Engineering topics
Ding, Zejun
Publications and source records attributed to Ding, Zejun.
Measurement of the Low-Energy Electron Inelastic Mean Free Path in Monolayer Graphene
Measuring the electron-transport properties of substrate-supported nanomaterials with the traditional two-point comparison method is difficult at electron energies below 50 eV, where core-level signals are too feeble to be detected against the strong secondary-electron background. Herein, a data-driven spectral analysis technique is used to study the low-energy electron-transport properties of substrate-supported target nanomaterials, while eliminating the influence of the substrate signal. Applying this technique, the electron-transport properties of the effective attenuation length and the inelastic mean free path (IMFP) can be determined with extremely high efficiency over the entire measured energy range of 6–600 eV. Further, these results show excellent agreement with other experimental and theoretical results. In this work, significant differences are observed between monolayer graphene and the bulk graphite IMFP, which illustrates the importance of the nanometer effect in the electron-transport properties of the material. Furthermore, this technique is readily applicable to any ultrathin material that can be transferred onto a polycrystalline gold substrate.
Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework
The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters.
Observation of Plasmon Energy Gain for Emitted Secondary Electron in Vacuo
Plasmon gain by core-level electrons or elastic electrons observed in past studies seems to be of no practical value in material characterization, mainly because of their ultralow signal intensities. Nevertheless, in the emission spectra of Au samples, we have observed plasmon gain in secondary electrons. Here, the electrons gain energy from surface plasmons after escaping from the surface and thereby only carry surface-plasmon information in the vacuum above the surface. Because the intensity of the emitted SEs is strong, rivaling that of core-level or elastic electrons, the observed phenomenon has in practice the potential to image directly in space the surface plasmon near but exterior to the metal surface.
Structural ordering at solid-liquid interfaces in Al-Sm system: A molecular-dynamics study
The structural ordering at solid-liquid interfaces far from equilibrium is studied with molecular dynamics simulations for the Al-Sm system. Using the van-Hove self-correlation function as the criterion to identify attachment/detachment events that occur at the interface, we are able to determine the time-dependent interface position, and characterize the detailed interfacial structure ordering surrounding the attached atoms. For the interface between an undercooled Al90Sm10 liquid and a metastable cubic structure, the solid induces the crystalline order of the cubic phase in the liquid layers, promoting the continuous growth of the crystal phase. When the same liquid is put in contact with f.c.c. Al, Sm from the liquid can still attach to the solid interface despite its insolubility in the Al lattice. Non-f.c.c. order is revealed surrounding the attached Sm atoms. Lastly, we show that the local structure ordering at interface is highly correlated to solid packing and liquid ordering.
Cooling rate dependence of structural order in Al{sub 90}Sm{sub 10} metallic glass
The atomic structure of Al{sub 90}Sm{sub 10} metallic glass is studied using molecular dynamics simulations. By performing a long sub-T{sub g} annealing, we developed a glass model closer to the experiments than the models prepared by continuous cooling. Using the cluster alignment method, we found that “3661” cluster is the dominating short-range order in the glass samples. The connection and arrangement of “3661” clusters, which define the medium-range order in the system, are enhanced significantly in the sub-T{sub g} annealed sample as compared with the fast cooled glass samples. Unlike some strong binary glass formers such as Cu{sub 64.5}Zr{sub 35.5}, the clusters representing the short-range order do not form an interconnected interpenetrating network in Al{sub 90}Sm{sub 10,} which has only marginal glass formability.