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At least 1,585 records · Page 88

Toward accelerating rare-earth metal extraction using equivariant neural networks

The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their thio-analogues, selected for their established efficacy in binding these elements. Trained on this valuable resource, our Allegro models demonstrate excellent performance, particularly when trained to directly predict DFT-level binding energies, yielding highly accurate results that closely correlate with theoretical calculations on a diverse test set. Furthermore, this strategy exhibited strong out-of-sample generalization, accurately predicting binding energies for an isomeric HOPO-derivative ligand not seen during training. By substantially reducing computational demands, this machine learning framework, alongside the provided dataset, represent powerful tools to accelerate the high-throughput screening and rational design of novel ligands for efficient rare-earth metal separation.

Gupta, Ankur K. [Lawrence Berkeley National Labora↗

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗

Sub-micrometer random-pyramid texturing of silicon solar wafers with excellent surface passivation and low reflectance

Select emerging solar cell technologies, ranging from very narrow screen-printed fingers to perovskite/silicon tandems, would benefit from a reduction of the height of the standard pyramidal texture of monocrystalline silicon wafers to below 1 μm. However, fabricating such small surface features usually compromises the requisite low reflectance and high passivation quality. Through systematic design of experiments, we demonstrate that conventional alkaline-based wet-chemical etching processes can be tuned to produce dense, spatially homogeneous, and uniformly sized sub-micrometer pyramids. Precise texture size can be further controlled by adding potassium silicate to the texturing solution to effectively slow the pyramid growth rate and promote nucleation. With pyramid size distribution data—extracted from multiple atomic force microscopy images across each wafer—we demonstrate 16 nanotextures with average pyramid sizes ranging from 62 to 512 nm, with the largest having only 0.3% of its pyramids taller than 1 μm. Six of the nanotextures exhibit light trapping and surface defect densities comparable to state-of-the-art microtextured wafers. In conclusion, the best nanotexture achieved an AM1.5G-weighted reflectance of 11.8% and a minority-carrier lifetime of 3.4 ms after passivation with amorphous silicon, demonstrating that smaller textures need not deteriorate solar cell performance.

14 SOLAR ENERGY↗

Effects of acute ethanol exposure on cytokine production by primary airway smooth muscle cells

Both chronic and binge alcohol abuse can be significant risk factors for inflammatory lung diseases such as acute respiratory distress syndrome and chronic obstructive pulmonary disease. However, metabolic basis of alcohol-related lung disease is not well defined, and may include key metabolites of ethanol [EtOH] in addition to EtOH itself. Therefore, we investigated the effects of EtOH, acetaldehyde [ACE], and fatty acid ethyl esters [FAEEs] on oxidative stress, endoplasmic reticulum (ER) stress, AMP-activated protein kinase (AMPK) signaling and nuclear translocation of phosphorylated (p)-NF-κB p65 in primary human airway smooth muscle (HASM) cells stimulated to produce cytokines using LPS exposure. Both FAEEs and ACE induced evidence of cellular oxidative stress and ER stress, and increased p-NF-κB in nuclear extracts. EtOH and its metabolites decreased p-AMPKα activation, and induced expression of fatty acid synthase, and decreased expression of sirtuin 1. In general, EtOH decreased secretion of IP-10, IL-6, eotaxin, GCSF, and MCP-1. However, FAEEs and ACE increased these cytokines, suggesting that both FAEEs and ACE as compared to EtOH itself are proinflammatory. A direct effect of EtOH could be consistent with blunted immune response. Collectively, these two features of EtOH exposure, coupled with the known inhibition of innate immune response in our model might explain some clinical manifestations of EtOH exposure in the lung. - Highlights: • Metabolic basis for EtOH toxicity was studied in human airway smooth muscle (HASM) cells. • In HASM cells, EtOH metabolites were found to be relatively more toxic than EtOH itself. • EtOH metabolites mediate deactivation of AMPK via oxidative stress and ER stress. • EtOH metabolites were found to be more proinflammatory than EtOH itself in HASM cells.

