Thermal conductivity of irregularly shaped nanoparticles from equilibrium molecular dynamics.

The computation of thermal conductivity for finite nanoparticulate systems, particularly those of irregular shapes, poses significant challenges. The nonequilibrium molecular dynamics (NEMD) methods has been extensively utilized in numerous prior studies for the computation of thermal conductivity of nanoparticles. One of our recent works (Donget al2021Phys. Rev.B103035417) proposed that equilibrium molecular dynamics (EMD) methods can be used for the simulation of thermal conductivity of finite-scale systems and demonstrated their equivalence to NEMD methods. In this study, we investigated the application of the (EMD) approach for the computation of thermal conductivity in zero-dimensional nanoparticles. In our initial step, we merged both methodologies to substantiate the equivalence in thermal conductivity calculation for cube and cylinder nanoparticles. After filtering the data, we confirmed the usefulness of EMD for evaluating the thermal conductivity of zero-dimensional materials. The NEMD method faces challenges in accurately predicting thermal conductivity in nanoparticle systems with a varying cross-sectional area along the transport direction, whereas EMD methods can be utilized to estimate thermal conductivity when the volume is known. In a subsequent study, we used the state-of-the-art machine learning potential to calculate the thermal conductivity of spherical nanoparticles and compared the results with those obtained using the classical Tersoff potential. Ultimately, we predicted the thermal conductivity of nanoparticles with various geometries in all directions. Our findings collectively demonstrate the simplicity and effectiveness of employing EMD methods for calculating thermal conductivity in nanoparticle systems, thereby opening up new avenues for investigating thermal transport properties in particle systems as well as nanopders.

Earthquake-enhanced dissolved carbon cycles in ultra-deep ocean sediments.

Hadal trenches are unique geological and ecological systems located along subduction zones. Earthquake-triggered turbidites act as efficient transport pathways of organic carbon (OC), yet remineralization and transformation of OC in these systems are not comprehensively understood. Here we measure concentrations and stable- and radiocarbon isotope signatures of dissolved organic and inorganic carbon (DOC, DIC) in the subsurface sediment interstitial water along the Japan Trench axis collected during the IODP Expedition 386. We find accumulation and aging of DOC and DIC in the subsurface sediments, which we interpret as enhanced production of labile dissolved carbon owing to earthquake-triggered turbidites, which supports intensive microbial methanogenesis in the trench sediments. The residual dissolved carbon accumulates in deep subsurface sediments and may continue to fuel the deep biosphere. Tectonic events can therefore enhance carbon accumulation and stimulate carbon transformation in plate convergent trench systems, which may accelerate carbon export into the subduction zones.

Investigation of the mechanical and transport properties of InGeX3 (X = S, Se and Te) monolayers using density functional theory and machine learning.

Recently, novel 2D InGeTe3 has been successfully synthesized and attracted attention due to its excellent properties. In this study, we investigated the mechanical properties and transport behavior of InGeX3 (X = S, Se and Te) monolayers using density functional theory (DFT) and machine learning (ML). The key physical parameters related to mechanical properties, including Poisson's ratio, elastic modulus, tensile strength and critical strain, were revealed. Using a ML method to train DFT data, we developed a neuroevolution-potential (NEP) to successfully predict the mechanical properties and lattice thermal conductivity. The fracture behavior predicted using NEP-based MD simulations in a large supercell containing 20 000 atoms could be verified using DFT. Due to the effects of size, these predicted physical parameters have a slight difference between DFT and ML methods. At 300 K, these monolayers exhibited a low thermal conductivity with the values of 13.27 ± 0.24 W m-1 K-1 for InGeS3, 7.68 ± 0.30 W m-1 K-1 for InGeSe3, and 3.88 ± 0.09 W m-1 K-1 for InGeTe3, respectively. The Boltzmann transport equation (BTE) including all electron-phonon interactions was used to accurately predict the electron mobility. Compared with InGeS3 and InGeSe3, the InGeTe3 monolayer showed flexible mechanical behavior, low thermal conductivity and high mobility.