Abstract:Particle size plays a crucial role in melting process of nanoparticles, but the mechanism, factors, range, and degree of the size effect are still unclear. Here, the precise equations of the integral melting enthalpy and entropy with radius of nanoparticles are deduced, without any adjustable parameters, and the influencing mechanism and the factors are discussed. Experimentally, the melting of spherical nano‐Au with different radii (0.9–37.4 nm) is taken as a system to research the melting behavior of nanopar… Show more
“…On average, the rPBE-derived ML-FF predicts 250 ± 50 K lower than the ones predicted by the LDA-derived ML-FF, and 180 ± 40 K lower than the ones predicted by the hybrid ML-FF. Interestingly, the predicted by the hybrid ML-FF are <50 K away from the melting temperatures found experimentally via differential scanning calorimetry measurements 39 . Fig.…”
Section: Resultsmentioning
confidence: 64%
“…The reported is averaged over the four (two for NPs with more than 2500 atoms) independent MD simulations carried out for each NP and each ML-FF. For an immediate comparison, we report the experimental melting temperature of bulk FCC Au at atmospheric pressure ( ), and the experimental melting temperatures of Au NPs as a function of the NP size 38 , 39 . For reference, we add the estimates obtained using a classical MD where the interatomic interaction is derived in the second-moment approximation of the tight-binding (TB-SMA) 13 .…”
Section: Resultsmentioning
confidence: 99%
“… Average as a function of NP’s reciprocal radius computed for MD simulations employing the LDA-trained (blue triangles), rPBE-trained (orange triangles) and hybrid (green triangles) ML-FFs. Experimental data for size-selected Au NPs supported on carbon (pink squares) and spherical Au NPs (purple diamonds) are taken from Foster et al 38 and Duan et al 39 , respectively. Grey pentagons refer to the estimates from TB-SMA iterative MD melting simulations from Delgado-Callico et al 13 .…”
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.
“…On average, the rPBE-derived ML-FF predicts 250 ± 50 K lower than the ones predicted by the LDA-derived ML-FF, and 180 ± 40 K lower than the ones predicted by the hybrid ML-FF. Interestingly, the predicted by the hybrid ML-FF are <50 K away from the melting temperatures found experimentally via differential scanning calorimetry measurements 39 . Fig.…”
Section: Resultsmentioning
confidence: 64%
“…The reported is averaged over the four (two for NPs with more than 2500 atoms) independent MD simulations carried out for each NP and each ML-FF. For an immediate comparison, we report the experimental melting temperature of bulk FCC Au at atmospheric pressure ( ), and the experimental melting temperatures of Au NPs as a function of the NP size 38 , 39 . For reference, we add the estimates obtained using a classical MD where the interatomic interaction is derived in the second-moment approximation of the tight-binding (TB-SMA) 13 .…”
Section: Resultsmentioning
confidence: 99%
“… Average as a function of NP’s reciprocal radius computed for MD simulations employing the LDA-trained (blue triangles), rPBE-trained (orange triangles) and hybrid (green triangles) ML-FFs. Experimental data for size-selected Au NPs supported on carbon (pink squares) and spherical Au NPs (purple diamonds) are taken from Foster et al 38 and Duan et al 39 , respectively. Grey pentagons refer to the estimates from TB-SMA iterative MD melting simulations from Delgado-Callico et al 13 .…”
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.
“…38 The X-ray powder diffraction (XRD) patterns show all peaks match with orthorhombic structure of Bi 2 S 3 NRs (JCPDS 84−0279) and cubic structure of Au NCs (JCPDS 89−3697), respectively (Figure 1e). 39,40 In addition, the transmission electron microscope (TEM) image reveals that Au−Bi 2 S 3 HNSCs are entirely composed of Bi 2 S 3 NRs and Au NCs, where Au NCs uniformly distribute on the surface of Bi 2 S 3 NRs, and the average hydrodynamic diameter of Au−Bi 2 S 3 HNSCs is about 34.5 nm with narrow size distribution (Figure 1b,i). Moreover, the high resolution TEM (HR-TEM) images exhibit that the lattice of Au NCs connect with that of Bi 2 S 3 NRs due to the small lattice mismatch and the atomically smooth interfaces between Bi 2 S 3 NRs and Au NCs (Figure 1c,d and Table S1), indicating that Bi 2 S 3 NRs and Au NCs have a crosslinked to form Schottky heterojunction rather than physically mixed or isolated.…”
Despite
the development of nanomaterials with high-Z elements for
radiosensitizers, most of them suffer from their oxygen-dependent
behavior in hypoxic tumor, nonideal selectivity to tumor, or inevasible
damages to normal tissue, greatly limiting their further applications.
Herein, we develop a Schottky-type heterostructure of Au–Bi2S3 with promising ability of reactive free radicals
generation under X-ray irradiation for selectively enhancing radiotherapeutic
efficacy by catalyzing intracellular H2O2 in
tumor. On the one hand, like many other nanomaterials with rich high-Z
elements, Au–Bi2S3 can deposit higher
radiation dose within tumors in the form of high energy electrons.
On the other hand, Au–Bi2S3 can remarkably
improve the utilization of a large number of X-ray-induced low energy
electrons during radiotherapy for nonoxygen dependent free radicals
generation even in hypoxic condition. This feature of Schottky-type
heterostructures Au–Bi2S3 attributes
to the generated Schottky barrier between metal Au and semiconductor
Bi2S3, which can trap the X-ray-generated electrons
and transfer them to Au, resulting in efficient separation of the
electron–hole pairs. Then, because of the matched potential
between the conduction band of Bi2S3 and overexpressed
H2O2 within tumor, the Au–Bi2S3 HNSCs can decompose the intracellular H2O2 into highly toxic •OH for selective
radiosensitization in tumor. As a consequence, this kind of nanoparticle
provides an idea to develop rational designed Schottky-type heterostructures
as efficient radiosensitizers for enhanced radiotherapy of cancer.
“…Due to the dynamical behavior of the particles, this should result in a lower limit for the true melting temperature. Duan et al found a linear dependence of the melting temperature on the inverse particle diameter for round nanoparticles with a diameter larger than 10 nm. For smaller particles, they found a more severe decrease in the melting temperature, which might be related to sintering as the heat produced via sintering may lead to an underestimation of the effective heat needed to melt the particles.…”
Nanoparticles have an immense importance in various fields, such as medicine, catalysis, and various technological applications. Nanoparticles exhibit a significant depression in melting point as their size goes below ≈10 nm. However, nanoparticles are frequently used in high temperature applications such as catalysis where temperatures often exceed several 100 degrees which makes it interesting to study not only the melting temperature depression, but also how the melting progresses through the particle. Using high‐resolution transmission electron microscopy, the melting process of gold nanoparticles in the size range of 2–20 nm Au nanoparticles combined with molecular dynamics studies is investigated. A linear dependence of the melting temperature on the inverse particle size is confirmed; electron microscopy imaging reveals that the particles start melting at the surface and the liquid shell formed then rapidly expands to the particle core.
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