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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. 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
  8. Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow

    This manuscript presents a complete framework for the development and verification of physics-informed neural networks with application to the alternating-current power flow (ACPF) equations. Physics-informed neural networks (PINN)s have received considerable interest within power systems communities for their ability to harness underlying physical equations to produce simple neural network architectures that achieve high accuracy using limited training data. The methodology developed in this work builds on existing methods and explores new important aspects around the implementation of PINNs including: (i) obtaining operationally relevant training data, (ii) efficiently training PINNs and using pruning techniques to reduce their complexity, and (iii) globallymore » verifying the worst-case predictions given known physical constraints. Here, the methodology is applied to the IEEE-14 and 118 bus systems where PINNs show substantially improved accuracy in a data-limited setting and attain better guarantees with respect to worst-case predictions.« less
  9. Physics-constrained graph modeling for building thermal dynamics

    In this paper, we propose a graph model embedded with compact physical equations for modeling the thermal dynamics of buildings. The principles of heat flow across various components in the building, such as walls and doors, fit the message-passing strategy used by Graph Neural networks (GNNs). The proposed method is to represent the multi-zone building as a graph, in which only zones are considered as nodes, and any heat flow between zones is modeled as an edge based on prior knowledge of the building structure. Furthermore, the thermal dynamics of these components are described by compact models in the graph.more » GNNs are further employed to train model parameters from collected data. During model training, our proposed method enforces physical constraints (e.g., zone sizes and connections) on model parameters and propagates the penalty in the loss function of GNN. Such constraints are essential to ensure model robustness and interpretability. We evaluate the effectiveness of the proposed modeling approach on a realistic dataset with multiple zones. The results demonstrate a satisfactory accuracy in the prediction of multi-zone temperature. Moreover, we illustrate that the new model can reliably learn hidden physical parameters with incomplete data.« less
  10. Earth: Extinguishing anthropogenic risks through harmonization

    Human diseases can kill one person at a time, but the COVID-19 pandemic showed massacres could be possible. The climate crisis could be even worse, potentially leading to a bigger number of deaths of the human species and all living systems on Earth. I urge us to change our human-focused mindset to solve many problems, including the climate crisis, which humans caused to the entire ecosystems due to our arrogance: humans own this world. In this perspective article, I propose four recommendations to address climate issues through paradigm change and safe and sustainable technologies.
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