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arXiv 2023年

作者： Ercius, Peter Johnson, Ian J. Pelz, Philipp Savitzky, Benjamin H. Hughes, Lauren Brown, Hamish G. Zeltmann, Steven E. Hsu, Shang-Lin Pedroso, Cassio C.S. Cohen, Bruce E. Ramesh, Ramamoorthy Paul, David Joseph, John M. Stezelberger, Thorsten Czarnik, Cory Lent, Matthew Fong, Erin Ciston, Jim Scott, Mary C. Ophus, Colin Minor, Andrew M. Denes, Peter National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Engineering Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Molecular Foundry Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Division of Molecular Biophysics & Integrated Bioimaging Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Materials Science Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Physics Department University of California BerkeleyCA94720 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Gatan Inc. 5794 W. Las Positas Blvd. PleasantonCA94588 United States

We describe the development, operation, and application of the 4D Camera – a 576 by 576 pixel active pixel sensor for scanning/transmission electron microscopy which operates at 87,000 Hz. The detector generates data at approximately 480 Gbit/s which is captured by dedicated receiver computers with a parallelized software infrastructure that has been implemented to process the resulting 10 - 700 Gigabyte-sized raw datasets. The back illuminated detector provides the ability to detect single electron events at accelerating voltages from 30 - 300 keV. Through electron counting, the resulting sparse data sets are reduced in size by 10−300× compared to the raw data, and open-source sparsity-based processing algorithms offer rapid data analysis. The high frame rate allows for large and complex 4D-STEM experiments to be accomplished with typical STEM scanning parameters. © 2023, CC BY.

关键词： Cameras

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Quantum turbulence coupled with externally driven normal-fluid turbulence in superfluid 4He

arXiv

引用

arXiv 2021年

作者： Yui, Satoshi Kobayashi, Hiromichi Tsubota, Makoto Yokota, Rio Research and Education Center for Natural Sciences Keio University 4-1-1 Hiyoshi Kohoku-ku Yokohama223-8521 Japan Department of Physics Hiyoshi Campus Keio University 4-1-1 Hiyoshi Kohoku-ku Yokohama223-8521 Japan Osaka City University 3-3-138 Sugimoto Sumiyoshi-ku Osaka558-8585 Japan Global Scientific Information and Computing Center Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo152-8550 Japan

The coupled dynamics of quantum turbulence (QT) and normal-fluid turbulence (NFT) have been a central challenge in quantum hydrodynamics, since it is expected to cause the unsolved T2 state of QT. We numerically studied the coupled dynamics of the two turbulences in thermal counterflow. NFT is driven by external forces to control its turbulent intensity, and the fast multipole method accelerates the calculation of QT. We show that NFT enhances QT via mutual friction. The vortex line density L of the QT satisfies the statistical law L1/2 ≈ γVns with the counterflow velocity Vns. The obtained γ agrees with the experiment of T2 state, validating the idea that the T2 state is caused by NFT. We propose a theoretical insight into the relation between the two turbulences. © 2021, CC BY.

关键词： Quantum theory

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A practical guide to machine learning interatomic potentials – Status and future

arXiv

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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CP violating effects in 210Fr and prospects for new physics beyond the Standard Model

arXiv

引用

arXiv 2020年

作者： Shitara, Nanako Yamanaka, Nodoka Sahoo, B.K. Watanabe, T. Das, B.P. Department of Physics School of Science Tokyo Institute of Technology Ookayama Meguro-ku Tokyo152-8550 Japan Amherst Center for Fundamental Interactions Department of Physics University of Massachusetts Amherst MA01003 United States Nishina Center RIKEN Wako351-0198 Japan Atomic Molecular and Optical Physics Division Physical Research Laboratory Navrangpura Ahmedabad380009 India Global Scientific Information and Computing Center Tokyo Institute of Technology Ookayama Meguro-ku Tokyo152-8550 Japan

