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Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

Physical Review Applied 2025年第3期23卷 034035-034035页

作者： Niko R. Reed Danyal Bhutto Matthew J. Turner Declan M. Daly Sean M. Oliver Jiashen Tang Kevin S. Olsson Nicholas Langellier Mark J.H. Ku Department of Physics University of Maryland College Park Maryland USA Quantum Technology Center University of Maryland College Park Maryland USA Joint Quantum Institute University of Maryland College Park Maryland USA Department of Biomedical Engineering Boston University Boston Massachusetts USA A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA The MITRE Corporation McLean Virginia 22102 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Department of Physics Harvard University Cambridge Massachusetts USA Present address: VideaHealth Inc. Boston MA. Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Present address: Northrop Grumman Mission Systems Linthicum Maryland 21090 USA.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources.

关键词： Signal to noise ratio

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Light-driven C-H activation mediated by 2D transition metal dichalcogenides

arXiv

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arXiv 2022年

作者： Li, Jingang Zhang, Di Guo, Zhongyuan Jiang, Xi Larson, Jonathan M. Zhu, Haoyue Zhang, Tianyi Gu, Yuqian Blankenship, Brian Chen, Min Wu, Zilong Huang, Suichu Kostecki, Robert Minor, Andrew M. Grigoropoulos, Costas P. Akinwande, Deji Terrones, Mauricio Redwing, Joan M. Li, Hao Zheng, Yuebing Materials Science & Engineering Program Texas Materials Institute Walker Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States Laser Thermal Laboratory Department of Mechanical Engineering University of California BerkeleyCA94720 United States Tohoku University Sendai980-8577 Japan Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Chemistry and Biochemistry Baylor University WacoTX76798 United States Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Chandra Family Department of Electrical & Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Energy Storage and Distributed Resources Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Center for Two-Dimensional and Layered Materials The Pennsylvania State University University ParkPA16802 United States Department of Physics Department of Chemistry The Pennsylvania State University University ParkPA16802 United States 2D Crystal Consortium Materials Research Institute The Pennsylvania State University University ParkPA16802 United States

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials. © 2022, CC BY-NC-SA.

关键词： Chemical activation

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Magnetostructural Coupling at the Néel point in YNiO3 Single Crystals

arXiv

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arXiv 2024年

作者： Gawryluk, Dariusz J. Klein, Y. Maximilian Scatena, Rebecca Shang, Tian Sibille, Romain Sheptyakov, Denis Casati, Nicola Linden, Anthony Chernyshov, Dmitry Kozlowski, Miroslaw Dluzewski, Piotr Rossell, Marta D. Macchi, Piero Medarde, Marisa Laboratory for Multiscale Materials Experiments Paul Scherrer Institut Villigen PSI5232 Switzerland Department of Chemistry and Biochemistry University of Bern Freiestrasse 3 Bern3012 Switzerland Department of Physics Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom School of Physics and Electronic Science East China Normal University Shanghai200241 China Laboratory for Neutron Scattering and Imaging Paul Scherrer Institut Villigen PSI5232 Switzerland Swiss Light Source Paul Scherrer Institut Villigen PSI5232 Switzerland Department of Chemistry University of Zürich Winterthurerstrasse 190 Zürich8057 Switzerland European Synchrotron Radiation Facility CS40220 Grenoble38043 CEDEX 9 France Lukasiewicz Research Network Tele & Radio Research Institute 11 Ratuszowa Street Warsaw03-450 Poland Institute of Physics Polish Academy of Science Al. Lotników 32/46 Warsaw02-668 Poland Electron Microscopy Center Empa Swiss Federal Laboratories for Materials Science and Technology Dübendorf8600 Switzerland Department of Chemistry Materials and Chemical Engineering Politecnico di Milano Via Mancinelli 7 Milano20131 Italy

