Multimodal Spectroscopic Study of Surface Termination Evolution in Cr 2 TiC 2 T x MXene
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The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are discussed. The target machine for this software is a JPL/Caltech Mark IIIfp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules; (1) user interface, (2) host-resident executive, (3) hypercube-resident executive, and (4) application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube a data management method which distributes, redistributes, and tracks data set information was implemented.
An integrated fiber optic probe, comprising a monomode optical fiber fusion spliced to a short length of a graded-index multimode fiber, is fabricated for use as a coherent receiver in dynamic light scattering. The multimode fiber is cleaved to provide a gradient-index fiber lens with a focal length of 125 microns and an f-number close to unity. An integrated fiber receiver is used to measure the intensity-intensity autocorrelation data from a 0.05 percent by weight concentration of an aqueous suspension of polystyrene latex spheres. Analysis of 100 independent data sets indicates that the particle size can be recovered with an accuracy of +/- 1 percent.
Sodium (Na) batteries are of growing interest due to the higher earth abundance of sodium than lithium, as well as their promising theoretical energy density when metallic Na anodes are used. However, Na plating and stripping are heavily influenced by the physicochemical properties of the solid electrolyte interphase (SEI), which is directly influenced by the solvent and salt used for the electrolyte. While most studies focus on the SEI that forms on the surface of Na metal after plating, we expand this analysis by identifying a nanoscale “pre-plating” SEI that forms on the current collector (CC) prior to the onset of Na plating. Here, we systematically investigate an array of Na salt and glyme solvents in the electrolyte and determine the associated impacts on pre-plating SEI formation on aluminum CCs. By combining analytical electrochemistry approaches with a multimodal suite of spectroscopy techniques (X-ray, infrared, and Raman), supported by density functional theory calculations, we reveal a direct correlation between the Na + coordination environment and pre-plating SEI composition. We find that longer-chain glymes produce larger proportions of organic alkoxide products in the interphase, consistent with increased Na + −glyme interactions, while the fraction of salt-derived inorganic products (e.g., NaF) correlates with Na + −anion coordination. These insights highlight the critical influence of electrolyte composition particularly solvent identity and Na + coordination on the initial SEI formation in anode-free Na batteries.
The photodissociation dynamics of alkyl iodides along the C–I bond are captured by attosecond extreme-ultraviolet (XUV) transient absorption spectroscopy employing resonant ∼20 fs UV pump pulses. The methodology of previous experiments on CH3I [Chang et al., J. Chem. Phys. 154, 234301 (2021)] is extended to the investigation of a C–I bond-breaking reaction in the dissociative A-band of C2H5I, i-C3H7I, and t-C4H9I. Probing iodine 4d core-to-valence transitions in the XUV enables one to map wave packet bifurcation at a conical intersection in the A-band as well as coherent vibrations in the ground state of the parent molecules. Analysis of spectroscopic bifurcation signatures yields conical intersection crossing times of 15 ± 4 fs for CH3I, 14 ± 5 fs for C2H5I, and 24 ± 4 fs for i-C3H7I and t-C4H9I, respectively. Observations of coherent vibrations, resulting from a projection of A-band structural dynamics onto the ground state by resonant impulsive stimulated Raman scattering, indirectly reveal multimode C–I stretch and CCI bend vibrations in the A-bands of C2H5I, i-C3H7I, and t-C4H9I.
A comparative failure analysis is presented based on the application of quadratic and cubic forms of the tensor polynomial lamina strength criterion to various composite structural configurations in a plane stress state. Failure loads have been predicted for off-angle laminates under simple loading conditions and for symmetric-balanced laminates subject to varying degrees of biaxial tension, including configurations subject to multimode failures. Some experimental data are also provided to support these calculations. From these results, the necessity of employing a cubic strength criterion to accurately predict the failure of composite laminae is demonstrated.
