sustainably meeting the needs of water users while protecting human and environmental health.
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The Manipulate Process Input/Output (IO) (ManiPIO) program allows users to develop custom scripts to execute Industrial Control System (ICS) manipulations. The driving development principles of ManiPIO are modularity and ease of use. Currently the program can utilize the Modbus TCP communication protocol, but its modular programming structure allows other protocols to be quickly and easily implemented. Additional functionality can be added to fit specific user needs, due to the usage of Python classes. The input configuration instructions are human readable and allow the user to create a complex series of control system manipulations.
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The purpose of this document is to provide evidence for assessing the adequacy of parameterized material models for a collection of materials used in a finite element analyses setting. “Adequacy” is relative to the intended use of the material in particular analyses. The intended application of the material models covered within this document is for system level abnormal mechanical solid mechanics analyses. Generally, material model parameterizations should be valid from temperatures of approximately -50 to 70° C, across a range of strain rates, and (depending on details of the parts involved) large deformations. Each material covered in this document is presented in its own chapter with a common format across materials. Model assumptions, limitations, existing validation results, readiness for use with uncertainty quantification, general usage guidance, and failure considerations are all provided along with specific parameterization inputs suitable for the finite element analysis code Sierra/Solid Mechanics.
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Data from four TGA experiments conducted at Sandia National Laboratories was used for determination of a pyrolysis model using a commercial thermokinetics program developed by Netzsch Instruments (Kinetics NEO, version 2.1). The data measured at 1 K/min and the average of three measurements at 50 K/min were used as input into Kinetics NEO. The model was developed using data in the range 373 to 773 K. An initial estimate of the energy of activation (E) and pre-exponential constant (A) were determined from the model-free Friedman approach.
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A site-specific Stormwater Pollution Prevention Plan (SWPPP) is needed for most construction activities/projects that disturb one (1) acre or more of land to meet the requirements of the National Pollutant Discharge Elimination System (NPDES) General Permit for Storm Water Discharges Associated With Construction and Land Disturbance Activities (General Permit) issued by the State Water Resources Control Board (State Board). This Order No. 2009-0009- DWQ was adopted by the State Board on September 2, 2009 and became effective July 1, 2010
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Engineers at Sandia National Laboratories (SNL) have used quartz - based clock oscillators in various missions since the 1980s. As such, the design of these frequency control devices requires a high degree of reliability and producibility. The quartz clock oscillators designed at SNL have evolved over many years and are currently developed in house and fabricated using SNLs internal CMOS foundry. They are designed to operate in harsh environments, including temperature, radiation, shock and vibration. This report documents the methodology behind the design of quartz clock oscillators developed at SNL and includes an overview of quartz resonator technology and usage guidelines and a detailed overview of oscillator circuits.
