A Novel Mechanical Metamaterial Exhibiting Auxetic Behavior and Negative Compressibility

Grima, Joseph N.; Attard, Daphne

doi:10.3390/ma13010079

Cited by 39 publications

(30 citation statements)

References 74 publications

(82 reference statements)

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“…The effect takes place when a metallic particle vibrates with the external frequency

approaching the plasma frequency

from above. In this case, the effective mass of the metallic particle

, where

is the mass of the ionic lattice, and

is the mass of the electron gas, becomes negative [ 12 , 13 , 15 , 18 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

“…Acoustic metamaterials demonstrating a negative Poisson’s ratio [ 13 ] and negative elastic modulus were discussed [ 14 ]. Mechanical metamaterials exhibiting auxetic behaviors and negative compressibility were suggested [ 15 ]. Acoustic metamaterials demonstrate a potential to be perfect absorbers of mechanical vibrations [ 16 ] and also of materials enabling the focusing of ultrasound [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Negative Effective Mass in Plasmonic Systems II: Elucidating the Optical and Acoustical Branches of Vibrations and the Possibility of Anti-Resonance Propagation

2020

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We report the negative effective mass metamaterials based on the electro-mechanical coupling exploiting plasma oscillations of free electron gas. The negative mass appears as a result of the vibration of a metallic particle with a frequency ω which is close to the frequency of the plasma oscillations of the electron gas m2, relative to the ionic lattice m1. The plasma oscillations are represented with the elastic spring constant k2=ωp2m2, where ωp is the plasma frequency. Thus, the metallic particle vibrating with the external frequency ω is described by the effective mass meff=m1+m2ωp2ωp2−ω2, which is negative when the frequency ω approaches ωp from above. The idea is exemplified with two conducting metals, namely Au and Li embedded in various matrices. We treated a one-dimensional lattice built from the metallic micro-elements meff connected by ideal springs with the elastic constant k1 representing various media such as polydimethylsiloxane and soda-lime glass. The optical and acoustical branches of longitudinal modes propagating through the lattice are elucidated for various ratios ω1ωp, where ω12=k1m1 and k1 represents the elastic properties of the medium. The 1D lattice, built from the thin metallic wires giving rise to low frequency plasmons, is treated. The possibility of the anti-resonant propagation, strengthening the effect of the negative mass occurring under ω = ωp = ω1, is addressed.

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“…The effect takes place when a metallic particle vibrates with the external frequency

approaching the plasma frequency

from above. In this case, the effective mass of the metallic particle

, where

is the mass of the ionic lattice, and

is the mass of the electron gas, becomes negative [ 12 , 13 , 15 , 18 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Negative Effective Mass in Plasmonic Systems II: Elucidating the Optical and Acoustical Branches of Vibrations and the Possibility of Anti-Resonance Propagation

2020

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“…[21] Since Baughman et al [21] first proposed this property, it has gradually attracted widespread attention owing to its unconventional mechanical properties. Thus far, a large number of micromaterials [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] and macrostructures [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] have been proved to exhibit negative compressibility. As for micromaterials, negative compressibility has been found to occur in a much greater variety of materials, ranging from molecular, [22,23] metal-organic frameworks (MOFs), [24,25] and polymers.…”

Section: Introductionmentioning

confidence: 99%

Negative Compressibility in Hexagonal and Trigonal Models Constructed by Hinging Wine‐Rack Mechanism

Zhou

Liu

et al. 2021

Physica Status Solidi (b)

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In our previous work, a monoclinic octahedron model based on the tetragonal octahedron model for the negative compressibility property was analyzed. To design a larger number of new models with greater negative compressibility property, an extensive study to propose the hexagonal and trigonal models constructed by a hinging wine‐rack mechanism is now conducted, according to the concept of the crystal system and the method to design new models by changing the system of models. The compressibility properties through theoretical modeling are analyzed, and the results show that the two models exhibit negative compressibility. Further analysis indicates that the two models have strong similarity with the tetragonal model, which can be analyzed uniformly, and the trigonal model has the greatest negative compressibility property among the three models, irrespective of the on‐axis or off‐axis compressibility property. Finally, owing to the particularity of the space configuration for the hexagonal and trigonal models, a series of new models with different spatial arrangements are proposed, which can greatly increase the number of models with negative compressibility.

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“…6 From conclusions based on the elastic properties of 121 pure silica zeolites, it suggested a feasibility criterion for zeolites based on their elastic anisotropy, as well as the fact that a small number of hypothetical pure silica zeolites could behave as mechanical metamaterials. [9][10][11] In general, the computational determination of the mechanical properties can be performed either at the force field level, 12 or using so-called first principle methods (i.e., at the quantum chemical level). 13 The differences between the two techniques have to do with transferability, accuracy, and computational cost.…”

Section: Introductionmentioning

confidence: 99%

Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

2020

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The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson's ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current "cheap" approach for screening, which is the use of force 1 fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials.

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A Novel Mechanical Metamaterial Exhibiting Auxetic Behavior and Negative Compressibility

Cited by 39 publications

References 74 publications

Negative Effective Mass in Plasmonic Systems II: Elucidating the Optical and Acoustical Branches of Vibrations and the Possibility of Anti-Resonance Propagation

Negative Effective Mass in Plasmonic Systems II: Elucidating the Optical and Acoustical Branches of Vibrations and the Possibility of Anti-Resonance Propagation

Negative Compressibility in Hexagonal and Trigonal Models Constructed by Hinging Wine‐Rack Mechanism

Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

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