Inspired by biological neuromorphic computing, artificial neural networks based on crossbar arrays of bilayer tantalum oxide memristors have shown to be promising alternatives to conventional complementary metal-oxide-semiconductor (CMOS) architectures. In order to understand the driving mechanism in these oxide systems, tantalum oxide films are resistively switched by conductive atomic force microscopy (C-AFM), and subsequently imaged by kelvin probe force microscopy (KPFM) and spatially resolved time-of-flight secondary ion mass spectrometry (ToF-SIMS). These workflows enable induction and analysis of the resistive switching mechanism as well as control over the resistively switched region of the film. In this work it is shown that the resistive switching mechanism is driven by both current and electric field effects. Reversible oxygen motion is enabled by applying low (<1 V) electric fields, while high electric fields generate irreversible breakdown of the material (>1 V). Fully understanding oxygen motion and electrical effects in bilayer oxide memristor systems is a fundamental step toward the adoption of memristors as a neuromorphic computing technology.
A preliminary design study examined the feasibility of using microwave resonator measurements to improve the accuracy of atmospheric absorption coefficients and refractivity between 18 and 35 GHz. Increased accuracies would improve the capability of water vapor radiometers to correct for radio signal delays caused by Earth's atmosphere. Calibration of delays incurred by radio signals traversing the atmosphere has applications to both deep space tracking and planetary radio science experiments. Currently, the Cassini gravity wave search requires 0.8-1.0% absorption coefficient accuracy. This study examined current atmospheric absorption models and estimated that current model accuracy ranges from 5% to 7%. The refractivity of water vapor is known to 1% accuracy, while the refractivity of many dry gases (oxygen, nitrogen, etc.) are known to better than 0.1%. Improvements to the current generation of models will require that both the functional form and absolute absorption of the water vapor spectrum be calibrated and validated. Several laboratory techniques for measuring atmospheric absorption and refractivity were investigated, including absorption cells, single and multimode rectangular cavity resonators, and Fabry-Perot resonators. Semi-confocal Fabry-Perot resonators were shown to provide the most cost-effective and accurate method of measuring atmospheric gas refractivity. The need for accurate environmental measurement and control was also addressed. A preliminary design for the environmental control and measurement system was developed to aid in identifying significant design issues. The analysis indicated that overall measurement accuracy will be limited by measurement errors and imprecise control of the gas sample's thermodynamic state, thermal expansion and vibration- induced deformation of the resonator structure, and electronic measurement error. The central problem is to identify systematic errors because random errors can be reduced by averaging. Calibrating the resonator measurements by checking the refractivity of dry gases which are known to better than 0.1% provides a method of controlling the systematic errors to 0.1%. The primary source of error in absorptivity and refractivity measurements is thus the ability to measure the concentration of water vapor in the resonator path. Over the whole thermodynamic range of interest the accuracy of water vapor measurement is 1.5%. However, over the range responsible for most of the radio delay (i.e. conditions in the bottom two kilometers of the atmosphere) the accuracy of water vapor measurements ranges from 0.5% to 1.0%. Therefore the precision of the resonator measurements could be held to 0.3% and the overall absolute accuracy of resonator-based absorption and refractivity measurements will range from 0.6% to 1.