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Digital engineering strategies typically assume that digital engineering models interoperate seamlessly across the multiple different engineering modeling software applications involved, such as model- based systems engineering (MBSE), mechanical computer-aided design (MCAD), electrical computer-aided design (ECAD), and other engineering modeling applications. The presumption is that the data schema in these modeling software applications are structured in the familiar flat- tabular schema like any other software application. Engineering domain-specific applications (e.g., systems, mechanical, electrical, simulation) are typically designed to solve domain-specific problems, necessarily excluding explicit representations of non-domain information to help the engineer focus on the domain problems (system definition, design, simulation). Such exclusions become problematic in inter-domain information exchange. The obvious assumptions of one domain might not be so obvious to experts in another domain. Ambiguity in domain-specific language can erode the ability to enable different domain modeling applications to interoperate, unless the underlying language is understood and used as the basis for translation from one application to another. The engineering modeling software application industry has struggled for decades to enable these applications to interoperate. Industry standards have been developed, but they have not unified the industry. Why is this? The authors assert that the industry has relied on traditional database integration methods. The basic issue prohibiting successful application integration then is that traditional database-driven integration does not consider the distinct languages of each domain. An engineering models meaning is expressed through the underlying language of that engineering domain. In essence, traditional integration methods do not retain the semantic context (meaning) of the model. The basis of this research stems from the widely held assumption that systems engineering models are (or can be) structured according to the underlying semantic ontology of the model. This assumption can be imagined from two thoughts. 1) Digital systems engineering models are often represented using graph theory (the graph of a complex systems model can contain millions of nodes and edges). When examining the nodes one at a time and following the outbound edges of each node one by one, one can end up with rudimentary statements about the model (i.e., node A relates to node B), as in a semantic graph. 2) Likewise, from the study of natural languages, a sentence can be structured into unambiguous triples of subject-predicate-object within formal and highly expressive semantic ontologies. The rudimentary statements about a systems model discerned with graph theory closely mimic the triples used in the ontologies that try to structure natural languages. In other words, a systems models semantic graph can be (or is) structured into an ontology. Additionally, it is well established in industry that through natural language processing (NLP), which provides the means to create language structures, that computers can interpret ontological graphs. Therefore, the authors hypothesized that if the integrity of the underlying semantic structure of a systems model is retained, the contextual meaning of the model is retained. By structuring system models into the triples of the underlying ontology during the transformation from one MBSE application to another, the authors have provided a proof of the concept that the meaning of a system model can be retained during transformation. The authors assert that this is the missing ingredient in effective systems model-to-model interoperability. ACKNOWLEDGEMENTS The authors would like to thank the FY19 Model Interoperability team members who provided a solid foundation for the FY20 team to leverage: John McCloud, for the work he did to guide us toward the right use of technology that will appropriately discover and manipulate ontologies. Carlos Tafoya, for the work he did to develop an application programming interface (API)/Adapter that would export ontology-based data from GENESYS. Peter Chandler, for the work he did to architect our overall integration solution, with an eye toward the future that would influence a large-scale federated production-level systems engineering digital model ecosystem.
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The purpose of this report is to share work based on material originally described in a Sandia LDRD proposal for 2016 as well as an invention submission SD 14734 ( DOE # 150281) –“Abstracted, Modular, Ephemeral Autonomic Computing System(s) Codified” from April 2018. This work was done at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525
The MELCOR Accident Consequence Code System (MACCS) is used by Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This User Guide is intended to assist analysts in understanding the MACCS/WinMACCS model and to provide information regarding the code. This user guide version describes MACCS Version 3.10.0. Features that have been added to MACCS in subsequent versions are described in separate documentation. This User Guide provides a brief description of the model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2. PAPERWORK REDUCTION ACT STATEMENT The NUREG does not contain information collection requirements and, therefore, is not subject to the requirements of the Paperwork Reduction Act of 1995 (44 USC 3501, et seq.). PUBLIC PROTECTION NOTIFICATION The NRC may not conduct or sponsor, and a person is not required to respond to, a request for information or an information collection requirement unless the requesting document displays a currently valid OMB control number. ACKNOWLEDGEMENTS Contributions to this User Guide were received from NRC and Sandia National Laboratories (SNL) project managers, technical experts, and code authors dedicated to the production of a valuable resource for the MACCS user community. Instructions and guidance included herein were developed over many years and include advancements in the code that provide users the ability to develop complex consequence modeling scenarios. WinMACCS and many of the early MACCS developments were due to vision of an earlier Project Manager, Jocelyn Mitchell. Jonathan Barr and AJ Nosek also contributed to the development of this report. The current NRC Project Manager, Salman Haq, provided the leadership to ensure this document was completed. Several other NRC and Sandia staff provided insights supporting development of the MACCS code and of this document.
Researchers at Sandia have developed a semiconductor-based high-voltage switch, with experimental results showing potential for enhanced radiation hardness, for use in multiple power conversion applications. Gallium nitride (GaN) metal-oxide semiconductor field effect transistors (MOSFETs) were modeled using commercial and Sandia CHARON simulation software to understand their performance and for future prediction of device operation in radiation environments.
