arXiv subjects

Naoki Fushimi

Publications and source records attributed to Naoki Fushimi.

3 recordsLinked to original sources

arXiv2026

Integration of diamond nanobeams with SnVs on Al2O3 waveguides for scalable quantum photonic chip application

Tin vacancy (SnV) centers in diamond are promising solid state qubits for integrated quantum photonics. Here, we fabricate and characterize a diamond on Al2O3 dual taper waveguide structure containing SnV centers, demonstrating optical coupling between the diamond nanobeam and the underlying Al2O3 waveguide. The devices are realized using a bilayer fabrication approach compatible with wafer scale lithography. Clear guided SnV- emission is observed in all optically active devices, indicating effective optical coupling in the integrated structure. These results demonstrate a scalable fabrication approach toward integrating diamond color centers with photonic waveguides.

physics.optics↗

arXiv2022

Josephson junctions of Weyl semimetal $\text{WTe}_2$ induced by spontaneous nucleation of $\text{PdTe}$ superconductor

We report on the fabrication of Josephson junction devices with weak links utilizing the Weyl and higher-order topological semimetal $\text{WTe}_2$. We show that $\text{WTe}_2\text{/Pd}$ contact annealed at a low temperature of 80{\deg}C did not exhibit superconducting properties because neither $\text{WTe}_2$ nor Pd are superconductors in the ground state. Upon 180{\deg}C annealing, spontaneous formation of superconducting $\text{PdTe}$ due to Pd diffusion enabled us to obtain the interface between $\text{WTe}_2$ and superconductor suitable for the Josephson junction. This result is a facile technique to make a Josephson junction and induce Cooper pairs into topological telluride semimetals.

cond-mat.supr-con↗

arXiv2019

Optimization of heterogeneous ternary Li3PO4-Li3BO3-Li2SO4 mixture for Li-ion conductivity by machine learning

Mixing heterogeneous Li-ion conductive materials is one of potential ways to enhance the Li-ion conductivity more than that of the parent materials. However, the development of the mixtures had not exhibited significant progress because it is a formidable task to cover the vast possible composition of the parent materials using traditional ways. Here, we introduce a fashion based on machine learning to optimize the composition ratio of ternary Li3PO4-Li3BO3-Li2SO4 mixture for its Li-ion conductivity. According to our results, the optimum composition of the ternary mixture system is 25:14:61 (Li3PO4: Li3BO3: Li2SO4 in mol%), whose Li-ion conductivity is measured as 4.9 x 10E-4 S/cm at 300 °C. Our X-ray structure analysis indicates that Li-ion conductivity in the mixing systems is enhanced by virtue of the coexistence of two or more phases. Although the mechanism enhancing Li-ion conductivity is not simple, our results demonstrate the effectiveness of machine learning for the development of materials.

cond-mat.mtrl-sci↗