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Results for “Meissner screening”

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24 records · Page 2

Oscillating paramagnetic Meissner effect and Berezinskii-Kosterlitz-Thouless transition in underdoped Bi2Sr2CaCu2O8+δ

ABSTRACT Superconducting phase transitions in two dimensions lie beyond the description of the Ginzburg-Landau symmetry-breaking paradigm for three-dimensional superconductors. They are Berezinskii-Kosterlitz-Thouless (BKT) transitions of paired-electron condensate driven by the unbinding of topological excitations, i.e. vortices. The recently discovered monolayers of layered high-transition-temperature (${{{T}}}_{{\rm C}}$) cuprate superconductor Bi2Sr2CaCu2O8+δ (Bi2212) meant that this 2D superconductor promised to be ideal for the study of unconventional superconductivity. But inhomogeneity posed challenges for distinguishing BKT physics from charge correlations in this material. Here, we utilize the phase sensitivity of scanning superconducting quantum interference device microscopy susceptometry to image the local magnetic response of underdoped Bi2212 from the monolayer to the bulk throughout its phase transition. The monolayer segregates into domains with independent phases at elevated temperatures below ${{{T}}}_{{\rm C}}$. Within a single domain, we find that the susceptibility oscillates with flux between diamagnetism and paramagnetism in a Fraunhofer-like pattern up to ${{{T}}}_{{\rm C}}$. The finite modulation period, as well as the broadening of the peaks when approaching ${{{T}}}_{{\rm C}}$ from below, suggests well-defined vortices that are increasingly screened by the dissociation of vortex-antivortex plasma through a BKT transition. In the multilayers, the susceptibility oscillation differs in a small temperature regime below ${{{T}}}_{{\rm C}}$, consistent with a dimensional crossover led by interlayer coupling. Serving as strong evidence for BKT transition in the bulk, we observe a sharp jump in phase stiffness and paramagnetism at small fields just below ${{{T}}}_{{\rm C}}$. These results unify the superconducting phase transitions from the monolayer to the bulk underdoped Bi2212, and can be collectively referred to as the BKT transition with interlayer coupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Interplay between magnetism and superconductivity in UTe 2

Time-reversal symmetry breaking (TRSB) in UTe 2 was inferred from observations of a spontaneous Kerr response in the superconducting state after cooling in zero magnetic field, while a finite c-axis magnetic field training was further used to determine the nature of the nonunitary composite order parameter of this material. Here, we present an extensive study of the magnetic-field-trained Kerr effect, which unveils a unique critical state of pinned “ferromagnetic vortices.” We show that a remanent Kerr signal appears following the removal of a training magnetic field, which reflects the response to the TRSB order parameter and the external magnetic field through the paramagnetic susceptibility. Furthermore, this unambiguously demonstrates the importance of the magnetic fluctuations and their intimate relation to the composite order parameter. Focusing the beam on the center of the sample, we are able to accurately determine the maximum field that is screened by the critical state and the respective critical current. Measurements in the presence of magnetic field show the tendency of the superconductor to produce shielding currents that oppose the increase in vortex-induced magnetization due to the diverging paramagnetic susceptibility.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Scanning SQUID study of ferromagnetism and superconductivity in infinite-layer nickelates

Infinite-layer nickelates R 1-x Sr x NiO 2 (R = La, Pr, Nd) are a class of superconductors with structural similarities to cuprates. Although long-range antiferromagnetic order has not been observed for these materials, magnetic effects such as antiferromagnetic spin fluctuations and spin-glass behavior have been reported. Different experiments have drawn different conclusions about whether the pairing symmetry is s or d wave. In this paper, we applied a scanning superconducting quantum interference device (SQUID) to probe the magnetic behavior of film samples of three infinite-layer nickelates (La 0.85 Sr 0.15 NiO 2 , Pr 0.8 Sr 0.2 NiO 2 , and Nd 0.775 Sr 0.225 NiO 2 ) grown on SrTiO 3 (STO), each with a nominal thickness of 20 unit cells. In all three films, we observed a ferromagnetic background. We also measured the magnetic susceptibility above the superconducting critical temperature in Pr 0.8 Sr 0.2 NiO 2 and La 0.85 Sr 0.15 NiO 2 and identified a non-Curie-Weiss dynamic susceptibility. Both magnetic features are likely due to NiO x nanoparticles. We investigated superconductivity in Pr 0.8 Sr 0.2 NiO 2 and Nd 0.775 Sr 0.225 NiO 2 , which exhibited inhomogeneous diamagnetic screening. The superfluid density inferred from the diamagnetic susceptibility in relatively homogeneous regions shows T-linear behavior in both samples. Finally, we observed superconducting vortices in Nd 0.775 Sr 0.225 NiO 2 . We determined a Pearl length of 330 µm for Nd 0.775 Sr 0.225 NiO 2 at 300 mK, both from the strength of the diamagnetism and from the size and shape of the vortices. These results highlight the importance of considering NiO x particles when interpreting experimental results for these films.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Universal temperature dependence of the London penetration depth in κ - ( ET ) 2 X superconductors

High-precision radio-frequency magnetic susceptibility measurements were performed on single crystals of fully deuterated $κ-(ET)_2Cu[N(CN)_2]\,Br$, hereafter designated as $κ-(ET)_2Cu[N(CN)_2]\,Br$. This material phase separates into superconducting and antiferromagnetic regions, the degree of which depends strongly upon the cooling rate. Here, we show that the screening fraction $η_{sc}$ varies logarithmically with the cooling rate over nearly five decades. The average size of superconducting regions is estimated to vary from 5 to $40μm$, depending upon cooling rate, consistent with previous infrared microscopy measurements. In the region $T≲T_c/3$, the effective magnetic penetration depth exhibits power-law behavior $λ(T)-λ(0)~T^n$ with $n=1.6$, independent of the cooling rate. Changes in cooling rate and the consequent phase separation evidently do not introduce the kind of disorder that would alter the exponent $n$ in a $d$-wave superconductor. The exponent remains close to $n=$ 1.5, reported in single crystals of $κ-(ET)_2Cu[N(CN)_2]\,Br$ and $κ-(ET)_2Cu(NCS)_2$ [A. Carrington et al., Phys. Rev. Lett 83, 4172 (1999)]. The transition temperature fell linearly with $1-η_{sc}$. Measurements were also made on $κ-(ET)_2Cu[N(CN)_2]\,Cl$ of normal isotopic abundance in which a very small amount of superconducting phase $η_{sc}≈10^{-4}$ developed, presumably through the strain-induced sample mounting. This material showed a power-law exponent of $n=1.64$, independent of the cooling rate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