This report represents completion of milestone deliverable M2SF-21SN010309012 “Annual Status Update for OWL and Waste Form Characteristics” that provides an annual update on status of fiscal year (FY 2020) activities for the work package SF-20SN01030901 and is due on January 29, 2021. The Online Waste Library (OWL) has been designed to contain information regarding United States (U.S.) Department of Energy (DOE)-managed (as) high-level waste (DHLW), spent nuclear fuel (SNF), and other wastes that are likely candidates for deep geologic disposal, with links to the current supporting documents for the data (when possible; note that no classified or official-use-only (OUO) data are planned to be included in OWL). There may be up to several hundred different DOE-managed wastes that are likely to require deep geologic disposal. This draft report contains versions of the OWL model architecture for vessel information (Appendix A) and an excerpt from the OWL User’s Guide (Appendix B and SNL 2020), which are for the current OWL Version 2.0 on the Sandia External Collaboration Network (ECN).
Journal of Chemical Physics
Many important chemically reacting systems are inherently multi-dimensional with spatial and temporal variations in the thermochemical state, which can be strongly coupled to interactions with transport processes. Fundamental insights into these systems require multi-dimensional measurements of the thermochemical state as well as fluid dynamics quantities. Laser-based imaging diagnostics provide spatially and temporally resolved measurements that help address this need. The state of the art in imaging diagnostics is continually progressing with the goal of attaining simultaneous multi-parameter measurements that capture transient processes, particularly those that lead to stochastic events, such as localized extinction in turbulent combustion. Development efforts in imaging diagnostics benefit from advances in laser and detector technology. This article provides a perspective on the progression of increasing dimensionality of laser-based imaging diagnostics and highlights the evolution from single-point measurements to 1D and 2D multi-parameter imaging and 3D high-speed imaging. This evolution is demonstrated using highlights of laser-based imaging techniques in combustion science research as an exemplar of a complex multi-dimensional chemically reacting system with chemistry-transport coupling. Imaging diagnostics impact basic research in other chemically reacting systems as well, such as measurements of near-surface gases in heterogeneous catalysis. The expanding dimensionality of imaging diagnostics leads to larger and more complex datasets that require increasingly demanding approaches to data analysis and provide opportunities for increased collaboration between experimental and computational researchers in tackling these challenges.
Nano Letters
Searching for multifunctional materials with tunable magnetic and optical properties has been a critical task toward the implementation of future integrated optical devices. Vertically aligned nanocomposite (VAN) thin films provide a unique platform for multifunctional material designs. Here, a new metal-oxide VAN has been designed with plasmonic Au nanopillars embedded in a ferromagnetic La0.67Sr0.33MnO3 (LSMO) matrix. Such Au-LSMO nanocomposite presents intriguing plasmon resonance in the visible range and magnetic anisotropy property, which are functionalized by the Au and LSMO phase, respectively. Furthermore, the vertically aligned nanostructure of metal and dielectric oxide results in the hyperbolic property for near-field electromagnetic wave manipulation. Such optical and magnetic response could be further tailored by tuning the composition of Au and LSMO phases.
Journal of Chemical Health and Safety
Laboratory research can expose workers to a wide variety of chemical hazards. Researchers must not only take personal responsibility for their safety but also inevitably rely on coworkers to also work safely. The foundations for protocols, requirements, and behaviors come from our history and lessons learned from others. For that reason, here, a recent incident is examined in which a researcher suffered hydrofluoric acid (HF) burns while working with an inorganic digestion mixture of aqueous HF (8%) and nitric acid (HNO3, 58%). HF education is critical for workers because delays in treatment, improper treatment, and delay of symptoms are all factors in unfavorable outcomes in case reports. Furthermore, while the potential severity of the incident was elevated due to bypassed engineered controls and lack of proper personal protective equipment, only minor injuries were sustained. We discuss the results of a causal analysis of the incident that revealed areas of improvement in protocols, personal protective equipment, and emergency response that could help prevent similar accidents from occurring. We also present simple improvements that anyone can implement to reduce the potential consequences of an accident, based upon our lessons learned.
