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  1. The hydration, microstructure, and mechanical properties of vaterite calcined clay cement (VC3)

    Limestone (calcite) calcined clay cement (LC3) is a promising low-CO2 binder, but the low activity of calcite cannot compensate the reduction in clinker factor, resulting in low one-day strength and limiting its broad applications. As recent carbon capture and utilization technologies allow scalable production of vaterite, a more reactive CaCO3 polymorph, we overcome the challenge by introducing vaterite calcined clay cement (VC3), inspired by the vaterite-calcite phase change. In the present study, VC3 exhibits higher compressive strengths and faster hydration than LC3. Compared to hydrated LC3, hydrated VC3 exhibits increased amount of hemi- and mono-carboaluminate formation and decreased amount ofmore » strätlingite formation. With gypsum adjustment, the 1-day strength of VC3 is higher than that of pure cement reference. Finally, VC3, a low-CO2 binder, presents great potential as a host of the metastable CaCO3 for carbon storage and utilization and as an enabler of carbon capture at gigaton scales.« less
  2. Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds

    Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production. These operations offer an especially useful casemore » to study as several management practices were implemented to reduce emissions (e.g., electrified, grid-powered drill rigs and closed loop fluid handling systems to reduce truck traffic and limit fluid handling on the pad). With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C8–C10 n-alkanes during drilling operations highlighted the importance of VOC emissions from synthetic drilling mud chosen to reduce odor impacts. More than 90 samples were collected of transient plumes. Using composition measurements, meteorological data, and information about well pad activities, these plumes were connected with specific UOGD operations including drilling, flowback, and production equipment maintenance. The chemical signatures of these plumes differed by operation type (e.g., C8–C10 n-alkanes constituted a larger fraction of measured VOCs in drilling-related plumes). Concentrations of individual, oil and gas-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Because these plumes typically impact a monitoring site for just several minutes, they are easily missed by slower-responding instruments. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations.« less
  3. Operational and Mission Highlights A Monthly Summary of Top Achievements December 2023

    This report contains a summary of highlights and top achievements from Los Alamos National Laboratory from December 2023, including Nuclear Security, Science Technology & Engineering, Mission Operations, and Community Relations.
  4. Stochastic finite volume method for uncertainty quantification of transient flow in gas pipeline networks

    We develop a weakly intrusive framework to simulate the propagation of uncertainty in solutions of generic hyperbolic partial differential equation systems on graph-connected domains with nodal coupling and boundary conditions. The method is based on the Stochastic Finite Volume (SFV) approach and can be applied for uncertainty quantification (UQ) of the dynamical state of fluid flow over actuated transport networks. The numerical scheme has specific advantages for modeling intertemporal uncertainty in time-varying boundary parameters, which cannot be characterized by strict upper and lower (interval) bounds. We describe the scheme for a single pipe, and then formulate the controlled junction Riemannmore » problem (JRP) that enables the extension to general network structures. In conclusion, we demonstrate the method's capabilities and performance characteristics using a standard benchmark test network.« less
  5. Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions

    In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolutionmore » of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.« less
  6. AI for Technoscientific Discovery: A Human-Inspired Architecture

    We present a high-level architecture for how artificial intelligences might advance and accumulate scientific and technological knowledge, inspired by emerging perspectives on how human intelligences advance and accumulate such knowledge. Agents advance knowledge by exercising a technoscientific method—an interacting combination of scientific and engineering methods. The technoscientific method maximizes a quantity we call “useful learning” via more-creative implausible utility (including the “aha!” moments of discovery), as well as via less-creative plausible utility. Society accumulates the knowledge advanced by agents so that other agents can incorporate and build on to make further advances. The proposed architecture is challenging but potentially complete:more » its execution might in principle enable artificial intelligences to advance and accumulate an equivalent of the full range of human scientific and technological knowledge.« less
  7. Recovery of valuable metals from electronic waste using a novel ammonia-based hydrometallurgical process

    The growing quantity of waste electrical and electronic equipment (WEEE), also known as electronic waste (E-waste), has been an area of growing public concern. The abundance of valuable metals contained in waste printed circuit boards (WPCBs) has made it a promising secondary resource, especially for Cu and Au. Although the recovery of metals from WPCBs via hydrometallurgical routes has been studied extensively over the past 20 years, most of the research has been limited in the laboratory. In current hydrometallurgical processes, strong acids and expensive oxidizers are often used to ensure a high recovery of metals without considering the sustainabilitymore » aspects of the environment and economics. To improve upon current hydrometallurgical offerings, the current study seeks to develop an energy-saving, environmentally friendly, economic and sustainable process to efficiently recover the valuable metals from real-world end-of-life WPCBs. The new contributions presented in this study are 1) design and evaluation of a comprehensive hydrometallurgical flowsheet; 2) employment of real end-of-life PCBs as feed materials in an investigation on Cu-NH3 leaching kinetics; 3) further application of kinetic model on a counter flow process simulation; and 4) evaluation of the influences by co-existing metals in Au-S2O3 leaching and recommendation for favorable leaching conditions.« less
  8. Quantum Annealing for Real-World Machine Learning Applications

    Optimizing the training of a machine learning pipeline is important for reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications have shown interesting results especially under the conditions where the performance of classical machine learning techniques are limited such as limited training data andmore » high dimensional features. This chapter explores the application of D-Wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, security, computational biology, biomedical sciences, and physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.« less
  9. Inverter-Based Layered Volt-VAR Control Scheme for High-Penetration Microgrids

    Inverter-based microgrid control architectures hold immense significance in addressing future power system stability challenges of distribution systems. As our energy landscape transitions towards greater reliance on intermittent renewable sources such as solar and wind, advanced layered inverter-based volt VAR control schemes offer localized and economical distributed voltage transient stability. The transient scenario test results conducted at the Idaho National Laboratory’s (INL) Microgrid Testbed identify a practical approach to achieve stability without requiring major infrastructure upgrades on distribution feeders. Our research findings illustrate the impact of inverter-based volt-VAR controls with a localized battery energy storage system (BESS) to provide flexibility andmore » fast transient response during voltage transients typically introduced from large load changes. Utilizing standard photovoltaic PV installations consisting of a BESS with layered volt-VAR control methodology can provide flexible voltage and power charge and discharge support to local consumers and connected utilities to create a sustainable distribution grid, mitigating interconnection disturbances.« less
  10. Construction of conjugated scaffolds driven by mechanochemistry towards energy storage applications

    Mechanochemistry has been recognized as an efficient and sustainable methodology to provide a unique driven force and reaction environments under ambient and neat conditions for the construction of functionalized materials possessing promising properties. Among them, highly porous conjugated scaffolds with attractive electronic conductivities and high surface areas are one of the representative categories exhibiting diverse task-specific applications, especially in electrochemical energy storage. In recent years, the mechanochemistry-driven procedures have been deployed to construct conjugated scaffolds with engineered structures and properties leveraging the tunability in chemical structures of building blocks and polymerization capability of diverse catalysts. Therefore, a thorough review ofmore » related works is required to gain an in-depth understanding of the mechanochemical synthesis procedure and property-performance relationship of the as-produced conjugated scaffolds. Herein, the mechanochemistry-driven construction of conjugated porous networks (CPNs), the carbon-based materials (e.g., graphite and graphyne), and carbon supported single atom catalysts (CS-SACs) are discussed and summarized. The electrochemical performance of the afforded conductive scaffolds as electrode materials in supercapacitors and alkali-ion batteries is elucidated. Finally, the challenges and potential opportunities related to the construction of conjugated scaffolds driven by mechanochemistry are also discussed and concluded.« less
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