Avalanche criticality during ferroelectric/ferroelastic switching

Casals, Blai; Nataf, Guillaume F.; Salje, Ekhard K. H.

doi:10.1038/s41467-020-20477-6

Cited by 40 publications

(27 citation statements)

References 65 publications

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“…Under stress, the domain walls do no longer move smoothly, but form avalanches. This phenomenon is rather common when walls de-pin and nucleate new walls, see the reviews in [16,[42][43][44][45][46]. The energy distributions of these avalanches follow power laws with energy exponents ε between 1.33 and 2.7.…”

Section: Twin Walls As Storages For Cations and Their Pinning Behaviour In Anorthoclasementioning

confidence: 96%

Ferroelastic Twinning in Minerals: A Source of Trace Elements, Conductivity, and Unexpected Piezoelectricity

Salje

2021

Minerals

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Ferroelastic twinning in minerals is a very common phenomenon. The twin laws follow simple symmetry rules and they are observed in minerals, like feldspar, palmierite, leucite, perovskite, and so forth. The major discovery over the last two decades was that the thin areas between the twins yield characteristic physical and chemical properties, but not the twins themselves. Research greatly focusses on these twin walls (or ‘twin boundaries’); therefore, because they possess different crystal structures and generate a large variety of ‘emerging’ properties. Research on wall properties has largely overshadowed research on twin domains. Some wall properties are discussed in this short review, such as their ability for chemical storage, and their structural deformations that generate polarity and piezoelectricity inside the walls, while none of these effects exist in the adjacent domains. Walls contain topological defects, like kinks, and they are strong enough to deform surface regions. These effects have triggered major research initiatives that go well beyond the realm of mineralogy and crystallography. Future work is expected to discover other twin configurations, such as co-elastic twins in quartz and growth twins in other minerals.

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Section: Twin Walls As Storages For Cations and Their Pinning Behaviour In Anorthoclasementioning

confidence: 96%

Ferroelastic Twinning in Minerals: A Source of Trace Elements, Conductivity, and Unexpected Piezoelectricity

Salje

2021

Minerals

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“…Avalanche dynamics also describe the fluctuations of stock markets, which have small fluctuations and stock disasters caused by financial crisis [6], the temporary evolution of neuron connectors during 'thinking' processes [13][14][15], and the medical deterioration of brain structures [16,17]. Other examples are the Barkhausen 'noise' of pinned domain walls during magnetization processes [18][19][20][21], martensitic transformations [22,23], plastic deformation in solids [24], materials failure [25], ferroelectric and ferroelastic domain movements [3,[26][27][28][29] etc. Avalanche events are monitored by different monitoring methods, such as force drop measurement [30], optical observation [28], thermal radiation observation [23], etc.…”

Section: Avalanches and Acoustic Emission Spectroscopymentioning

confidence: 99%

“…Other examples are the Barkhausen 'noise' of pinned domain walls during magnetization processes [18][19][20][21], martensitic transformations [22,23], plastic deformation in solids [24], materials failure [25], ferroelectric and ferroelastic domain movements [3,[26][27][28][29] etc. Avalanche events are monitored by different monitoring methods, such as force drop measurement [30], optical observation [28], thermal radiation observation [23], etc.…”

Section: Avalanches and Acoustic Emission Spectroscopymentioning

confidence: 99%

Acoustic Emission Spectroscopy: Applications in Geomaterials and Related Materials

et al. 2021

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As a non-destructive testing technology with fast response and high resolution, acoustic emission is widely used in material monitoring. The material deforms under stress and releases elastic waves. The wave signals are received by piezoelectric sensors and converted into electrical signals for rapid storage and analysis. Although the acoustic emission signal is not the original stress signal inside the material, the typical statistical distributions of acoustic emission energy and waiting time between signals are not affected by signal conversion. In this review, we first introduce acoustic emission technology and its main parameters. Then, the relationship between the exponents of power law distributed AE signals and material failure state is reviewed. The change of distribution exponent reflects the transition of the material’s internal failure from a random and uncorrelated state to an interrelated state, and this change can act as an early warning of material failure. The failure process of materials is often not a single mechanism, and the interaction of multiple mechanisms can be reflected in the probability density distribution of the AE energy. A large number of examples, including acoustic emission analysis of biocemented geological materials, hydroxyapatite (human teeth), sandstone creep, granite, and sugar lumps are introduced. Finally, some supplementary discussions are made on the applicability of Båth’s law.

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“…The unique aspect is that geometrical patterns can be observed, e.g., in optical microscopy, and their evolution can be measured. It was found that the changes of pattern are often fractal and that direct anticorrelations between the Hausdorff dimension of the pattern evolution and the avalanche scaling of their energies or amplitudes were observed [34]. So far, these observations are empirical and do not distinguish between exponents of dynamical processes and topological configurations, but it is anticipated that with the advent of more experiments with much higher time and space resolution, the detailed characterization of the pattern formation process will advance further.…”

Section: Introductionmentioning

confidence: 99%

“…Avalanches in BaTiO 3 have the advantage that the Hausdorff dimension of the emerging domain patterns can be evaluated directly by optical observations [34]. Our model is designed to answer the question: What pattern evolution is expected during porous collapse and what correlations exist between dynamic avalanche parameters (e.g., the exponents of power law distributions), the collapse pattern, and the Hausdorff dimension.…”

Section: Introductionmentioning

confidence: 99%

Energy exponents of avalanches and Hausdorff dimensions of collapse patterns

Casals

Salje

2021

Phys. Rev. E

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A simple numerical model to simulate athermal avalanches is presented. The model is inspired by the "porous collapse" process where the compression of porous materials generates collapse cascades, leading to power law distributed avalanches. The energy (E ), amplitude (A max ), and size (S) exponents are derived by computer simulation in two approximations. Time-dependent "jerk" spectra are calculated in a single avalanche model where each avalanche is simulated separately from other avalanches. The average avalanche profile is parabolic, the scaling between energy and amplitude follows E ∼ A 2 max , and the energy exponent is ε = 1.33. Adding a general noise term in a continuous event model generates infinite avalanche sequences which allow the evaluation of waiting time distributions and pattern formation. We find the validity of the Omori law and the same exponents as in the single avalanche model. We then add spatial correlations by stipulating the ratio G/N between growth processes G (linked to a previous event location) and nucleation processes N (with new, randomly chosen nucleation sites). We found, in good approximation, a power law correlation between the energy exponent ε and the Hausdorff dimension H D of the resulting collapse pattern H D −1 ∼ ε −3 . The evolving patterns depend strongly on G/N with the distribution of collapse sites equally power law distributed. Its exponent ε topo would be linked to the dynamical exponent ε if each collapse carried an energy equivalent to the size of the collapse. A complex correlation between ε, ε topo , and H D emerges, depending strongly on the relative occupancy of the collapse sites in the simulation box.

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Avalanche criticality during ferroelectric/ferroelastic switching

Cited by 40 publications

References 65 publications

Ferroelastic Twinning in Minerals: A Source of Trace Elements, Conductivity, and Unexpected Piezoelectricity

Ferroelastic Twinning in Minerals: A Source of Trace Elements, Conductivity, and Unexpected Piezoelectricity

Acoustic Emission Spectroscopy: Applications in Geomaterials and Related Materials

Energy exponents of avalanches and Hausdorff dimensions of collapse patterns

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