The recent discovery of superconductivity in infinite layer thin films and bulk Ruddlesden-Popper nickelates has stimulated the investigation of other predicted properties of these materials. Among them, the existence of magnetism-driven ferroelectricity in the parent compounds RNiO3 (R = 4f lanthanide and Y) at the onset of the Néel order, TN, has remained particularly elusive. Using diffraction techniques, we reveal here the existence of magnetostriction at TN in bulk YNiO3 single crystals. Interestingly, the associated lattice anomalies are much more pronounced along the b crystal axis, which coincides with the electric polarization direction expected from symmetry arguments. This axis undergoes an abrupt contraction below TN that reaches Δb/b ∼ - 0.01 %, a value comparable to those found in some magnetoresistive manganites and much larger than those reported for magnetism-driven multiferroics. This observation suggests a strong spin-lattice coupling in these materials, consistent with theoretical predictions. Using the symmetry-adapted distortion mode formalism we identify the main ionic displacements contributing to the lattice anomalies and discuss the most likely polar displacements below TN. Furthermore, our data support symmetric superexchange as the most likely mechanism responsible for the magnetoelastic coupling. These results, that may be common to the full RNiO3 family, provide new experimental evidence supporting the predicted existence of magnetism-driven ferroelectricity in RNiO3 perovskites. © 2024, CC BY-NC-ND.

关键词： Single crystals

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imaging Atomic-Scale Chemistry from Fused Multi-Modal Electron microscopy

arXiv

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arXiv 2022年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Fielitz, Alyssa J. Ha, Don-Hyung Perera, Sanjaya D. Baggari, Ismail El Robinson, Richard D. Fessler, Jeffrey A. Ophus, Colin Rozeveld, Steve Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Dow Chemical Co. MidlandMI United States Department of Material Science and Engineering Cornell University IthacaNY United States School of Integrative Engineering Chung-Ang University Seoul Korea Republic of Department of Physics Cornell University IthacaNY United States The Rowland Institute at Harvard CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic- resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials. © 2022, CC BY.

关键词： Electron microscopy

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Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images

arXiv

引用

arXiv 2024年

作者： Reed, Niko R. Bhutto, Danyal Turner, Matthew J. Daly, Declan M. Oliver, Sean M. Tang, Jiashen Olsson, Kevin S. Langellier, Nicholas Ku, Mark J.H. Rosen, Matthew S. Walsworth, Ronald L. Department of Physics University of Maryland College ParkMD United States Quantum Technology Center University of Maryland College ParkMD United States Joint Quantum Institute University of Maryland College ParkMD United States Department of Biomedical Engineering Boston University BostonMA United States A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Electrical and Computer Engineering University of Maryland College ParkMD United States The MITRE Corporation McLeanVA22102 United States Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College ParkMD20742 United States Department of Physics Harvard University CambridgeMA United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Harvard Medical School 25 Shattuck St. BostonMA United States

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM). Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources. © 2024, CC BY.

关键词： Magnetic fields

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Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

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ACS Nano 2025年第23期19卷 21538-21555页

作者： Cave, Joseph Christiono, Anne Schiavone, Carmine Pownall, Henry J. Cristini, Vittorio Staquicini, Daniela I. Pasqualini, Renata Arap, Wadih Brinker, C. Jeffrey Campen, Matthew Wang, Zhihui Van Nguyen, Hien Noureddine, Achraf Dogra, Prashant Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston 77030 TX United States Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York 10065 NY United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge 02139 MA United States Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy Department of Medicine Houston Methodist Houston 77030 TX United States Department of Medicine Weill Cornell Medicine New York 10065 NY United States Neal Cancer Center Houston Methodist Research Institute Houston 77030 TX United States Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston 77030 TX United States Rutgers Cancer Institute Newark 08901 NJ United States Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark 08901 NJ United States Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark 08901 NJ United States Department of Chemical and Biological Engineering University of New Mexico Albuquerque 87106 NM United States College of Pharmacy University of New Mexico Albuquerque 87106 NM United States Department of Physiology and Biophysics Weill Cornell Medical College New York 10065 NY United States Department of Electrical and Computer Engineering University of Houston Houston 77204 TX United States

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation. © 2025 The Authors. Published by American Chemical Society.