We report theoretical results of the electric dipole moment (EDM) of 210Fr which arises from the interaction of the EDM of an electron with the internal electric field in an atom and the scalar-pseudoscalar electron-nucleus interaction;the two dominant sources of CP violation in this atom. Employing the relativistic coupled-cluster theory, we evaluate the enhancement factors for these two CP violating interactions to an accuracy of about 3% and analyze the contributions of the many-body effects. These two quantities in combination with the projected sensitivity of the 210Fr EDM experiment provide constraints on new physics beyond the Standard Model. Particularly, we demonstrate that their precise values are necessary to account for the effect of the bottom quark in the Higgs doublet models. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric dipole moments

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Comparing recent PTA results on the nanohertz stochastic gravitational wave background

arXiv

引用

arXiv 2023年

作者： Agazie, G. Antoniadis, J. Anumarlapudi, A. Archibald, A.M. Arumugam, P. Arumugam, S. Arzoumanian, Z. Askew, J. Babak, S. Bagchi, M. Bailes, M. Bak Nielsen, A.-S. Baker, P.T. Bassa, C.G. Bathula, A. Bécsy, B. Berthereau, A. Bhat, N.D.R. Blecha, L. Bonetti, M. Bortolas, E. Brazier, A. Brook, P.R. Burgay, M. Burke-Spolaor, S. Burnette, R. Caballero, R.N. Cameron, A. Case, R. Chalumeau, A. Champion, D.J. Chanlaridis, S. Charisi, M. Chatterjee, S. Chatziioannou, K. Cheeseboro, B.D. Chen, S. Chen, Z.-C. Cognard, I. Cohen, T. Coles, W.A. Cordes, J.M. Cornish, N.J. Crawford, F. Cromartie, H.T. Crowter, K. Curylo, M. Cutler, C.J. Dai, S. Dandapat, S. Deb, D. DeCesar, M.E. DeGan, D. Demorest, P.B. Deng, H. Desai, S. Desvignes, G. Dey, L. Dhanda-Batra, N. Di Marco, V. Dolch, T. Drachler, B. Dwivedi, C. Ellis, J.A. Falxa, M. Feng, Y. Ferdman, R.D. Ferrara, E.C. Fiore, W. Fonseca, E. Franchini, A. Freedman, G.E. Gair, J.R. Garver-Daniels, N. Gentile, P.A. Gersbach, K.A. Glaser, J. Good, D.C. Goncharov, B. Gopakumar, A. Graikou, E. Grießmeier, J.-M. Guillemot, L. Gültekin, K. Guo, Y.J. Gupta, Y. Grunthal, K. Hazboun, J.S. Hisano, S. Hobbs, G.B. Hourihane, S. Hu, H. Iraci, F. Islo, K. Izquierdo-Villalba, D. Jang, J. Jawor, J. Janssen, G.H. Jennings, R.J. Jessner, A. Johnson, A.D. Jones, M.L. Joshi, B.C. Kaiser, A.R. Kaplan, D.L. Kapur, A. Kareem, F. Karuppusamy, R. Keane, E.F. Keith, M.J. Kelley, L.Z. Kerr, M. Key, J.S. Kharbanda, D. Kikunaga, T. Klein, T.C. Kolhe, N. Kramer, M. Krishnakumar, M.A. Kulkarni, A. Laal, N. Lackeos, K. Lam, M.T. Lamb, W.G. Larsen, B.B. Lazio, T.J.W. Lee, K.J. Levin, Y. Lewandowska, N. Littenberg, T.B. Liu, K. Liu, T. Liu, Y. Lommen, A. Lorimer, D.R. Lower, M.E. Luo, J. Luo, R. Lynch, R.S. Lyne, A.G. Ma, C.-P. Maan, Y. Madison, D.R. Main, R.A. Manchester, R.N. Mandow, R. Mattson, M.A. McEwen, A. McKee, J.W. McLaughlin, M.A. McMann, N. Meyers, B.W. Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Institute of Astrophysics FORTH N. Plastira 100 Heraklion70013 Greece Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Newcastle University NE1 7RU United Kingdom Department of Physics Indian Institute of Technology Roorkee Roorkee247667 India Department of Electrical Engineering IIT Hyderabad Telangana Kandi502284 India X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Australia Université Paris Cité CNRS Astroparticule et Cosmologie Paris75013 France The Institute of Mathematical Sciences C.I.T. Campus Taramani Chennai600113 India Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India Fakultät für Physik Universität Bielefeld Postfach 100131 Bielefeld33501 Germany Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands Department of Physical Sciences Indian Institute of Science Education and Research Punjab Mohali140306 India Department of Physics Oregon State University CorvallisOR97331 United States Laboratoire de Physique et Chimie de l'Environnement et de l'Espace Université d'Orléans CNRS Orléans45071 Cedex 02 France Observatoire Radioastronomique de Nançay Observatoire de Paris Université PSL Université d'Orléans CNRS Nançay18330 France International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Physics Department University of Florida GainesvilleFL32611 United States Dipartimento di Fisica "G. Occhialini" Universitá degli Studi di Milano-Bicocca

