We propose a method of generating spin currents in monolayer graphene through adiabatic quantum pumping by two oscillating potentials. Spin splitting is induced in the graphene layer by ferromagnetic proximity. The pumped charge and spin currents are sensitive functions of the Fermi energy, which can thus be used to control the degree of polarization. The predicted pumped currents are measurable using the current technology. The proposed method is useful in the development of graphene spintronics.

Mechanical damage is a major factor limiting the long-term stability of solid oxide fuel cells (SOFCs). Here, the mechanical stability of planar SOFCs consisting of Ni-YSZ anode/YSZ electrolyte/LSM-YSZ cathode (Ni=Nickel, YSZ=yttria-stabilized zirconia, LSM=lanthanum strontium manganite) is analyzed by a structural mechanics model with composition dependent mechanical properties. Influencing factors considered include: the Ni and LSM volume fractions, the thicknesses of anode, cathode and electrolyte layers, and the cell types of anode-, cathode-, and electrolyte-supported designs. It is found that (i) the anode failure probability increases with the Ni content. However, SOFCs remain mechanically safe if the Ni volume fraction is below 65%. (ii) An LSM volume fraction of over 40% is required to maintain the mechanical integrity of cathode. (iii) For an anode-supported cell with a 20 μm thick electrolyte, the anode thickness should be more than 0.5 mm to be mechanically stable. (iv) The anode-supported cell is found to be mechanically safer than that of the electrolyte-supported cell.

We investigated the adsorption of three aromatic amino acids—phenylalanine, tyrosine, and tryptophan—on the sidewalls of a number of representative single-walled carbon nanotubes (SWNTs) using density-functional tight-binding calculations, complemented by an empirical dispersion correction. The armchair (n, n) SWNTs (n = 3–12) and zigzag (n, 0) SWNTs (n = 4–12) were thoroughly examined. We found that the most stable amino acid/SWNT complexes for different SWNTs have similar local structures, and that the distance between the amino acid and SWNT is about 3 Å. Owing to the π–π and H–π stacking interactions, the benzene and indole rings are not exactly parallel to the SWNTs but instead lie at a small angle. We also investigated the diameter and chirality dependences of binding energies and found that SWNT (5, 0) has an especially large binding energy that can be used for SWNT identification or selection.

Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. We first test SCCOP on 35 typical compounds to demonstrate its generality. As a case study, we apply our approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our framework is generally applicable to all types of systems for precise and efficient structural search, providing insights into the relationship between ML-extracted structural features and physical properties.