IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-29044-7.html
   My bibliography  Save this article

Dislocation avalanches are like earthquakes on the micron scale

Author

Listed:
  • Péter Dusán Ispánovity

    (Department of Materials Physics)

  • Dávid Ugi

    (Department of Materials Physics)

  • Gábor Péterffy

    (Department of Materials Physics)

  • Michal Knapek

    (Faculty of Mathematics and Physics, Department of Physics of Materials)

  • Szilvia Kalácska

    (Department of Materials Physics
    Univ Lyon, CNRS, UMR 5307 LGF, Centre SMS)

  • Dániel Tüzes

    (Department of Materials Physics)

  • Zoltán Dankházi

    (Department of Materials Physics)

  • Kristián Máthis

    (Faculty of Mathematics and Physics, Department of Physics of Materials)

  • František Chmelík

    (Faculty of Mathematics and Physics, Department of Physics of Materials)

  • István Groma

    (Department of Materials Physics)

Abstract

Compression experiments on micron-scale specimens and acoustic emission (AE) measurements on bulk samples revealed that the dislocation motion resembles a stick-slip process – a series of unpredictable local strain bursts with a scale-free size distribution. Here we present a unique experimental set-up, which detects weak AE waves of dislocation slip during the compression of Zn micropillars. Profound correlation is observed between the energies of deformation events and the emitted AE signals that, as we conclude, are induced by the collective dissipative motion of dislocations. The AE data also reveal a two-level structure of plastic events, which otherwise appear as a single stress drop. Hence, our experiments and simulations unravel the missing relationship between the properties of acoustic signals and the corresponding local deformation events. We further show by statistical analyses that despite fundamental differences in deformation mechanism and involved length- and time-scales, dislocation avalanches and earthquakes are essentially alike.

Suggested Citation

  • Péter Dusán Ispánovity & Dávid Ugi & Gábor Péterffy & Michal Knapek & Szilvia Kalácska & Dániel Tüzes & Zoltán Dankházi & Kristián Máthis & František Chmelík & István Groma, 2022. "Dislocation avalanches are like earthquakes on the micron scale," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29044-7
    DOI: 10.1038/s41467-022-29044-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-29044-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-29044-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Janićević, Sanja & Mijatović, Svetislav & Spasojević, Djordje, 2023. "Finite driving rate effects in the nonequilibrium athermal random field Ising model of thin systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    2. Spasojević, Djordje & Janićević, Sanja, 2023. "Disordered ferromagnetic systems with stochastic driving," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    3. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    7. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Laura Portell & Sergi Morera & Helena Ramalhinho, 2022. "Door-to-Door Transportation Services for Reduced Mobility Population: A Descriptive Analytics of the City of Barcelona," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    9. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    10. Matthias Wagener & Andriette Bekker & Mohammad Arashi, 2021. "Mastering the Body and Tail Shape of a Distribution," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    11. Gallo Cassarino, Tiziano & Barrett, Mark, 2022. "Meeting UK heat demands in zero emission renewable energy systems using storage and interconnectors," Applied Energy, Elsevier, vol. 306(PB).
    12. Maren Schnieder, 2023. "Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    13. Gabriele Orlando & Daniele Raimondi & Ramon Duran-Romaña & Yves Moreau & Joost Schymkowitz & Frederic Rousseau, 2022. "PyUUL provides an interface between biological structures and deep learning algorithms," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    14. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
    15. Vincent Wagner & Nicole Erika Radde, 2021. "SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems," Mathematics, MDPI, vol. 9(10), pages 1-13, May.
    16. L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022. "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    17. Samuel G. Fadel & Sebastian Mair & Ricardo da Silva Torres & Ulf Brefeld, 2023. "Contextual movement models based on normalizing flows," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 51-72, March.
    18. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    19. Wout Bittremieux & Nicole E. Avalon & Sydney P. Thomas & Sarvar A. Kakhkhorov & Alexander A. Aksenov & Paulo Wender P. Gomes & Christine M. Aceves & Andrés Mauricio Caraballo-Rodríguez & Julia M. Gaug, 2023. "Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    20. Bo Lin & Jian Jiang & Xiao Cheng Zeng & Lei Li, 2023. "Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29044-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.