The Australian, Chinese, European, Indian, and North American pulsar timing array (PTA) collaborations recently reported, at varying levels, evidence for the presence of a nanohertz gravitational wave background (GWB). Given that each PTA made different choices in modeling their data, we perform a comparison of the GWB and individual pulsar noise parameters across the results reported from the PTAs that constitute the International Pulsar Timing Array (IPTA). We show that despite making different modeling choices, there is no significant difference in the GWB parameters that are measured by the different PTAs, agreeing within 1σ. The pulsar noise parameters are also consistent between different PTAs for the majority of the pulsars included in these analyses. We bridge the differences in modeling choices by adopting a standardized noise model for all pulsars and PTAs, finding that under this model there is a reduction in the tension in the pulsar noise parameters. As part of this reanalysis, we"extended" each PTA's data set by adding extra pulsars that were not timed by that PTA. Under these extensions, we find better constraints on the GWB amplitude and a higher signal-to-noise ratio for the Hellings and Downs correlations. These extensions serve as a prelude to the benefits offered by a full combination of data across all pulsars in the IPTA, i.e., the IPTA's Data Release 3, which will involve not just adding in additional pulsars, but also including data from all three PTAs where any given pulsar is timed by more than as single PTA. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulsars

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Impact of subdominant modes on the interpretation of gravitational-wave signals from heavy binary black hole systems

arXiv

引用

arXiv 2019年

作者： Shaik, Feroz H. Lange, Jacob Field, Scott E. O'Shaughnessy, Richard Varma, Vijay Kidder, Lawrence E. Pfeiffer, Harald P. Wysocki, Daniel Department of Physics Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth DartmouthMA02747 United States Center for Computational Relativity and Gravitation Rochester Institute of Technology RochesterNY14623 United States Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth DartmouthMA02747 United States Theoretical Astrophysics California Institute of Technology PasadenaCA91125 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Am Mühlenberg 1 Potsdam14476 Germany

Over the past year, a handful of new gravitational wave models have been developed to include multiple harmonic modes thereby enabling for the first time fully Bayesian inference studies including higher modes to be performed. Using one recently-developed numerical relativity surrogate model, NRHybSur3dq8, we investigate the importance of higher modes on parameter inference of coalescing massive binary black holes. We focus on examples relevant to the current three-detector network of observatories, with detector-frame masses and signal amplitudes consistent with plausible candidates for the next few observing runs. We show that for such systems the higher mode content will be important for interpreting coalescing binary black holes, reducing systematic bias, and computing properties of the remnant object. For asymmetric binaries with mass ratios q > 1, higher modes are critical, and their omission usually produces substantial parameter biases. Even for comparable-mass binaries and at low signal amplitude, the omission of higher modes can influence posterior probability distributions. We discuss the impact of our results on source population inference and self-consistency tests of general relativity. Copyright © 2019, The Authors. All rights reserved.

