We found a giant Seebeck effect in semiconducting single-wall carbon nanotube (SWCNT) films, which exhibited a performance comparable to that of commercial Bi 2 Te 3 alloys. Carrier doping of semiconducting SWCNT films further improved the thermoelectric performance. These results were reproduced well by first-principles transport simulations based on a simple SWCNT junction model. These findings suggest strategies that pave the way for emerging printed, allcarbon, flexible thermoelectric devices.

Nanostructural effects on the thermoelectric power of graphene nanoribbons (GNRs) are revealed through first-principles simulation based on the density functional theory combined with nonequilibrium Green's function theory. The thermoelectric power of GNRs exhibits essentially different behavior depending on their edge structure and ribbon width. For zigzag-edged GNRs, the thermoelectric power shows a peculiar energy dependence originating from edge-localized electronic states with energy near the Fermi level. On the other hand, for armchair-edged GNRs (AGNRs), the thermoelectric power is classified into three categories depending on the ribbon width. Among AGNRs with similar ribbon width, an AGNR belonging to a category satisfying mod(N a, 3)=1 displays the largest thermoelectric power, where N a is the integer determining the ribbon width of an AGNR.

We demonstrate a drastic improvement in the efficiency of rare-element-free graphene nanomesh (GNM) magnets with saturation magnetization values as large as ∼10−4 emu/mm2, which are 10–100 times greater than those in previous GNM magnets hydrogenated by only annealing under a hydrogen molecule (H2) atmosphere, even at room temperature. This improvement is realized by a significant increase in the area of the mono-H-terminated pore edges by using hydrogen silsesquioxane resist treatment with electron beam irradiation, which can produce mono-H by detaching H-silicon (Si) bonds. This result must open the door for industrial applications of graphene magnets to rare-element-free magnetic and spintronic systems.

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This study proposes a benefit estimation method that considers travel time reliability. The proposed method is based on a network model that is formulated as a utility maximization problem with constraints. Since this utility maximization problem has the same equilibrium conditions as a multiclass user equilibrium traffic assignment problem with elastic demand, both transport demand forecasting and benefit estimation can be carried out in the same framework. By assuming a certain form for the utility function, the road network model can estimate the prohibitive price, so the proposed method is convenient for estimating opportunity loss due to disruption of origin-destination connection in the event of a natural disaster. Furthermore, the values of travel time and travel time reliability are estimated endogenously in the proposed method; thus, changes in these values can be reflected in the benefit estimation. A numerical experiment demonstrates the method presented in this study.