60 APPLIED LIFE SCIENCES↗

Direct Extraction of Uniform Lignin Microspheres from Bamboo Using a Polyol-Based Deep Eutectic Solvent

Conventional preparation of lignin micro-/nano-particles (LMNPs) often needs multiple steps accompanied by high cost, low efficiency, and low yield. Herein, this study established a polyhydric alcohol-based deep eutectic solvent (PA-DES) fractionation for direct LMNP assembly from raw lignocellulose. The results indicated that lignin was significantly extracted by 41.55–70.33% depending on the PA-DES varieties. With lignin removal, the substrate showed high glucan enzymatic saccharification from 70.44 to 100%. Specially, over 90% lignin could be recovered in each PA-DES, and the obtained LMNPs showed quite uniform spherical morphology with a tunable size of 550–3254 nm and narrow polydispersity index (PDI) of 0.295–0.398. The proposed DES showed a near theoretical recovery yield, which still featured excellent fractionation performance even after three cycles of reuse. In conclusion, the findings highlight the simultaneous utilization of cellulose and lignin in a one-pot biomass fractionation under mild conditions with sugars and LMNPs as the coproducts.

36 MATERIALS SCIENCE↗

Evidence of restricted heat transport in National Ignition Facility Hohlraums

We present experimental evidence of restricted electron thermal conduction in the high-Z coronal plasma regions of laser-driven Hohlraums on the National Ignition Facility. Four separate measurements, three of which are direct observations of Hohlraum dynamics, corroborate this finding. (1) The velocity of the coronal plasma ablated and heated by the outer-cone laser beams is determined by time-dependent imaging of the gold plasma plume, or “bubble.” The velocities of the incoming plume (perpendicular to the Hohlraum axis) are consistent with high-fidelity 2D radiation-hydrodynamic simulations using flux-limited thermal electron conduction with a flux multiplier f = 0.03. Simulations using f = 0.15, which is very nearly classical Spitzer–Härm transport, predict plume velocities slower than measured. (2) Specific features in time-resolved images of the Hohlraum wall at an angle of 19° are also more consistent with f = 0.03 simulations compared to f = 0.15. (3) Spectroscopic tracers were added to the Hohlraum wall in the outer-beam bubble region. The ratios of hydrogen-like to helium-like line emission are sensitive to the electron temperature of the bubble. The hydrogen-like to helium-like ratios extracted from the time-integrated spectra of manganese and cobalt tracers from two observation angles are consistent with f = 0.03 and not with f = 0.15. (4) The time of peak capsule emission, or “bang time,” an integrated measurement, is also more consistent with f = 0.03 than with f = 0.15. Overall, while these findings do not identify the causes of restricted thermal conduction in Hohlraums, they motivate future experiments to test specific hypotheses and focus on model development in the regions of the plasma exhibiting restricted transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A new Devonian euthycarcinoid reveals the use of different respiratory strategies during the marine-to-terrestrial transition in the myriapod lineage

Myriapods were, together with arachnids, the earliest animals to occupy terrestrial ecosystems, by at least the Silurian. The origin of myriapods and their land colonization have long remained puzzling until euthycarcinoids, an extinct group of aquatic arthropods considered amphibious, were shown to be stem-group myriapods, extending the lineage to the Cambrian and evidencing a marine-to-terrestrial transition. Although possible respiratory structures comparable to the air-breathing tracheal system of myriapods are visible in several euthycarcinoids, little is known about the mechanism by which they respired. Here, we describe a new euthycarcinoid from Upper Devonian alluvio-lagoonal deposits of Belgium. Synchrotron-based elemental X-ray analyses were used to extract all available information from the only known specimen. Sulfur X-ray fluorescence (XRF) mapping and spectroscopy unveil sulfate evaporation stains, spread over the entire slab, suggestive of a very shallow-water to the terrestrial environment prior to burial consistent with an amphibious lifestyle. Trace metal XRF mapping reveals a pair of ventral spherical cavities or chambers on the second post-abdominal segment that do not compare to any known feature in aquatic arthropods, but might well play a part in air-breathing. Our data provide additional support for amphibious lifestyle in euthycarcinoids and show that different respiratory strategies were used during the marine-to-terrestrial transition in the myriapod lineage.