The purpose of this document is to disseminate lessons learned from the Sandia National Laboratories (SNL) Building 1090 modification project. The following sections summarize lessons learned at various phases of the project.
Sandia National Laboratories is developing a way to visualize molecular changes that indicate penetration of a tamper-indicating enclosure (TIE). Such "bleeding" materials (analogous to visually obvious, colorful bruised skin that doesn't heal) allows inspectors to use simple visual observation to readily recognize that penetration into a material used as a TIE has been attempted, without providing adversaries the ability to repair damage. Such a material can significantly enhance the current capability for TIEs, used to support treaty verification regimes. Current approaches rely on time-consuming and subjective visual assessment by an inspector, external equipment, such as eddy current or camera devices, or active approaches that may be limited due to application environment. The complexity of securing whole volumes includes: (1) enclosures that are non-standard in size/shape; (2) enclosures that may be inspectorate- or facility-owned; (3) tamper attempts that are detectable but difficult or timely for an inspector to locate; (4) the requirement for solutions that are robust regarding reliability and environment (including facility handling); and (5) the need for solutions that prevent adversaries from repairing penetrations. The approach is based on a transition metal ion solution within a microsphere changing color irreversibly when the microsphere is ruptured. Investigators examine 3D printing of the microspheres as well as the spray coating formulation. The anticipated benefits of this work are passive, flexible, scalable, cost-effective TIEs with obvious and robust responses to tamper attempts. This results in more efficient and effective monitoring, as inspectors will require little or no additional equipment and will be able to detect tamper without extensive time-consuming visual examination. Applications can include custom TIEs (cabinets or equipment enclosures), spray-coating onto facility-owned items, spray-coating of walls or structures, spray-coatings of circuit boards, and 3D-printed seal bodies. The paper describes research to-date on the sensor compounds and microspheres.
In line with the Global Health Security Agenda for Vietnam SNL has successfully engaged in country by ensuring long term sustainability of its programs and building BRM capacity by engaging with Government Institutions and strengthening the national biosafety and biosecurity and also by engaging with academic institutions through Vietnam One Health University Network to educate the One Health Workforce and promote a shared culture of responsibility, reduce dual use risks, mitigate biological proliferation and deliberate use threats.
The precise and accurate detection of dismounts is a desired capability for many radar systems. To detect dismounts under foliage requires using a radar with foliage penetrating (FOPEN) capability. FOPEN frequencies are subject to unique phenomenological aspects that are not typically encountered at microwave frequencies. This phenomenology places limitations on the feasibility of microwave radar approaches for dismount detection. This report provides an overview of these aspects of FOPEN radar system design.
Women make essential contributions to agricultural sectors worldwide through livestock rearing, production of animal-based products, and promotion of animal health. Approximately 752 million of the world's poor have livestock for food, income, work, and/or societal status, and women comprise two-thirds of this population. The Food and Agriculture Organization for the United Nations, the World Bank, the United States Agency for International Development, and other international organizations widely recognize the value of women in agriculture.
n this presentation we will discuss recent results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia Labs developed Whet stone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.
