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Nanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations, with the former offering efficiency but limited reliability, and the latter providing accuracy but restricted to systems of relatively small sizes. Herein, we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a training dataset. This includes small nanoclusters, nanoparticles, as well as surface and bulk systems with periodic boundary conditions. The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes. Through rigorous validation, we have shown that our trained machine learning potential is capable of effectively driving molecular dynamics simulations of nanoparticles across a wide temperature range, especially within the nanoscale regime. Remarkably, this is achieved while preserving the accuracy typically associated with ab initio methods.

Abstract Enhancements of edge zonal flows, radial electric fields, and turbulence are observed in electron cyclotron resonance heating-heated plasmas (Zhao et al 2013 Nucl. Fusion 53 083011). In this paper, the effects of sawtooth heat pulses on flows and turbulence are presented. These experiments are performed using multiple Langmuir probe arrays in the edge plasmas of the HL-2A tokamak. The edge zonal flows, radial electric fields, and turbulence are all enhanced by sawteeth. Propagation of the zonal flow and turbulence intensities is also observed. The delay time of the maximal intensity of the electric fields, zonal flows, and turbulence with respect to the sawtooth crashes is estimated as ∼1 ms and comparable to that of the sawtooth-triggered intermediate phases. Not only the zonal flows but also the radial electric fields lag behind the turbulence. Furthermore, the intensities of both the zonal flows and electric fields nearly linearly increase/decrease with the increase/decrease of the turbulence intensity. A double-source predator–prey model analysis suggests that a relatively strong turbulence source may contribute to the dominant zonal flow formation during sawtooth cycles.

An understanding of solid-liquid interfaces is of great importance for fundamental research as well as industrial applications. However, it has been very challenging to directly image solid-liquid interfaces with high resolution, thus their structure and properties are often unknown. Here, we report a quasi-liquid phase between metal (In, Sn) nanoparticle surfaces and an aqueous solution observed using liquid cell transmission electron microscopy. Our real-time high-resolution imaging reveals a thin layer of liquid-like materials at the interfaces with the frequent appearance of small In nanoclusters. Such a quasi-liquid phase serves as an intermediate for the mass transport from the metal nanoparticle to the liquid. Density functional theory-molecular dynamics simulations demonstrate that the positive charges of In ions greatly contribute to the stabilization of the quasi-liquid phase on the metal surface.

C17H16N2O8, monoclinic, P21/c (no. 14), a = 7.0825(4) Å, b = 15.6168(9) Å, c = 15.1086(9) Å, β = 93.3830(10)°, V = 1668.19(17) Å3, Z = 4, Rgt(F) = 0.0543, wRref(F2) = 0.1443, T = 170.15 K.

C27H32FN3O9, triclinic, P1‾$P\overline{1}$ (no. 2), a = 8.0932(4) Å, b = 11.8392(7) Å, c = 14.6782(8) Å, α = 87.860(3)∘, β = 76.795(2)∘, γ = 78.317(2)°, V = 1340.81(13) Å3, Z = 2, Rgt (F) = 0.0559, wRref (F2) = 0.1537, T = 170 K.

Fluidic thrust vectoring technology can provide good stealth performance and longitudinal maneuvering moment for supersonic tailless configurations, with advantages of fast response and light weight. The bypass shock vectoring nozzle does not need to draw air from the engine, which overcomes the problem of increasing the engine flow to increase the vector angle and reduces the burden on the engine. A study of the effect of bypass position on the performance of two-dimensional shock vectoring nozzle was conducted to lay the foundation for a better understanding of the performance of this nozzle and its practical applications. The results show that the throat bypass jet has the characteristics of both the shock vector and the throat skewing method, and that the inlet bypass jet performs better in the over-expanded condition, while the throat bypass jet has more advantages in the under-expanded condition. The separation mode after the jet significantly affects the nozzle vector performance. Closed separation makes the nozzle vector performance decrease significantly, and the nozzle pressure ratio related to the sudden change in vector performance of the throat bypass nozzle is smaller than that of the inlet bypass nozzle. In practice, it should be avoided to switch the separation mode from open separation to closed separation. Matching different bypass modes according to different expansion states can maximize the bypass shock vectoring nozzle performance.

The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant Kβ XES of 3d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 (AXEAP2), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3d electron spin state, 3d–3p electron exchange force and Kβ emission core-hole lifetime.