59 BASIC BIOLOGICAL SCIENCES↗

Microscale THMC Modeling of Pressure Solution in Salt Rock: Impacts of Geometry and Temperature

Abstract Pressure solution, a mechanism that involves tight coupling between the geometry and thermal-hydro-mechanical-chemical (THMC) processes, plays an important role in diagenesis. In this study, we make the first attempt to conduct microscale THMC modeling to understand and quantify the impacts of geometry and temperature on pressure solution, taking natural salt rock as an example. This modeling capability is achieved by expanding a novel MC code that we developed previously (Hu et al. J Geophys Res: Solid Earth 126:e2021JB023112, 2021) to include temperature effects. We first conduct a simulation of an example that involves a single brine inclusion within a single halite grain and find that the temperature impact is limited for that case. We then extract geometry from an image of a natural salt rock and conduct simulations with different cases: (A) only temperature and no stress, (B) only stress and no temperature, and (C) with both stress and temperature. These different cases result in quite different phenomena. In case A, dissolution and precipitation occur across the entire system due to isolated pore space reaching a localized mass balance between dissolution, precipitation, and diffusion. In case B, intense geometric features (e.g., major asperities, inclusions) in one area undergo stress concentration, thus dominating pressure solution in that area. In case C, pressure solution is spread out at contacting highly stressed geometric features close to the hotter side. We conclude that geometric features dominate stress distribution, thus dominating pressure solution in a natural salt rock that may be affected by the temperature if a sufficient temperature gradient is applied.

58 GEOSCIENCES↗

Deciphering Reaction Mechanisms of Molecular Proton Reduction Catalysts with Cyclic Voltammetry: Kinetic vs Thermodynamic Control

The kinetics and thermodynamics of elementary reaction steps involved in the catalytic reduction of protons to hydrogen define the reaction landscape for catalysis. The mechanisms can differ in the order of the elementary proton transfer, electron transfer, and bond-forming steps and can be further differentiated by the sites at which protons and electrons localize. Access to fully elucidated mechanistic, kinetic, and thermochemical details of molecular catalysts is crucial to facilitate the development of new catalysts that operate with optimal efficiency, selectivity, and durability. The mechanism by which a catalyst operates, as well as the kinetics and thermodynamics associated with the individual steps, can often be accessed through electroanalytical studies. Here, this Account details the application of cyclic voltammetry to interrogate reaction mechanisms and quantify the kinetics and thermodynamics of elementary reaction steps for a series of molecular catalysts that mediate electrochemical proton reduction. I distinguish the limiting scenarios wherein a catalyst operates under kinetic control vs thermodynamic control, with a focus on detecting how cyclic voltammetry features shift with proton source strength and concentration, as well as scan rate. For systems that operate under kinetic control, catalytic currents are observed at, or slightly positive toward, the formal potential for the redox process that triggers catalysis. Under thermodynamic control, catalytic responses shift as a function of the proton source pKa and effective pH of the solution. After drawing this distinction, we introduce the appropriate voltammetry experiments and accompanying analytical expressions for extracting key metrics from the data. To illustrate analytical strategies to quantify elementary reaction steps of catalysts operating under kinetic control, I describe our studies of proton reduction catalysts Co(dmgBF 2 ) 2 (CH 3 CN) 2 (dmgBF 2 = difluoroboryl-dimethylglyoxime) and [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ (P 2 Ph N 2 Ph = 1,5-phenyl-3,7-phenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, peak shift analysis, foot-of-the-wave analysis, and plateau current analysis are applied to data sets wherein voltammetric response are recorded as a function of catalyst concentration, proton source concentration, proton source strength, and scan rate to quantify rate constants for elementary proton transfer and bond-forming steps in a catalytic cycle. Further, the case study of [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ illustrates how complementary spectroscopic methods can bolster the mechanistic assignment. Collectively, these two studies showcase how detailed mechanistic studies inform on rate-limiting elementary steps in catalysis and other key processes underpinning catalysis. Second, I present analytical strategies to interrogate catalysts operating under thermodynamic control, centered on the case study of [Ni II (P 2 Ph N 2 Bn ) 2 ] 2+ (P 2 Ph N 2 Bn = 1,5-dibenzyl-3,7-diphenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, the application of nonaqueous Pourbaix theory to extract thermodynamic information is introduced, and the construction of a coupled Pourbaix diagram is detailed. This study identifies ligand-based protonation as the key process that places catalysis under thermodynamic control and influences the reaction mechanism. Together, the work detailed in this Account showcases the utility of electroanalytical methods to disentangle complex reaction mechanisms and extract key thermochemical and kinetic parameters for elementary steps of catalysis. Through detailed presentation of the key analytical expressions that underpin these analyses, this Account seeks to facilitate the adoption of cyclic voltammetry by the community to fully extract kinetic, thermochemical, and mechanistic information on electrochemical small-molecule activation.

