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Finite-Frequency Dissipation in Two-Dimensional Superconductors with Disorder at the Nanoscale
Giulia Venditti1, Ilaria Maccari1, Marco Grilli1
1Dipartimento di Fisica, Università di Roma Sapienza, Piazzale Aldo Moro, 5, I-00185 Roma, Italy.
Disordered two-dimensional superconductors exhibit unique transitions due to filamentary clusters. Dissipation effects, including vortices, influence cluster strengthening and superfluid stiffness below the transition.
Area of Science:
- Condensed Matter Physics
- Materials Science
Background:
- Two-dimensional (2D) superconductors with nanoscale disorder display complex phenomena.
- Superconducting transitions in these systems are characterized by broad, percolative transitions with extended resistive tails.
- Fragile superconducting clusters form at low temperatures and can be enhanced by proximity effects.
Purpose of the Study:
- To investigate the role of dissipation effects in the formation and strengthening of superconducting clusters in disordered 2D systems.
- To model the influence of finite-frequency dissipation, such as from thermally excited vortices, on superconducting properties.
- To analyze these effects using a random impedance model and effective medium theory.
Main Methods:
- Development of a random impedance model to capture dissipation effects.
- Application of effective medium theory to analyze the model.
- Investigation of superconducting cluster formation and strengthening mechanisms.
Main Results:
- Dissipation effects significantly contribute to the physics of disordered 2D superconductors.
- The random impedance model provides insights into cluster strengthening via proximity effects.
- Superfluid stiffness is enhanced below the percolative transition due to these mechanisms.
Conclusions:
- Dissipation, particularly from vortices, plays a crucial role in the behavior of disordered 2D superconductors.
- The proposed model offers a framework for understanding the interplay between disorder, dissipation, and superconductivity.
- Effective medium theory provides a viable approach to solve the model and predict system behavior.
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