A test of a phenomenological model of size dependent melting in Au nanoparticles

Dai, Cong; Saidi, Peyman; Song, Hantao; Yao, Zhongwen; Daymond, Mark R.; Hoyt, J. J.

doi:10.1016/j.actamat.2017.06.052

Cited by 23 publications

(20 citation statements)

References 47 publications

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“…In order to determine the ultimate nanoparticle size, we used the Kofman model, which is the radius-dependent free energy difference between solid and liquid. The details and validity of this phenomenological model have been discussed in ref for the case of Au nanoparticles. Based on this model the melting temperature of a 20 nm Au nanoparticle is significantly (∼60 K) less than the bulk melting temperature.…”

Section: Results and Discussionmentioning

confidence: 99%

Bulk Immiscibility at the Edge of the Nanoscale

et al. 2017

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In the quest to identify more effective catalyst nanoparticles for many industrially important applications, the Au-Pt system has gathered considerable attention. Despite considerable effort the interplay between phase equilibrium behavior and surface segregation in Au-Pt nanoparticles is still poorly understood. Here we investigate the phase equilibrium behavior of 20 nm Au-Pt nanoparticles using a combination of high-resolution scanning transmission electron microscopy and a hybrid Monte Carlo and molecular dynamics atomistic simulation technique. Our approach takes into account the effects of immiscibility, elastic strain, interfacial free energy, and surface segregation. This is used to explain two key phenomena taking place in these nanoparticles. The first is whether the binary system remains immiscible at the nanoscale, and if so what morphology would the secondary phase take. Our findings suggest that even at sizes of 20 nm, thermally equilibrated Au-Pt nanoparticles remain largely immiscible and behave thermodynamically as bulk-like systems. We explain why 20 nm Au-Pt nanoparticles phase separate into hemispheres as opposed to a thick-shelled core-shell structure. These insights are central to further optimization of Au-Pt nanoparticles toward enhanced catalytic activities. The phase-separated Janus particles observed in this study offer enhanced material functionality arising from the nonuniformity of their plasmonic, catalytic, and surface properties.

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Section: Results and Discussionmentioning

confidence: 99%

Bulk Immiscibility at the Edge of the Nanoscale

et al. 2017

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“…the nominal simulation temperature (or, equivalently, the simulation time) is observed. The is commonly defined as the temperature where the highest value of the heat capacity is observed , or as the temperature here the highest standard deviation in the total energy is found 13 , 53 , 54 . Supplementary Fig.…”

Section: Methodsmentioning

confidence: 99%

Data-driven simulation and characterisation of gold nanoparticle melting

et al. 2021

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The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.

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“…value of the heat capacity is observed T NP melt , or as the temperature here the highest standard deviation in the total energy is found 13,53,54 In Section 7 of the Suppl. Mat., we show the striking correspondence that exists between the T NP melt estimated using the three aforementioned methods.…”

Section: Melting Temperature Estimationmentioning

confidence: 85%

Data-driven simulation and characterisation of gold nanoparticle melting

Zeni,

Rossi,

Pavloudis

et al. 2021

Preprint

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We develop efficient, accurate, transferable, and interpretable machine learning force fields for Au nanoparticles, based on data gathered from Density Functional Theory calculations. We then use them to investigate the thermodynamic stability of Au nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, with respect to a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with respect to available experimental data. Furthermore, we characterize in detail the solid to liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a rigorous and data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle, and employ it to show that melting initiates at the outer layers.

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A test of a phenomenological model of size dependent melting in Au nanoparticles

Cited by 23 publications

References 47 publications

Bulk Immiscibility at the Edge of the Nanoscale

Bulk Immiscibility at the Edge of the Nanoscale

Data-driven simulation and characterisation of gold nanoparticle melting

Data-driven simulation and characterisation of gold nanoparticle melting

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