catalysts↗

Nondestructive flash cathode recycling

Effective recycling of end-of-life Li-ion batteries (LIBs) is essential due to continuous accumulation of battery waste and gradual depletion of battery metal resources. The present closed-loop solutions include destructive conversion to metal compounds, by destroying the entire three-dimensional morphology of the cathode through continuous thermal treatment or harsh wet extraction methods, and direct regeneration by lithium replenishment. Here, we report a solvent- and water-free flash Joule heating (FJH) method combined with magnetic separation to restore fresh cathodes from waste cathodes, followed by solid-state relithiation. The entire process is called flash recycling. This FJH method exhibits the merits of milliseconds of duration and high battery metal recovery yields of ~98%. After FJH, the cathodes reveal intact core structures with hierarchical features, implying the feasibility of their reconstituting into new cathodes. Relithiated cathodes are further used in LIBs, and show good electrochemical performance, comparable to new commercial counterparts. Life-cycle-analysis highlights that flash recycling has higher environmental and economic benefits over traditional destructive recycling processes.

25 ENERGY STORAGE↗

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗

Electric microfields in dense carbon-hydrogen plasmas

Classical molecular dynamics is used to investigate stationary and time-dependent properties of microfields in hot, solid density, electron-ion plasmas. Even at the high temperatures considered here, such simulations require quantum statistical potentials (QSPs) to mimic the essential effects of diffraction and exchange symmetry for electrons. Fortunately, key results relevant to microfield distributions are found to be insensitive to different, plausible QSP choices. Atomic processes in plasmas will depend on the time average of the microfields. It is not clear, a priori, what the time duration of this average should be. The question of how best to extract the quasistatic (low-frequency) microfield from a classical molecular dynamics simulation is explored in some detail, and the time-averaging approach we adopt involves both plasma and atomic time scale constraints. One of the major findings described in the paper is that for a large time interval, the time-averaged microfield does not significantly change. Our discussion of this suite of large simulations for plasma mixtures focuses on understanding various features and trends revealed by data for C-H plasmas having carbon fractions ranging from 0.01 to 1, and different temperatures well above T Fermi .

74 ATOMIC AND MOLECULAR PHYSICS↗

Truck Platooning Performance with ADAS and Onboard Camera Data Describing Traffic Interactions

This project was part of the Characterizing Behaviors and Capabilities for Emerging Connected and Automated Vehicle Technologies, Sensors, and Connectivity project. The National Laboratory of the Rockies partnered with Cummins Inc. to collect data from Class 8 tractor trailer combinations in platoon (cooperative adaptive cruise control) operations on public roads in southern Indiana. Data collected include J1939 CAN bus, radar, intervehicle position, and video data. The video data could not be shared in the raw form, so they were processed to extract information on the other vehicles on the road, their relative positions, and intrusion events. This information was then columnized for modeling use and further enhanced by appending road information including road type, speed limit, altitude, and grade. The test route included free-flowing traffic, highway interchanges, and construction zones, as well as low-, medium-, and high-grade sections. Individual test conditions varied by day, with advanced driver-assistance system (ADAS) features engaged or disengaged and different combined vehicle masses tested in addition to uncontrolled variables such as weather and traffic interactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

IDAES-PSE 2.2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. New features in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗

