A5087479520- Physics of Superconductivity and Magnetism
- Iron-based superconductors research
- Particle accelerators and beam dynamics
- Electronic and Structural Properties of Oxides
- Topological Materials and Phenomena
- Fluid Dynamics and Mixing
- Advanced Condensed Matter Physics
- Magnetic confinement fusion research
- Advanced Chemical Physics Studies
- Superconducting Materials and Applications
- Magnetic and transport properties of perovskites and related materials
- Graphene research and applications
- Surface and Thin Film Phenomena
- Chemical and Physical Properties of Materials
- Superconductivity in MgB2 and Alloys
- Corporate Taxation and Avoidance
- Heat Transfer and Boiling Studies
- Advancements in Battery Materials
- Lattice Boltzmann Simulation Studies
Temple University
2020-2024
West Virginia University
2023
Tribhuvan University
2013
We use scanning tunneling microscopy (STM) and spectroscopy (STS), x-ray photoelectron (XPS) to investigate the effect of nitrogen doping on surface electronic chemical structures cutouts from superconducting $\mathrm{Nb}$ radio-frequency cavities. The goal this work is get insights into fundamental physics materials mechanisms behind striking decrease resistance with magnetic field, which has been observed $N$-doped Our XPS measurements reveal significantly more oxidized $3d$ states a...
Kagome lattice hosts a plethora of quantum states arising from the interplay topology, spin-orbit coupling, and electron correlations. Here, we report symmetry-breaking electronic orders tunable by an applied magnetic field in model magnet FeSn consisting alternating stacks two-dimensional Fe3Sn Sn2 honeycomb layers. On layer terminated thin films epitaxially grown on SrTiO3(111) substrates, observe trimerization using scanning tunneling microscopy/spectroscopy, breaking its six-fold...
Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, electronic excitations. However, plain visual analysis of STM images can be challenging such determination in multicomponent alloys, particularly beyond the diluted limit due to disorder inhomogeneity. One viable solution use machine learning analyze data identify hidden patterns correlations. Here, we apply this approach...
We report radio-frequency measurements of quality factors and temperature mapping a nitrogen doped Nb superconducting RF cavity. Cavity cutouts hot cold spots were studied with low scanning tunneling microscopy spectroscopy, X-ray photoelectron spectroscopy secondary electron microscopy. Temperature revealed substantial reduction the residual resistance upon cooling cavity greater gradient hysteretic losses at quench location, pointing to trapped vortices as dominant source surface...
Chemical pressure from the isovalent substitution of Se by a larger Te atom in epitaxial film iron chalcogenide FeSe can effectively tune its superconducting, topological, and magnetic properties. However, such during growth inherently leads to defects structural inhomogeneity, making determination alloy composition atomic sites for substitutional atoms challenging. Here, we utilize machine learning distinguish between scanning tunneling microscopy images single-layer FeSe1−xTex on...
By substituting S into single-layer FeSe/SrTiO3, chemical pressure is applied to tune its paramagnetic state that modeled as an incoherent superposition of spin-spiral states. The resulting electronic bands resemble ordered checkerboard antiferromagnetic structure, consistent with angle-resolved photoemission spectroscopy measurements. Scanning tunneling reveals a gap evolving from U-shaped for FeSe V-shaped FeS decreasing size, attributed d-wave superconducting which nodes emerge once the...
Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, electronic excitations. However, traditional methods of visual inspection can be challenging such determination in multi-component alloys, particularly beyond the dilute limit due to disorder inhomogeneity. One viable solution use machine learning analyze STM data identify patterns correlations that may not immediately apparent...