Effects of Temperature and Grain Size on Diffusivity of Aluminium: Electromigration Experiment and Molecular Dynamic Simulation

Understanding the atomic diffusion features in metallic material is significant to explain the diffusion-controlled physical processes. In this paper, using electromigration experiments and molecular dynamic (MD) simulations, we investigate the effects of grain size and temperature on the self-diffusion of polycrystalline aluminum (Al). The mass transport due to electromigration are accelerated by increasing temperature and decreasing grain size. Magnitudes of effective diffusivity (Deff) and grain boundary diffusivity (DGBs) are experimentally determined, in which the Deff changes as a function of grain size and temperature, but DGBs is independent of the grain size, only affected by the temperature. Moreover, MD simulations of atomic diffusion in polycrystalline Al demonstrate those observations from experiments. Based on MD results, the Arrhenius equation of DGBs and empirical formula of the thickness of grain boundaries at various temperatures are obtained. In total, Deff and DGBs obtained in the present study agree with literature results, and a comprehensive result of diffusivities related to the grain size is presented.

Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion

Identifying the phenotypes and interactions of various cells is the primary objective in cellular heterogeneity dissection. A key step of this methodology is to perform unsupervised clustering, which, however, often suffers challenges of the high level of noise, as well as redundant information. To overcome the limitations, we proposed self-diffusion on local scaling affinity (LSSD) to enhance cell similarities’ metric learning for dissecting cellular heterogeneity. Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. Our approach implements the self-diffusion process by propagating the affinity matrices to further improve the cell similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness, we applied LSSD on two simulated and four real scRNA-seq datasets. Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability.

Defect-controlled softness, diffusive permeability, and mesh-topology of metallo-supramolecular hydrogels

In a model 4-arm pEG supramolecular network, connectivity defects are systematically introduced with different ratios of 8-arm pEG, resulting in intra-molecular loops, and providing a softer polymer network and higher self-diffusion coefficients.

Molecular Dynamics Study of Diffusion Coefficient for Low-Temperature Dusty Plasmas in the Presence of External Electric Fields

The effects of external electric field (E) on the diffusion coefficient of dust particles in low-temperature dusty plasmas (LT-DPs) have been computed through nonequilibrium molecular dynamics (NEMD) simulations. The new simulation result was obtained by employing the integral formula of velocity autocorrelation functions (VACF) using the Green-Kubo relation. The normalized self-diffusion coefficient (D*) is investigated for different combinations of plasma coupling (Γ) and Debye screening (κ) parameters. The simulation outcome shows that the decreasing position of D* shifts toward Γ and also increased with the increase of κ. The D* linearly decreased with Γ and increased when applied external E increases. It is observed that the increasing trend of D* depends on the E strength. These investigations show that the present algorithm provides precise data with fast convergence and effects of κ, Γ, E. It is shown that the current NEMD techniques with applied external E can be employed to understand the microscopic mechanism of dusty plasmas.

Alloying and Phase Transformation of Fe/FeNi Core/Alloy Nanoparticles at High Temperatures

This work explores how to form and tailor the alloy composition of Fe/FexNi1-x core/alloy nanoparticles by annealing a pre-formed particle at elevated temperatures between 180 – 325 oC. This annealing allowed for a systematic FeNi alloying at a nanoparticle whose compositions and structure began as a alpha-Fe rich core, and a thin gamma-Ni rich shell, into mixed phases resembling gamma-FeNi3 and gamma-Fe3Ni2. This was possible in part by controlling surface diffusion via annealing temperature, and the enhanced diffusion at the many grain boundaries of the nanoparticle. Lattice expansion and phase change was characterized by powder X-ray diffraction (XRD), and composition was monitored by X-ray photoelectron spectroscopy (XPS). Of interest is that no phase precipitation was observed (i.e., heterostructure formation) in this system and the XRD results suggest that alloying composition or alloy gradient is uniform. This uniform alloying was considered using calculations of bulk diffusion and grain boundary diffusion for Fe and Ni self-diffusion, as well as Fe-Ni impurity diffusion is provided. In addition, alloying was further considered by calculations for Fe-Ni mixing enthalpy (Hmix) and phase segregation enthalpy (HSeg) using the Miedema model, which allowed for the consideration of alloying favorability or core-shell segregation in the alloying, respectively. Of particular interest is the formation of stable metal carbides compositions, which suggest that the typically inert organic self-assembled monolayer encapsulation can also be internalized.

