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Evolution of Mass Spectrometers for High m / z Biological Ion Formation, Transmission, Analysis and Detection: A Personal Perspective
Mass spectrometry (MS) has become an essential tool in virtually all academic, pharmaceutical, and biopharmaceutical analytical laboratories. The specialized and bespoke area of MS research and application of high m / z ion (> m / z 6000 and high mass, >150 kDa) formation, transmission, analysis, and detection is a relatively new area of focus for MS that has seen dramatic acceleration in interest over the last two decades. Herein we delve into this exciting aspect of MS, discussing how MS instrumentation has been refined and evolved for native-MS analysis. We cover the early groundbreaking experiments showing high m / z ion formation, transmission, and preservation of protein structure in the gas phase. Additionally, we discuss specific instrument optimizations and modifications that have advanced high m / z ion generation, transmission, analysis, and detection, contributing to the research area known as gas-phase structural biology. Native-MS sample introduction methods, emerging technologies, and future perspectives are also examined. Finally, we share personal opinions, observations, and experiences that are new to the community or previously unpublished.
Reduction of Acceleration to Effectively Microgravity Levels
Pressure gradients, i.e. pressure head, and buoyancy driven convection can be greatly decreased in experimental systems by acceleration of the laboratory reference frame at a rate consistent with the acceleration due to gravity. This may be done in a number of ways, the best known of which is the use of orbiting spacecraft. Other techniques include the use of aircraft following an appropriate parabolic trajectory or drop towers. The result is an experimental condition in which fluids experience virtually no outside forces relative to the laboratory reference frame. Such conditions are appropriate for the study of processes with diffusion dominated heat and/or mass Under, the study of phase transitions in the absence of pressure gradients, the study of solutal or thermal capillary convection, or containerless processes. Ways of achieving these conditions and complexities that arise in performing experiments in this environment are discussed.
Virtual-diagnostic-based time stamping for ultrafast electron diffraction
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Measuring the Beam-Spin Asymmetry of Hard Exclusive ¿0 Production off the Neutron with CLAS12
Over the course of the last couple of decades, Generalised Parton Distributions (GPDs) have become a compelling area of focus in both theoretical and experimental research in the ?eld of hadronic structure. GPDs can be indirectly accessed via observables in hard exclusive reactions such as Deeply Virtual Compton Scattering (DVCS) and Deeply Virtual Meson Production (DVMP). In these reactions, an incident lepton scatters o? an individual quark within the target nucleon via the exchange of a virtual photon, and a real photon (DVCS) or a meson (DVMP) are emitted as a result. This thesis presents an analysis of Deeply Virtual ?0 Meson Production o? the neutron in the deuteron (nDV?0P). The data were taken with the CLAS12 detector, which is housed in the experimental Hall B at the Je?erson National Laboratory (JLab) in Virginia, USA. A longitudinally polarised electron beam delivered by JLab?s Continuous Electron Beam accelerator Facility (CEBAF), at an energy of 10.2 and 10.6 GeV, was incident on a liquid deuterium target housed within the CLAS12 detector assembly. The nDV?0P channel is sensitive to the lesser-studied chiral-odd transversity GPDs. GPDs are accessed indirectly via observables related to the cross-sections of hard exclusive processes. One such example is the beam-spin asymmetry (?LU) as a function of Trento-?, the angle between the leptonic plane and the hadronic plane which are de?ned with the beam and scattered electron, and with the recoiling neutron and reconstructed ?0, respectively. Despite the limited statistics, the observable ?LU was successfully extracted for eight kinematic bins (four in Mandelstam ?, and two in ??). This is a ?rst measurement for nDV?0P in the phase-space which is now available with CLAS12 in the 12 GeV era of JLab.
Gamma-ray bursts from sheared Alfven waves
The physical process by which sheared Alfven waves can accelerate electrons to a Lorentz factor of 10,000 to 100,000 within 5 km of the stellar surface is applied to a study of gamma-ray bursts, taking both resonant and nonresonant scattering into account. Several very encouraging features of the model are discussed. Although the field is oscillatory, virtually all the charges are ejected from the system, resulting in very little backheating of the stellar surface. The particle number density is accounted for naturally in terms of BA0 and m, which in principle are known from the physical manifestation of the agent causing the crustal disturbance. The resulting gamma-ray spectrum compares very favorably with the observation. The model restricts the geometry of the emission region, in the sense that only the Compton upscattering of soft photons from a warm polar cap can produce the correct spectral shape.
High-Performance Accelerator Modeling: Toward Improving Controls and Diagnostics for High-Brightness Beams in Experiment
One of the main charges of beam physics is to improve the overall beam quality delivered to experiments and applications, which is generally quantified by the beam brightness. High brightness beams have numerous applications, including x-ray free electron lasers and ultrafast electron diffraction (UED). To these ends, this thesis details efforts to use high-performance models --- high-fidelity models that execute quickly --- for controls and diagnostics. To demonstrate the generality of these techniques, this thesis focuses on three of the most successful photoinjector designs currently used worldwide: the UCLA/SLAC/BNL-type high-gradient S-band gun, the continuous-wave high-repetition rate VHF APEX gun and the L-band DESY-PITZ-type gun. Work will be shown from Pegasus (UCLA), HiRES (LBNL) and FAST (FNAL).Specifically, data-driven models for online virtual diagnostics are presented, in this case, in the context of UED at HiRES, leading to a temporal resolution improvement. Methods for improving the fidelity of physics-based models are discussed, with examples at HiRES, Pegasus and FAST. Markov-chain Monte Carlo analysis is applied to match simulations, in the context of photocathode studies at HiRES and Pegasus. Lastly, the augmentation of online model-based predictions with model-independent optimization is explored in a fluctuating environment at Pegasus and HiRES. A central theme of this dissertation is working with parameter fluctuations when modeling an accelerator beamline. Long-term drifts and shot-to-shot jitter exist in every accelerator to a varying degree and therefore play an important role in every chapter of this dissertation. This thesis attempts to address the issues associated with these fluctuations when trying to develop faithful model representations of the system.