关键词： artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity PBPK

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THz Emission from Exchange-Coupled Fe/Ru/Ni Spintronic Emitters

THz Emission from Exchange-Coupled Fe/Ru/Ni Spintronic Emitt...

引用

International Conference on Infrared and Millimeter Waves

作者： R. Adam C. Greb D. E. Bürgler D. Cao S. Heidtfeld F. Wang J. Cheng D. Chakraborty I. Komissarov H. Hardtdegen M. Mikulics M. Büscher C. M. Schneider Roman Sobolewski Research Centre Jülich Peter Grünberg Institute (PGI-6) Jülich Germany Institut für Laser- und Plasmaphysik Heinrich Heine Universität Düsseldorf Düsseldorf Germany College of Physics Center for Marine Observation and Communications Qingdao University Qingdao China Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA Research Centre Jülich Ernst Ruska Centre for Microscopy and Spectroscopy with Electrons Jülich Germany Faculty of Physics University Duisburg-Essen Duisburg Germany Department of Physics University of California Davis Davis California USA

We explored THz emission from $\mathrm{Si}^{2} / \mathrm{SiO}_{2} / / \mathrm{Ta} / \mathrm{Fe} / \mathrm{Ru} /$ $\mathrm{Ni} / \mathrm{Al}_{2} \mathrm{O}_{3}$ spintronic emitters. We tuned magnetization alignment of Fe and Ni layers by varying the interlayer exchange coupling (IEC) strength using a range of Ru layer thickness t. Depending on IEC strength, magnetization hysteresis shows either ferromagnetic $(t=1.1 \mathrm{~nm}, 1.5 \mathrm{~nm})$, antiferromagnetic $(t=1.3 \mathrm{~nm})$ or canted $(t=1.7 \mathrm{~nm}, 1.9 \mathrm{~nm})$ relative alignment. Competition between IEC and an external magnetic field results in a dramatic difference in THz emission from the ferromagnetically (FM) and anti-ferromagnetically (AFM) coupled structures. The resulting THz emission from IEC structures is a result of an interference of THz transiens generated by the individual $\mathrm{Fe} / \mathrm{Ru}$ and $\mathrm{Ru} / \mathrm{Ni}$ emitters.

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Development and Applications of D-Amino Acid Derivatives-based Metabolic Labeling of Bacterial Peptidoglycan

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Angewandte Chemie 2024年第17期136卷

作者： Dr. Yongfang Zheng Xinyu Zhu Mingyi Jiang Dr. Fangfang Cao Dr. Qing You Prof. Xiaoyuan Chen Fujian-Taiwan Science and Technology Cooperation Base of Biomedical Materials and Tissue Engineering Engineering Research Center of Industrial Biocatalysis Fujian Provincial Key Laboratory of Advanced Materials Oriented Chemical Engineering Fujian Provincial Key Laboratory of Polymer Materials College of Chemistry and Materials Science Fujian Normal University 32 Shangsan Road Fuzhou 350007 P.R. China Departments of Diagnostic Radiology Surgery Chemical and Biomolecular Engineering and Biomedical Engineering Yong Loo Lin School of Medicine and Faculty of Engineering National University of Singapore Singapore 119074 Singapore Nanomedicine Translational Research Program NUS Center for Nanomedicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore

