On the Size of Superconducting Islands on the Density-Wave Background in Organic Metals

Most high-Tc superconductors are spatially inhomogeneous. Usually, this heterogeneity originates from the interplay of various types of electronic ordering. It affects various superconducting properties, such as the transition temperature, the magnetic upper critical field, the critical current, etc. In this paper, we analyze the parameters of spatial phase segregation during the first-order transition between superconductivity (SC) and a charge- or spin-density wave state in quasi-one-dimensional metals with imperfect nesting, typical of organic superconductors. An external pressure or another driving parameter increases the transfer integrals in electron dispersion, which only slightly affects SC but violates the Fermi surface nesting and suppresses the density wave (DW). At a critical pressure Pc, the transition from a DW to SC occurs. We estimate the characteristic size of superconducting islands during this phase transition in organic metals in two ways. Using the Ginzburg-Landau expansion, we analytically obtain a lower bound for the size of SC domains. To estimate a more specific interval of the possible size of the superconducting islands in (TMTSF)2PF6 samples, we perform numerical calculations of the percolation probability via SC domains and compare the results with experimental resistivity data. This helps to develop a consistent microscopic description of SC spatial heterogeneity in various organic superconductors.

Automated summary

Most high-Tc superconductors are spatially inhomogeneous due to the interplay of electronic ordering. This paper analyzes the parameters of spatial phase segregation during the first-order transition in organic superconductors. The transition from a charge- or spin-density wave state to superconductivity is influenced by external factors, such as pressure, that affect the nesting of Fermi surfaces and suppress the density wave. The size of superconducting islands during this phase transition is estimated using both analytical and numerical methods.