Danielle Villa | Searcharxiv

arXiv2026

Superconducting dome due to the Fano-Feshbach shape resonance in artificial high-Tc superlattices

In this work we provide compelling experimental validation of the Bianconi Perali Valletta (BPV) theory predicting a superconducting dome based on a quantum material design of Artificial High TC Superlattices (AHTS) made with a selected nanoscale heterostructure geometry. These AHTS are SNSN superlattices of quantum wells of period d, composed of first units, superconducting doped Mott insulator layers with Rashba spin orbit coupling (S) of thickness L, intercalated by second units, normal metal spacers (N). In these superlattices, grown by molecular beam epitaxy (MBE), the experimental superconducting dome is obtained by material quantum design changing the chemical potential via the quantum geometrical factor L/d which tunes the Fano-Feshbach shape resonance in the pair transfer between superconducting gaps in the BCS regime and different gaps in the BEC-BCS crossover. Here we present a systematic magneto-transport study of AHTS artificial superlattices across the full doping range of the superconducting dome, from the deeply underdoped to the overdoped regime, using pulsed magnetic fields up to 72 T. By varying the L/d ratio, we tune the effective hole concentration delta=0.45(1-L/d) and map the evolution of the resistive transitions, the upper critical magnetic field and the Ginzburg-Landau coherence length

cond-mat.supr-con↗

arXiv2025

Cross-Examiner: Evaluating Consistency of Large Language Model-Generated Explanations

Large Language Models (LLMs) are often asked to explain their outputs to enhance accuracy and transparency. However, evidence suggests that these explanations can misrepresent the models' true reasoning processes. One effective way to identify inaccuracies or omissions in these explanations is through consistency checking, which typically involves asking follow-up questions. This paper introduces, cross-examiner, a new method for generating follow-up questions based on a model's explanation of an initial question. Our method combines symbolic information extraction with language model-driven question generation, resulting in better follow-up questions than those produced by LLMs alone. Additionally, this approach is more flexible than other methods and can generate a wider variety of follow-up questions.

cs.CL↗