Lithium Tracer Diffusion in Sub-Stoichiometric Layered Lithium-Metal-Oxide Compounds

Cathode materials based on lithium-metal-oxide compounds are an essential technical component for lithium-ion batteries, which are still being researched and continuously improved. For a fundamental understanding of kinetic processes at and in electrodes the Li diffusion is of high relevance. Most cathode materials are based on the layered LiCoO2 (LCO) and LiNi0.33Mn0.33Co0.33O2 (NMC333). In the present study Li tracer self-diffusion is investigated in polycrystalline sintered bulk samples of sub-stoichiometric Li0.9CoO2 at 145 °C ≤ T ≤ 350 °C and compared to Li0.9Ni0.33Mn0.33Co0.33O2 in the temperature range between 110 and 350 °C. For analysis, stable 6Li tracers are used in combination with secondary ion mass spectrometry (SIMS). The Li tracer diffusivities D* of both compounds with a sub-stoichiometric Li concentration are identical within error limits and can be described by the Arrhenius law with an activation enthalpy of (0.76 ± 0.13) eV for LCO and (0.85 ± 0.03) eV for NMC333, which is interpreted as the migration energy of a single Li vacancy. This means that a modification of the transition metal (M) layer composition within the LiMO2 structure does not significantly influence lithium diffusion in the temperature range investigated.

The Measurement of Tortuosity of Porous Media Using Imaging, Electrical Measurements, and Pulsed Field Gradient NMR

Tortuosity, in general characterizes the geometric complexity of porous media. It is considered as one of the key factors in characterizing the heterogonous structure of porous media and has significant implications for macroscopic transport flow properties. There are four widely used definitions of tortuosity, that are relevant to different fields from hydrology to chemical and petroleum engineering, which are: geometric, hydraulic, electrical, and diffusional. Recent work showed that hydraulic, electrical and diffusional tortuosity values are roughly equal to each other in glass beads. Nevertheless, the relationship between the different definitions of Tortuosity in natural rocks is not well understood yet. Understanding the relationship between the different Tortuosity definitions in rocks can help to establish a workflow that allows us to estimate other types from the available technique. Therefore, the objective of this study is to investigate the relationship between the different tortuosity definitions in natural rocks. A major focus of this work is to utilize Nuclear Magnetic Resonance (NMR) technology to estimate Tortuosity. Such technique has been traditionally used to obtain diffusional tortuosity which can be defined as the ratio of the free fluid self-diffusion coefficient to the restricted fluid self-diffusion coefficient inside the porous media. In this study, the following techniques were used to quantify hydraulic, electrical, and diffusional tortuosity respectively on the same rock sample: (1) Microcomputed Tomography 3D imaging (2) Four-Electrodes resistivity measurements (3) Pulsed-Field Gradient Nuclear Magnetic Resonance (PFG NMR). PFG NMR is very powerful, non-invasive technique employed to measure the self-diffusion coefficient for free and confined fluids. The measurements were done based on two carbonate rock core plugs characterized by variable porosity, permeability and texture complexity. Results show that PFG NMR can be applied directionally to quantify the pore network anisotropy created by fractures. For both samples, hydraulic tortuosity was found to have the lowest magnitude compared to geometric, electrical and diffusional tortuosity. This could be explained by the more heterogeneous microstructure of carbonate rocks. NMR technique has however advantages over the other electrical and imaging techniques for tortuosity characterization: it is faster, non-destructive and can be applied in well bore environment (in situ). We therefore conclude that NMR can provide a tool for estimating not only diffusional tortuosity but also for indirectly obtaining hydraulic and electrical tortuosity.