关键词： Stars

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Numerical methods for nonlocal and fractional models

arXiv

引用

arXiv 2020年

作者： D'Elia, Marta Du, Qiang Glusa, Christian Gunzburger, Max Tian, Xiaochuan Zhou, Zhi Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States Department of Applied Physics and Applied Mathematics Data Science Institute Columbia University New YorkNY10027 United States Department of Scientific Computing Florida State University TallahasseeFL32306 United States Department of Mathematics University of Texas Austin78751 United States Department of Applied Mathematics Hong Kong Polytechnic University Kowloon Hong Kong

Partial differential equations (PDEs) are used, with huge success, to model phenomena arising across all scientific and engineering disciplines. However, across an equally wide swath, there exist situations in which PDE models fail to adequately model observed phenomena or are not the best available model for that purpose. On the other hand, in many situations, nonlocal models that account for interaction occurring at a distance have been shown to more faithfully and effectively model observed phenomena that involve possible singularities and other anomalies. In this article, we consider a generic nonlocal model, beginning with a short review of its definition, the properties of its solution, its mathematical analysis, and specific concrete examples. We then provide extensive discussions about numerical methods, including finite element, finite difference, and spectral methods, for determining approximate solutions of the nonlocal models considered. In that discussion, we pay particular attention to a special class of nonlocal models that are the most widely studied in the literature, namely those involving fractional derivatives. The article ends with brief considerations of several modeling and algorithmic extensions which serve to show the wide applicability of nonlocal modeling. Copyright © 2020, The Authors. All rights reserved.

关键词： Numerical methods

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Current State of Community Driven Radiological AI Deployment in Medical Imaging

arXiv

引用

arXiv 2022年

作者： Gupta, Vikash Erdal, Barbaros Selnur Ramirez, Carolina Floca, Ralf Jackson, Laurence Genereaux, Brad Bryson, Sidney Bridge, Christopher P. Kleesiek, Jens Nensa, Felix Braren, Rickmer Younis, Khaled Penzkofer, Tobias Bucher, Andreas Michael Qin, Ming Melvin Bae, Gigon Lee, Hyeonhoon Cardoso, M. Jorge Ourselin, Sebastien Kerfoot, Eric Choudhury, Rahul White, Richard D. Cook, Tessa Bericat, David Lungren, Matthew Haukioja, Risto Shuaib, Haris Center for Augmented Intelligence in Imaging Mayo Clinic Florida United States Center for Intelligent Imaging University of California San Francisco United States Heidelberg Germany Clinical Scientific Computing Medical Physics Guy’s and St’ Thomas’ NHS Foundation Trust London United Kingdom NVIDIA Inc Santa Clara United States Department of Radiology Massachusetts General Hospital Boston United States Institute of AI in Medicine University Medicine Essen Essen Germany Institute for Radiology School of Medicine Technical University Munich Germany Philips Inc Netherlands Department of Radiology Charité - Universitätsmedizin Berlin Germany University Hospital Frankfurt Germany Biomedical Research Institute Seoul National University Hospital Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Perelman School of Medicine University of Pennsylvania United States Microsoft Corporation

Background: Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently, introducing a demand for tools that improve the efficiency with which radiologists can comfortably interpret these exams. Methods: AI has been shown to improve efficiency in medical image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed the Medical Open Network for Artificial Intelligence (MONAI) Consortium, an open source community which is building standards for AI deployment in hospitals, and developing tools and infrastructure to facilitate their implementation. This paper represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians. Findings: We identify barriers between AI model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical radiology workflow. We also present a taxonomy of radiology AI use cases. Interpretation: Through this paper, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions. Funding: MONAI is an open-source

关键词： Medical imaging

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Surrogate model of hybridized numerical relativity binary black hole waveforms

引用

Physical Review D 2019年第6期99卷 064045-064045页

作者： Vijay Varma Scott E. Field Mark A. Scheel Jonathan Blackman Lawrence E. Kidder Harald P. Pfeiffer Theoretical Astrophysics California Institute of Technology Pasadena California 91125 USA Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Radiophysics and Space Research Cornell University Ithaca New York 14853 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 Potsdam 14476 Germany

