McMillan Model Explains Gaps in Thin NbSe₂, TaS₂ Samples

Tunneling measurements into thin layers of niobium diselenide and tantalum disulfide cannot be fully explained by current understandings of superconductivity, researchers from the Racah Institute of Physics at the Hebrew University of Jerusalem report. While these materials appear to exhibit only a single superconducting gap, the team found that detailed analysis reveals a more complex mechanism. Excellent agreement with the McMillan two-band model was achieved when considering large scattering parameters relative to the superconducting gaps, even with the apparent presence of just one gap in tunneling data. Lead author Hadar Steinberg said that they propose these highly cross-scattering gaps reside on the Γ and K Fermi surfaces derived from the transition metal atoms, suggesting a specific structural origin for these superconducting properties; the research also indicates that bulk 2H-NbSe₂ is likely a three-band superconductor.

Quantitative analysis of magnetic field-induced pair breaking also aligned with the two-band McMillan theory, bolstering the model’s validity. The researchers suggest that bulk 2H-NbSe₂ may be even more complex, likely a three-band superconductor, indicating a hierarchical structure to its superconducting behavior. This finding opens new avenues for investigating multi-band superconductivity in transition metal dichalcogenides and refining theoretical models to accurately capture their behavior.

This alignment is particularly notable given that the scattering parameters are large with respect to the superconducting gaps, challenging conventional understanding of how material imperfections influence superconductivity. These findings underscore the need for more sophisticated models to fully capture the behavior of these materials and potentially unlock new superconducting technologies.

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Ivy Delaney

We've seen the rise of AI over the last few short years with the rise of the LLM and companies such as Open AI with its ChatGPT service. Ivy has been working with Neural Networks, Machine Learning and AI since the mid nineties and talk about the latest exciting developments in the field.

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