Defining the proximal interaction networks of Arf GTPases reveals a mechanism for the regulation of PLD1 and PI4KB.

The Arf GTPase family is involved in a wide range of cellular regulation including membrane trafficking and organelle-structure assembly. Here, we have generated a proximity interaction network for the Arf family using the miniTurboID approach combined with TMT-based quantitative mass spectrometry. Our interactome confirmed known interactions and identified many novel interactors that provide leads for defining Arf pathway cell biological functions. We explored the unexpected finding that phospholipase D1 (PLD1) preferentially interacts with two closely related but poorly studied Arf family GTPases, ARL11 and ARL14, showing that PLD1 is activated by ARL11/14 and may recruit these GTPases to membrane vesicles, and that PLD1 and ARL11 collaborate to promote macrophage phagocytosis. Moreover, ARL5A and ARL5B were found to interact with and recruit phosphatidylinositol 4-kinase beta (PI4KB) at trans-Golgi, thus promoting PI4KB's function in PI4P synthesis and protein secretion.

Ab initio predictions of graphite-like phase with anomalous grain boundaries and flexoelectricity from collapsed carbon nanotubes.

Although large-radius carbon nanotubes (CNTs) are now available in macroscopic quantities, little is known about their condensed phase. Large-scale density functional theory calculations predict a low energy phase in which the same-diameter "dog-bone" collapsed CNTs form a graphite-like phase with complex, anomalous grain boundaries (GBs). The excess GB volume does not prevent the strong van der Waals coupling of the flattened CNT sides into AB stacking. The associated GB energetics is dominated by the van der Waals energy penalty and high curvature bending of the loop CNT edges, which exhibit reactivity and flexoelectricity. The large density and superior mechanical rigidity of the proposed microstructural organization as well as the GB flexoelectricity are desirable properties for developing ultra-strong composites based on large-radius CNTs.

Tuning interfacial thermal conductance of graphene embedded in soft materials by vacancy defects.

Nanocomposites based on graphene dispersed in matrices of soft materials are promising thermal management materials. Their effective thermal conductivity depends on both the thermal conductivity of graphene and the conductance of the thermal transport across graphene-matrix interfaces. Here, we report on molecular dynamics simulations of the thermal transport across the interfaces between defected graphene and soft materials in two different modes: in the "across" mode, heat enters graphene from one side of its basal plane and leaves through the other side; in the "non-across" mode, heat enters or leaves graphene simultaneously from both sides of its basal plane. We show that as the density of vacancy defects in graphene increases from 0% to 8%, the conductance of the interfacial thermal transport in the "across" mode increases from 160.4 ± 16 to 207.8 ± 11 MW/m(2) K, while that in the "non-across" mode increases from 7.2 ± 0.1 to 17.8 ± 0.6 MW/m(2) K. The molecular mechanisms for these variations of thermal conductance are clarified using the phonon density of states and structural characteristics of defected graphene. On the basis of these results and effective medium theory, we show that it is possible to enhance the effective thermal conductivity of thermal nanocomposites by tuning the density of vacancy defects in graphene despite the fact that graphene's thermal conductivity always decreases as vacancy defects are introduced.

Data-driven prediction of grain boundary segregation and disordering in high-entropy alloys in a 5D space.

Grain boundaries (GBs) can critically influence the microstructural evolution and various material properties. However, a fundamental understanding of GBs in high-entropy alloys (HEAs) is lacking because of the complex couplings of the segregations of multiple elements and interfacial disordering, which can generate new phenomena and challenge the classical theories. Here, by combining large-scale atomistic simulations and machine learning models, we demonstrate the feasibility of predicting the GB properties as functions of four independent compositional degrees of freedom and temperature in a 5D space, thereby enabling the construction of GB diagrams for quinary HEAs. The artificial neural network (ANN), support vector machine (SVM), regression tree, and rational quadratic Gaussian models are trained and tested, and the ANN model yields the best machine learning based predictions. A data-driven discovery further reveals new coupled segregation and disordering effects in HEAs. For instance, interfacial disordering can enhance the co-segregation of Cr and Mn at CrMnFeCoNi GBs. A physics-informed data-driven model is constructed to provide more physical insights and better transferability. Density functional theory (DFT) calculations are used to validate the prediction generality and reveal the underlying segregation mechanisms. This study not only provides a new paradigm enabling the prediction of GB properties in a 5D space, but also uncovers new GB segregation phenomena in HEAs beyond the classical GB segregation models.

Disconnection-Mediated Transition in Segregation Structures at Twin Boundaries.

Twin boundaries play an important role in the thermodynamics, stability, and mechanical properties of nanocrystalline metals. Understanding their structure and chemistry at the atomic scale is key to guide strategies for fabricating nanocrystalline materials with improved properties. We report an unusual segregation phenomenon at gold-doped platinum twin boundaries, which is arbitrated by the presence of disconnections, a type of interfacial line defect. By using atomistic simulations, we show that disconnections containing a stacking fault can induce an unexpected transition in the interfacial-segregation structure at the atomic scale, from a bilayer, alternating-segregation structure to a trilayer, segregation-only structure. This behavior is found for faulted disconnections of varying step heights and dislocation characters. Supported by a structural analysis and the classical Langmuir-McLean segregation model, we reveal that this phenomenon is driven by a structurally induced drop of the local pressure across the faulted disconnection accompanied by an increase in the segregation volume.

Discovery of electrochemically induced grain boundary transitions.

Electric fields and currents, which are used in innovative materials processing and electrochemical energy conversion, can often alter microstructures in unexpected ways. However, little is known about the underlying mechanisms. Using ZnO-Bi2O3 as a model system, this study uncovers how an applied electric current can change the microstructural evolution through an electrochemically induced grain boundary transition. By combining aberration-corrected electron microscopy, photoluminescence spectroscopy, first-principles calculations, a generalizable thermodynamic model, and ab initio molecular dynamics, this study reveals that electrochemical reduction can cause a grain boundary disorder-to-order transition to markedly increase grain boundary diffusivities and mobilities. Consequently, abruptly enhanced or abnormal grain growth takes place. These findings advance our fundamental knowledge of grain boundary complexion (phase-like) transitions and electric field effects on microstructural stability and evolution, with broad scientific and technological impacts. A new method to tailor the grain boundary structures and properties, as well as the microstructures, electrochemically can also be envisioned.

Interfacial superstructures and chemical bonding transitions at metal-ceramic interfaces.

Metal-ceramic interfaces are scientifically interesting and technologically important. However, the transition of chemical bonding character from a metal to a nonoxide ceramic is not well understood. The effects of solute segregation and interfacial structural transitions are even more elusive. In this study, aberration-corrected electron microscopy is combined with atomic-resolution energy-dispersive x-ray and electron energy loss spectroscopy to investigate Ti-, V-, and Cr-segregated WC-Co interfaces as model systems. The experiments reveal the general anisotropic formation of reconstructed trilayer-like superstructures with segregant-specific compositional profiles that facilitate the transition from covalent to metallic electronic structures. Density functional theory calculations confirm the gradual increasing metallicity from WC to Co in the interfacial trilayers via increasing metallic solute concentration. This study uncovers unprecedented details of the sophisticated interfacial superstructures at metal-ceramic interfaces. It sheds light on how a metal transits to a ceramic at a "general" interface with strong segregation.