High-resolution mobility-based ion separations in Structures for Lossless Ion Manipulations (SLIM) have been useful for ion mobility separations for a variety of molecular classes in the gas phase. Here, in this study, we present multipass SLIM separations for gas-phase proteins in their near-native state exhibiting charge-state-dependent arrival time distributions using carbonic anhydrase (29 kDa), alcohol dehydrogenase (148 kDa), and apo-transferrin (79 kDa). The experimental CCS values were obtained from calibration curves for the arrival times of Agilent Tune Mix ions. For multipass separations, the ATDs were converted to CCS values by deconvoluting the multipass arrival times into accurate single-pass values amenable to the single-pass calibration curves. Mass spectra of carbonic anhydrase (CA) showed three different charge states (z = 9+ to 11+). Their corresponding mobility peaks were baseline-separated by using 8-m single-pass separations. When compared to the corresponding drift tube ion mobility (DTIMS) measurements, the CCS values obtained from DTIMS and SLIM were in agreement within experimental error. Single-pass analysis of alcohol dehydrogenase (ADH) exhibits three predominant charge states (z = 23+ to 25+) with mobility overlap between adjacent charge states. The mobility peak resolution for ADH improved with multipass separations (up to 24-m path length). In addition, CCS distributions obtained for charge states z = 16+ to 18+ of apo-transferrin reveal a transition from a compact unimodal form (z = 18+ and 19+) to broader multimodal CCS distributions for z = 16+. For apo-transferrin, 40-m multipass separations were performed allowing for complete isolation of the selected mobility range corresponding to z = 17+, leading to selective isolation of a narrow arrival time window. The extended mobility separations provided minimal alterations to the structure of the proteins, and the experimentally derived CCS values showed minimal change as a function of the separation time or number of passes. Mobility-based ion separations for native-like proteins, using SLIM, open opportunities for native-IMS applications as well as other manipulations enabled by SLIM-like mobility-selective isolation and collection.
The concept of squeezing and uncertainty relations are discussed for multimode quantum light with the consideration of polarization. Using the polarization gauge SU(2) invariance of free electromagnetic fields, we separate the polarization and biphoton degrees of freedom from other ones, and consider uncertainty relations characterizing polarization and biphoton observables. As a consequence, we obtain a new classification of states of unpolarized (and partially polarized) light within quantum optics. We also discuss briefly some interrelations of our analysis with experiments connected with solving some fundamental problems of physics.
Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.
Glauber’s g (2) -function provides a common measure of quantum field statistics through two-photon coincidence counting in Hanbury Brown–Twiss measurements. Here, we propose to use nonlinear optical signals as a tool for the characterization of quantum light. In particular, we show that Raman measurements provide an alternative direct probe for a different component of the four-point correlation function underlying the g (2) -function. We illustrate this capacity for a specific quantum state obtained from a frequency conversion process. Our work points out how the analysis of controlled optical nonlinear processes can provide an alternative window toward the analysis of quantum light sources.
The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.
Herein, we assess the complementarity and complexity of data that can be detected within mammalian lipidome mass spectrometry imaging (MSI) via matrix-assisted laser desorption ionization (MALDI) and nanospray desorption electrospray ionization (nano-DESI). We do so by employing 21 T Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) with absorption mode FT processing in both cases, allowing unmatched mass resolving power per unit time (≥613k at m/z 760, 1.536 s transients). While our results demonstrated that molecular coverage and dynamic range capabilities were greater in MALDI analysis, nano-DESI provided superior mass error, and all annotations for both modes had sub-ppm error. Taken together, these experiments highlight the coverage of 1676 lipids and serve as a functional guide for expected lipidome complexity within nano-DESI-MSI and MALDI-MSI. To further assess the lipidome complexity, mass splits (i.e., the difference in mass between neighboring peaks) within single pixels were collated across all pixels from each respective MSI experiment. The spatial localization of these mass splits was powerful in informing whether the observed mass splits were biological or artificial (e.g., matrix related). Mass splits down to 2.4 mDa were observed (i.e., sodium adduct ambiguity) in each experiment, and both modalities highlighted comparable degrees of lipidome complexity. Further, we highlight the persistence of certain mass splits (e.g., 8.9 mDa; double bond ambiguity) independent of ionization biases. Here, we also evaluate the need for ultrahigh mass resolving power for mass splits ≤4.6 mDa (potassium adduct ambiguity) at m/z > 1000, which may only be resolved by advanced FTICR-MS instrumentation.
Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils, because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hotspots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood. Here, we addressed this knowledge gap by directly visualizing nutrient acquisition and transport through fungal hyphae in a mineral doped soil micromodel using a multimodal imaging approach. Here, we observed how a representative of common saprotrophic soil fungi, Fusarium sp. DS 682, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces (~12 μm) in the presence of minerals.This study establishes the significance of fungal biology and nutrient translocation mechanisms in maintaining fungal growth under water and nutrient limitations in a soil-like microenvironment, using a high-throughput multi-omic analysis approach. Data package KS4A-Omics.1.0_FspDS682 (Publication: Fungal Mineral Weathering Mechanisms Revealed Through Direct Molecular Visualization) contents reported here are the first version (1.0) and contain pre- and post-processed data using high throughput data capture technologies for multi-omic analysis and integration, this data package contains raw and post-processed experimental data for X-Ray Absorption Near Edge Structure Spectroscopy (XANES/XRF), Optical Microscopy, Proteomics, Scanning Electron Microscope (SEM), Time-of-Flight Secondary Ion Mass Spectroscopy (ToF-SIMS), X-Ray Diffraction Spectroscopy (XRD) files, and X- Ray Photoelectron Spectroscopy (XPS) using EMSL capabilities. This data package DOI contains a comprehensive collection of high-throughput multi-omics data and process metadata catalog. Support files include additional data download contents “Read Me” with dataset descriptor information and data source method application ontologies (see data dictionary section). Reported data download content is structured for compliance with reported guidelines provided by community standard initiatives and publisher stakeholder policies supporting FAIR data principles.
An overview is given of work in progress aimed at developing computational algorithms addressing two important aspects in the control of large flexible space structures; namely, the selection and placement of sensors and actuators, and the resulting multivariable control law design problem. The issue of sensor/actuator set selection is particularly crucial to obtaining a satisfactory control design, as clearly a poor choice will inherently limit the degree to which good control can be achieved. With regard to control law design, the researchers are driven by concerns stemming from the practical issues associated with eventual implementation of multivariable control laws, such as reliability, limit protection, multimode operation, sampling rate selection, processor throughput, etc. Naturally, the burden imposed by dealing with these aspects of the problem can be reduced by ensuring that the complexity of the compensator is minimized. Our approach to these problems is based on extensions to input/output oriented techniques that have proven useful in the design of multivariable control systems for aircraft engines. In particular, researchers are exploring the use of relative gain analysis and the condition number as a means of quantifying the process of sensor/actuator selection and placement for shape control of a large space platform.
Surface science methodologies for understanding thermodynamic aspects of surface processes are at an advanced level. However, instrumentation and approaches for extracting kinetic parameters from elementary steps are far less accessible. In this work, we present an approach combining the use of a fast gas pulsing valve synchronized with data acquisition to enable surface transient kinetics studies using infrared reflection absorption spectroscopy. This methodology applies to the study of reversible processes and borrows concepts and ideas from molecular beam scattering and temporal analysis of products. Here, a temporal resolution of ~67 ms is achieved, and this is illustrated through the study of CO adsorption and desorption on a Pd(111) crystal in the presence and absence of background O 2 . The same approach can be extended to other surface spectroscopies, such as X-ray photoelectron spectroscopy, to obtain spectra with high temporal resolution and signal-to-noise ratio and enable future multimodal surface transient kinetic studies aiming at elucidating reaction mechanisms.
A quantitative analysis is presented of the role of optical fibers in laser light scattering. Design of a general fiber optic/microlens probe by means of ray tracing is described. Several different geometries employing an optical fiber of the type used in lightwave communications and a graded index microlens are considered. Experimental results using a nonimaging fiber optic detector probe show that due to geometrical limitations of single mode fibers, a probe using a multimode optical fiber has better performance, for both static and dynamic measurements of the scattered light intensity, compared with a probe using a single mode fiber. Fiber optic detector probes are shown to be more efficient at data collection when compared with conventional approaches to measurements of the scattered laser light. Integration of fiber optic detector probes into a fiber optic spectrometer offers considerable miniaturization of conventional light scattering spectrometers, which can be made arbitrarily small. In addition static and dynamic measurements of scattered light can be made within the scattering cell and consequently very close to the scattering center.