Two-Steps Versus One-Step Solidification Pathways of Binary Metallic Nanodroplets

Nelli, Diana; Koraychy, El Yakout El; Cerbelaud, Manuella; Crespin, Benoît; Videcoq, Arnaud; Giacomello, Alberto; Ferrando, Riccardo

doi:10.1021/acsnano.2c09741

Cited by 11 publications

(4 citation statements)

References 59 publications

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“…Also Huang and Balbuena 296 showed a two-stage process for bimetallic Cu−Ni 343-and 1000-atom nanoclusters of compositions Cu 0.25 Ni 0.75 and Cu 0.5 Ni 0.5 by MD simulations using the Sutton−Chen many-body potential. A similar twostep mechanism was shown by Nelli et al 297 for the solidification of AgCo, AgNi, and AgCu nanodroplets in the size range of 2−8 nm by MD simulations. Another important phenomenon is that for supported nanoparticles the melting temperature strongly depends on its wetting angle, hence the substrate.…”

Section: Melting Of Nanoparticlessupporting

confidence: 80%

Melting Is Well-Known, but Is It Also Well-Understood?

de With

2023

Chem. Rev.

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Contrary to continuous phase transitions, where renormalization group theory provides a general framework, for discontinuous phase transitions such a framework seems to be absent. Although the thermodynamics of the latter type of transitions is wellknown and requires input from two phases, for melting a variety of one-phase theories and models based on solids has been proposed, as a generally accepted theory for liquids is (yet) missing. Each theory or model deals with a specific mechanism using typically one of the various defects (vacancies, interstitials, dislocations, interstitialcies) present in solids. Furthermore, recognizing that surfaces are often present, one distinguishes between mechanical or bulk melting and thermodynamic or surface-mediated melting. After providing the necessary preliminaries, we discuss both types of melting in relation to the various defects. Thereafter we deal with the effect of pressure on the melting process, followed by a discussion along the line of type of materials. Subsequently, some other aspects and approaches are dealt with. An attempt to put melting in perspective concludes this review.

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Section: Melting Of Nanoparticlessupporting

confidence: 80%

Melting Is Well-Known, but Is It Also Well-Understood?

de With

2023

Chem. Rev.

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“…For Cu‐rich and Ag‐rich compositions, the results of chemical ordering optimization are closer to those of full optimization. [ 86,87 ]…”

Section: Resultsmentioning

confidence: 99%

“…For Cu-rich and Ag-rich compositions, the results of chemical ordering optimization are closer to those of full optimization. [86,87] Therefore the answer to point (iii) is no in many cases. The optimization of chemical ordering alone at fixed geometrical structures is often unable to lead to low-energy structures in AgCu because the search often remains trapped in unphysical highenergy shapes.…”

Section: Optimization Of Larger Nanoparticles: the Case Of Agcumentioning

confidence: 99%

Optimizing the Shape and Chemical Ordering of Nanoalloys with Specialized Walkers

Rapetti

Roncaglia

Ferrando

2023

Advcd Theory and Sims

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New algorithms for the optimization of alloy nanoparticles (nanoalloys) are presented. The new algorithms are based on the concept of multiple basin‐hopping walkers running in parallel, each with its own specialized task—the flying walker, exploring the energy landscape at high temperatures to sample different geometric structures, and the landing and hiking walkers mainly refining the optimization of chemical ordering at low temperatures. These algorithms are referred to as flying‐landing (FL) and flying‐landing‐hiking (FLH). The algorithms are tested against several benchmarks (AuCu and AuRh clusters of 400 atoms and PtNi clusters of 38 and 55 atoms). In all cases, both FL and FLH are shown to perform very well compared to previous results in the literature. In addition, the algorithms are applied to the optimization of larger AgCu nanoparticles, with sizes up to 4000 atoms, in order to establish the behavior of the mixing energy and to compare full global optimization of shape and chemical ordering with optimization of chemical ordering alone at a fixed shape. In general, the results show that the simultaneous optimization of shape and chemical ordering is necessary in many cases, and that the FLH approach is especially efficient for that purpose.

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“…The employed numerical setup that involved freezing disordered liquid droplets was already used to simulate nanoparticle formation and mimic gas-phase synthesis. [90][91][92] In addition, to assess the stability of our obtained systems, we also carried out hybrid MD/MC simulations that involved 10 Monte Carlo (MC) moves combined with 10 atomic species swapping every 100 MD time steps.…”

Section: Atomistic Simulationsmentioning

confidence: 99%