Peptidoglycan, an essential component within the cell walls of virtually all bacteria, is composed of glycan strands linked by stem peptides that contain D-amino acids. The peptidoglycan biosynthesis machinery exhibits high tolerance to various D-amino acid derivatives. D-amino acid derivatives with different functionalities can thus be specifically incorporated into and label the peptidoglycan of bacteria, but not the host mammalian cells. This metabolic labeling strategy is highly selective, highly biocompatible, and broadly applicable, which has been utilized in various fields. This review introduces the metabolic labeling strategies of peptidoglycan by using D-amino acid derivatives, including one-step and two-step strategies. In addition, we emphasize the various applications of D-amino acid derivative-based metabolic labeling, including bacterial peptidoglycan visualization (existence, biosynthesis, and dynamics, etc.), bacterial visualization (including bacterial imaging and visualization of growth and division, metabolic activity, antibiotic susceptibility, etc.), pathogenic bacteria-targeted diagnostics and treatment (positron emission tomography (PET) imaging, photodynamic therapy, photothermal therapy, gas therapy, immunotherapy, etc.), and live bacteria-based therapy. Finally, a summary of this metabolic labeling and an outlook is provided.

关键词： peptidoglycan metabolic labeling D-amino acid derivatives peptidoglycan and bacterial visualization pathogenic bacteria-targeted diagnostics and treatment

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The Mesoscale Crystallinity of Nacreous Pearls

Research Square

引用

Research Square 2021年

作者： Gim, Jiseok Koch, Alden Otter, Laura M. Savitzky, Benjamin H. Erland, Sveinung Estroff, Lara A. Jacob, Dorrit E. Hovden, Robert Department of Materials Science & Engineering University of Michigan Ann ArborMI United States Department of Biomedical Engineering University of Michigan Ann ArborMI United States Research School of Earth Sciences Australian National University CanberraACT2600 Australia National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Maritime Studies Western Norway University of Applied Sciences Haugesund Norway Department of Materials Science & Engineering Cornell University IthacaNY United States Applied Physics Program University of Michigan Ann ArborMI United States

A pearl’s distinguished beauty and toughness is attributable to the periodic stacking of aragonite tablets known as nacre. Nacre is a naturally occurring mesocrystal that remarkably arises in the absence of translational symmetry. Gleaning the inspiring biomineral design of a pearl requires quantifying its structural coherence and understanding the stochastic processes that govern formation. By characterizing the entire structure of pearls (~3 mm) in cross-section at high resolution, we show nacre is a medium-range mesocrystal formed through nanoparticle assembly processes. Self-correcting growth mechanisms actively remedy disorder and topological defects of the tablets and act as a countervailing force to paracrystallinity (i.e. long-range disorder). Nacre has a correlation length of roughly 16 tablets (~5.5 µm) despite persistent fluctuations and topological defects. For longer distances (> 25 tablets, ~8.5 µm), the frequency spectrum of nacre tablets follows f −1.5 behavior suggesting growth is coupled to external stochastic processes—a universality found across disparate natural phenomena which now includes pearls. © 2021, CC BY.

关键词： Random processes

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Atomic-Scale Dynamics of the Initial Stages of Cu and Cu Alloy Oxidation

引用

microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Yang, Judith C Li, Meng Zakharov, Dmitri N McEver, Jack Greene Qiao, Linna Qu, Xiaohui Lu, Deyu Curnan, Matthew T House, Stephen D Gleason, Samuel Ciston, Jim Zhou, Guangwen Saidi, Wissam A Chemical and Petroleum Engineering Department University of Pittsburgh Pittsburgh PA United States Physics and Astronomy Department University of Pittsburgh Pittsburgh PA United States Center for Functional Nanomaterials Brookhaven National Lab Upton NY United States Materials Science and Engineering Program & Department of Mechanical Engineering State University of New York at Binghamton Binghamton NY United States Chemical Engineering Department Pohang University of Science and Technology (POSTECH) Pohang Gyeongbuk Republic of Korea Center for Integrated Nanotechnologies Sandia National Laboratories Albuquerque NM United States Chemistry Department University of California Berkeley CA United States National Center for Electron Microscopy The Molecular Foundry Lawrence Berkeley National Laboratory Berkeley CA United States Mechanical Engineering and Materials Science Department University of Pittsburgh Pittsburgh PA United States National Energy Technology Laboratory Pittsburgh PA United States

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