Potential of electrolytic processes for recovery of molybdenum from molten salts for 99 Mo production

Molten salt reactor (MSR) technologies are receiving significant interest for commercialization because of their potential safety features and efficient energy production. In addition to producing reliable and clean energy, high-value radioisotopes could be harvested from MSRs as an additional revenue stream. Although significant quantities of 99 Mo will be produced in MSRs through fission, how accessible 99 Mo will be for direct recovery from a fuel salt is unclear. Electrolytic processes in fused salts have been used extensively for processing molten salts and have the potential to enable the extraction of 99 Mo directly from the fuel salt. This paper reviews the behavior of 99 Mo in the molten salt reactor experiment and summarizes theoretical aspects of the electrolytic process in high-temperature fused salt and the published experimental results on Mo extraction through electrolytic processes. Here, the general principle of fused-salt electrolytic processes is described, by electrodeposition of Mo with the following aspects: electrowinning, electrorefining, electroplating, and electroextraction with several experimental considerations. Finally, important considerations for recovery of 99 Mo from MSR relevant molten salts via electrodeposition are discussed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Time-resolved single-photon detection module based on silicon photomultiplier: A novel building block for time-correlated measurement systems

We present the design and preliminary characterization of the first detection module based on Silicon Photomultiplier (SiPM) tailored for single-photon timing applications. The aim of this work is to demonstrate, thanks to the design of a suitable module, the possibility to easily exploit SiPM in many applications as an interesting detector featuring large active area, similarly to photomultipliers tubes, but keeping the advantages of solid state detectors (high quantum efficiency, low cost, compactness, robustness, low bias voltage, and insensitiveness to magnetic field). The module integrates a cooled SiPM with a total photosensitive area of 1 mm{sup 2} together with the suitable avalanche signal read-out circuit, the signal conditioning, the biasing electronics, and a Peltier cooler driver for thermal stabilization. It is able to extract the single-photon timing information with resolution better than 100 ps full-width at half maximum. We verified the effective stabilization in response to external thermal perturbations, thus proving the complete insensitivity of the module to environment temperature variations, which represents a fundamental parameter to profitably use the instrument for real-field applications. We also characterized the single-photon timing resolution, the background noise due to both primary dark count generation and afterpulsing, the single-photon detection efficiency, and the instrument response function shape. The proposed module can become a reliable and cost-effective building block for time-correlated single-photon counting instruments in applications requiring high collection capability of isotropic light and detection efficiency (e.g., fluorescence decay measurements or time-domain diffuse optics systems).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Strain-modulated helimagnetism and emergent magnetic phase diagrams in highly crystalline MnP nanorod films

Here, we explore strain-modulated helimagnetism in highly crystalline MnP nanorod films grown on Si(100) substrates using molecular beam epitaxy. The strained MnP film exhibits a paramagnetic to ferromagnetic (FM) phase transition at $T_C$ ~ 279 K, and the FM to helical phase transition at $T_N$ ~ 110 K. The value of $T_N$ is greater than $T_N$ ~ 47K for the MnP single crystal, indicating strong strain-modulated helimagnetic states in the MnP nanorod film. The presence of significant thermal hysteresis in the helical phase indicates the coexistence of competing magnetic interactions, leading to the first-order metamagnetic transition. Similar to its single-crystal counterpart, an anisotropic magnetic effect is observed in the MnP film, which is independently confirmed by magnetic hysteresis loop and radio-frequency transverse susceptibility (TS) measurements. The evolution of the screw to the cone and fan phases is precisely tracked from magnetization vs magnetic field/temperature measurements. The temperature dependence of the anisotropy fields, extracted from the TS spectra, yields further insight into the competing nature of the magnetic phases. Unfolding of the different helical phases at $\textit{T}$ < 120 K (~$T_N$) is analyzed by the temperature- and field-dependent magnetic entropy change. Based on these findings, the comprehensive magnetic phase diagrams of the MnP nanorod film are constructed for both the in-plane and out of plane magnetic field directions, revealing emergent strain/dimensionality-driven helical magnetic features that are absent in the magnetic phase diagram of the MnP single crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