Numerical relativity (NR) simulations provide the most accurate binary black hole gravitational waveforms, but are prohibitively expensive for applications such as parameter estimation. Surrogate models of NR waveforms have been shown to be both fast and accurate. However, NR-based surrogate models are limited by the training waveforms’ length, which is typically about 20 orbits before merger. We remedy this by hybridizing the NR waveforms using both post-Newtonian and effective one-body waveforms for the early inspiral. We present NRHybSur3dq8, a surrogate model for hybridized nonprecessing numerical relativity waveforms, that is valid for the entire LIGO band (starting at 20 Hz) for stellar mass binaries with total masses as low as 2.25 M⊙. We include the ℓ≤4 and (5, 5) spin-weighted spherical harmonic modes but not the (4, 1) or (4, 0) modes. This model has been trained against hybridized waveforms based on 104 NR waveforms with mass ratios q≤8, and |χ1z|,|χ2z|≤0.8, where χ1z (χ2z) is the spin of the heavier (lighter) black hole in the direction of orbital angular momentum. The surrogate reproduces the hybrid waveforms accurately, with mismatches ≲3×10−4 over the mass range 2.25 M⊙≤M≤300 M⊙. At high masses (M≳40 M⊙), where the merger and ringdown are more prominent, we show roughly 2 orders of magnitude improvement over existing waveform models. We also show that the surrogate works well even when extrapolated outside its training parameter space range, including at spins as large as 0.998. Finally, we show that this model accurately reproduces the spheroidal-spherical mode mixing present in the NR ringdown signal.

关键词： Gravitational waves Numerical relativity

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Thermal conductivity and rheological behavior of silica / simulated body fluid - Dispersed Hardystonite and Åkermanite nanofluids: X-ray diffraction to analyze structure and morphology of a bio-ceramic

引用

International Communications in Heat and Mass Transfer 2025年 165卷

作者： Liu, Shulong Saidani, Taoufik Feng, Dulong Alawi, Mohammed Jameel Hussien, Mohamed Deebani, Wejdan Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei Normal University Huaibei235000 China Anhui Province Industrial Generic Technology Research Center for Alumics Materials Huaibei Normal University Huaibei235000 China School of Physics and Electronic Information Huaibei Normal University Huaibei235000 China Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Anhui Guangbo Mechanical Electronic Manufacturing Co. Ltd. Huaibei235000 China Electrical Engineering Department College of Engineering and Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Chemistry Faculty of Science King Khalid University P.O. Box 9004 Abha61413 Saudi Arabia Department of Mathematics College of Science & Arts King Abdul Aziz University Rabigh21911 Saudi Arabia

Bioceramics are integral in biomedical applications due to their biocompatibility, bioactivity, and mechanical properties. This study compares the thermal and rheological behavior of two hybrid bio-nanofluids (bi-ns): Hardystonite/Silica and Åkermanite/Silica dispersed in simulated body fluid (SBF). Nanoparticle morphology and composition were analyzed using X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM), confirming the crystalline phases of both composites. Åkermanite/Silica showed better dispersion stability, with a higher zeta potential (−16.19 mV) than Hardystonite/Silica (−7.23 mV). The addition of silica (SiO2) improved stability for both, with zeta potential increases of ∼203 % and 69 %, respectively. Thermal conductivity of both nanofluids increased with temperature and volume fraction, with Åkermanite/Silica achieving a maximum enhancement of 13.16 % (20–40 °C). However, both showed negative thermal conductivity enhancements compared to SBF, especially at higher concentrations. Hardystonite/Silica demonstrated greater thermal compatibility with SBF. Rheologically, both nanofluids exhibited shear-thinning behavior. Hardystonite/Silica showed a stronger reduction in viscosity with temperature increase—up to 43.43 % at 0.25 % volume fraction—compared to 32.74 % for Åkermanite/Silica. These results highlight the interaction between thermal and flow properties in bi-ns, indicating that Hardystonite/Silica may be better suited for applications requiring thermal compatibility and viscosity control. The findings contribute to the optimization of hybrid nanofluids for bone-related biomedical applications. © 2025 Elsevier Ltd

关键词： Medical nanotechnology

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