Map Applications to Target Exascale Architecture with Machine-Specific Performance Analysis, Including Challenges and Projections
This Exascale Computing Project (ECP) milestone report summarizes the status of all 30 ECP Applications Development (AD) subprojects at the end of FY20. In October and November of 2020, a comprehensive assessment of AD projects was conducted by the ECP leadership. Reviews occurred virtually between October 27, 2020 and November 12, 2020. The review committee—consisting of the AD lead, deputy, and L3—was tasked with evaluating each subproject’s progress in porting their codes to early exascale architectures considered precursors to the planned exascale machines. This includes characterizing which modules have been ported to multi-accelerator nodes, initial performance analyses, the status of software integration, and a current vision of successes, obstacles, and next steps. As such, this report contains not only an accurate snapshot of each subproject’s current status but also represents an unprecedentedly broad account of experiences in porting large scientific applications to next-generation high-performance computing architectures.
A multigrid and upwind viscous flow solver on 3-D embedded and overlapped grids
A numerically efficient method is presented for solving the three-dimensional governing equations of the viscous compressible flow about complex configurations with topologically different components. The physical domain is decomposed into regions for which the grid generation is relatively simple and virtually with no significant restrictions. The Navier-Stokes equations are solved by an implicit, approximately factored, upwind, finite-volume scheme. The block inversions and the diagonalized scalar inversions of the coefficient matrices are modified to allow the holes created in the computational domain by the embedded and overlapped grids. The convergence is accelerated by a multigrid algorithm despite the existence of such holes. The solution for s supersonic flow past a blunt-nose-cylinder at high angle-of-attack is obtained using a C-O grid embedded in a global Cartesian grid.
Improving Control of Two Motor Controllers
A computer program controls motors that drive translation stages in a metrology system that consists of a pair of two-axis cathetometers. This program is specific to Compumotor Gemini (or equivalent) motors and the Compumotor 6K-series (or equivalent) motor controller. Relative to the software supplied with the controller, this program affords more capabilities and is easier to use. Written as a Virtual Instrument in the LabVIEW software system, the program presents an imitation control panel that the user can manipulate by use of a keyboard and mouse. There are three modes of operation: command, movement, and joystick. In command mode, single commands are sent to the controller for troubleshooting. In movement mode, distance, speed, and/or acceleration commands are sent to the controller. Position readouts from the motors and from position encoders on the translation stages are displayed in marked fields. At any time, the position readouts can be recorded in a file named by the user. In joystick mode, the program yields control of the motors to a joystick. The program sends commands to, and receives data from, the controller via a serial cable connection, using the serial-communication portion of the software supplied with the controller.
Spaceflight Holography Investigation in a Virtual Apparatus
The movement of a particle in a fluid is one of the most fundamental processes in physics and plays an important role in materials science. For example, the physics of crystal growth from a nucleation site in solution can be dominated by movement of the solution over the crystal surface. Gravity usually dominates the equations of motion, but in microgravity other terms can dominate, making the equation much more complex. Until recently, the equations were solved only by numerical methods and/or by neglecting terms. During this study, we discovered an exact solution to the equations, which shows that the usually neglected terms become extremely important in microgravity. We also developed diagnostic recording methods using holography to save all of the particle field data, allowing the experiment to essentially be transferred from space back to earth in what we call the virtual apparatus . We will record holograms of particle distributions in motion in microgravity and bring them back to earth for analysis, allowing the study of the full three-dimensional motion of sets of particles, allowing us to test the new analytical solutions. The experiment will also provide accurate measurements of the quasi-steady acceleration of the space platform and other interesting microgravity and g-jitter effects. This program will produce the flight definition for an experiment in the microgravity environment of space to validate the theoretical model. We will design an experiment with the help of the theoretical model that is optimized for testing the model, measuring g, g-jitter, and other microgravity phenomena.
Emerging materials intelligence ecosystems propelled by machine learning
We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.
From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases
Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.
Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation
High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.
Artificial Intelligence-Assisted Design and Virtual Diagnostic for the Initial Condition of a Storage-Ring-Based Quantum Information System
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SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment
Presentation material for a paper presented at the GHGT-17 conference, Calgary, Canada, October 20-24, 2024. The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.
SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment
The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.
Digital Twins for Nuclear Power Plants and Facilities
The nuclear digital twin (DT) is the virtual representation of a nuclear energy system across its lifecycle. The nuclear DT uses real-time information and other data sources to improve the process of design, licensing, construction, security, O&M, decommissioning, and waste disposal. By leveraging the knowledge base and experience from the past 40 years of LWR operation, the nuclear DT is helping to accelerate the development and deployment of advanced nuclear technology in areas of passive safety, new fuel forms, instrumentation, and reactor control. For the currently operating nuclear fleet, DTs are reducing the operational risks, increasing plant availability, increasing energy capability, and reducing electricity production costs. For advanced fission and fusion reactors, DTs are being used to design for passive safety and built-in security-by-design. Rapidly deployable small modular reactor (SMR) and microreactor designs compatible with modular construction techniques and advanced manufacturing will be the new normal, reducing the need for large capital expenditures and compressing construction schedules. In addition, lower operational and maintenance costs will be realized by reducing the complexity of operations, staffing needs, and maintenance-related activities.