Regulatory drivers and market demands for lower pollutant emissions, lower carbon dioxide emissions, and lower fuel consumption motivate the development of clean and fuel-efficient engine operating strategies. Most current production engines use a combination of both in-cylinder and exhaust emission control strategies to achieve these goals. The emissions and efficiency performance of in-cylinder strategies depend strongly on flow and mixing processes associated with fuel injection. Both heavy- and light-duty engine/vehicle manufacturers use multiple-injection strategies to reduce noise, emissions, and fuel consumption. For both conventional and low-temperature diesel combustion, the state of knowledge and modeling tools for multiple injections are far less advanced than for single-injection strategies. Engine efficiency is limited to some degree by tradeoffs that must be accepted to meet particulate matter (including soot) emissions limits. Recent work on this project has filled some knowledge gaps on soot oxidation with multiple injections, and the current work for Fiscal Year (FY) 2018 addresses knowledge gaps on soot formation for multiple injections. While total in-cylinder soot is readily measured, discerning formation from oxidation is difficult. The FY 2018 experiments are designed to create in-cylinder conditions at the threshold of soot formation, where processes that affect soot formation can be more readily discerned. Soot formation pathways under such conditions are fraught with uncertainties, and soot models significantly overpredict polyaromatic hydrocarbon (PAH) and soot, so experimental data at these conditions will provide much needed data for improvements to PAH and soot models.
The goal of this project is to quantify the effect of adsorption on noble gas transport though rocks that contain zeolite compared with rocks that don't. Success is defined by developing a coefficient called Retardation that describes the separation effect of zeolites. This coefficient can then be used by gas transport modelers to predict field-scale observations of noble gases released underground in a chemical explosion
The document provides instructions to follow for software and server installation and implementation.
Dalton Transactions
Non-aqueous redox flow batteries (RFBs) offer the possibility of higher voltage and a wider working temperature range than their aqueous counterpart. Here, we optimize the established 2.26 V Fe(bpy)3(BF4)2/Ni(bpy)3(BF4)2 asymmetric RFB to lessen capacity fade and improve energy efficiency over 20 cycles. We also prepared a family of substituted Fe(bpyR)3(BF4)2 complexes (R = -CF3, -CO2Me, -Br, -H, -tBu, -Me, -OMe, -NH2) to potentially achieve a higher voltage RFB by systematically tuning the redox potential of Fe(bpyR)3(BF4)2, from 0.94 V vs. Ag/AgCl for R = OMe to 1.65 V vs. Ag/AgCl for R = CF3 (ΔV = 0.7 V). A series of electronically diverse symmetric and asymmetric RFBs were compared and contrasted to study electroactive species stability and efficiency, in which the unsubstituted Fe(bpy)3(BF4)2 exhibited the highest stability as a catholyte in both symmetric and asymmetric cells with voltage and coulombic efficiencies of 94.0% and 96.5%, and 90.7% and 80.7%, respectively.
The boundary regions of low-temperature plasmas are known to be susceptible to kinetic instabilities, which can affect the energies and fluxes of particles directed at the material boundary. For example, both the ion acoustic instability as well as an instability near the electron plasma frequency have been observed. Particle-in-cell (PIC) simulation is a tool that, alongside experiments, can capture the effects these instabilities have on the particle distribution functions. Ultimately, simulations can determine under what conditions these effects are significant by comparing to theoretical predictions and explore conditions unamenable to experiments.
ACS Applied Materials and Interfaces
Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. A diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.
Angewandte Chemie - International Edition
Optical tags provide a way to quickly and unambiguously identify valuable assets. Current tag fluorophore options lack the tunability to allow combined methods of encoding in a single material. Herein we report a design strategy to encode multilayer complexity in a family of heterometallic rare-earth metal–organic frameworks based on highly connected nonanuclear clusters. To impart both intricacy and security, a synergistic approach was implemented resulting in both overt (visible) and covert (near-infrared, NIR) properties, with concomitant multi-emissive spectra and tunable luminescence lifetimes. Tag authentication is validated with a variety of orthogonal detection methodologies. Importantly, the effect induced by subtle compositional changes on intermetallic energy transfer, and thus on the resulting photophysical properties, is demonstrated. This strategy can be widely implemented to create a large library of highly complex, difficult-to-counterfeit optical tags.
Bulletin of the Seismological Society of America
Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.