arXiv subjects

Xuyang Zhou

Publications and source records attributed to Xuyang Zhou.

22 records · Page 2Linked to original sources

arXiv2021

Understanding alkali contamination in colloidal nanomaterials to unlock grain boundary impurity engineering

Metal nano-aerogels combine a large surface area, a high structural stability, and a high catalytic activity towards a variety of chemical reactions. The performance of such nanostructures is underpinned by the atomic-level distribution of their constituents. Yet monitoring their sub-nanoscale structure and composition to guide property optimization remains extremely challenging. Here, we synthesized Pd nano-aerogels from a K2PdCl4 precursor and two different NaBH4 reductant concentrations in distilled water. Atom probe tomography reveals that the aerogel is poly-crystalline and that impurities (Na, K) are integrated from the solution into grain boundaries. Ab initio calculations indicate that these impurities preferentially bound to the Pd-metal surface and are ultimately found in grain boundaries forming as the particles coalesce during synthesis, with Na atoms thermodynamically equilibrating with the surrounding solution and K atoms remaining between growing grains. If controlled, impurity integration, i.e. grain boundary decoration, may offer opportunities for designing new nano-aerogels.

cond-mat.mtrl-sci↗

arXiv2021

Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data

Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB-specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.

cond-mat.mtrl-sci↗

arXiv2021

Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

Nanoscale L12-type ordered structures are widely used in face-centred cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyse the complete point cloud ($>10$ million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12-type $δ^\prime$-Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.

cond-mat.mtrl-sci↗

arXiv2021

Aluminum depletion induced by complex co-segregation of carbon and boron in a Σ 5 [3 1 0] bcc-iron grain boundary

The local variation of grain boundary atomic structure and chemistry caused by segregation of impurities influences the macroscopic properties of poylcrystalline materials. Here, the effect of co-segregation of carbon and boron on the depletion of aluminum at a $Σ5\,(3\,1\,0\,) [0\,0\,1]$ tilt grain boundary in a $α-$Fe-$4~at.~\%$Al bicrystal was studied by combining atomic resolution scanning transmission electron microscopy, atom probe tomography and density functional theory calculations. The atomic grain boundary structural units mostly resemble kite-type motifs and the structure appears disrupted by atomic scale defects. Atom probe tomography reveals that carbon and boron impurities are co-segregating to the grain boundary reaching levels of >1.5 at.\%, whereas aluminum is locally depleted by approx. 2~at.\%. First-principles calculations indicate that carbon and boron exhibit the strongest segregation tendency and their repulsive interaction with aluminum promotes its depletion from the grain boundary. It is also predicted that substitutional segregation of boron atoms may contribute to local distortions of the kite-type structural units. These results suggest that the co-segregation and interaction of interstitial impurities with substitutional solutes strongly influences grain boundary composition and with this the properties of the interface.

cond-mat.mtrl-sci↗