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At least 127 records · Page 7

Shapeshifting Nanocatalyst for CO2 Conversion

The conversion of CO2 into high-value chemicals through a photoreduction reaction in water is a promising route to reduce the dependence on fossil fuels. Enhancing selectivity toward hydrocarbons or alcohols can be achieved by Ag-Cu alloys. However, the stabilized surface state created by Ag-Cu interactions is still poorly understood. In this work, multi-modal in situ X-ray experiments reveals underlying mechanisms and the evolution of Ag-Cu nanoparticles under CO2 reduction reaction (CO2RR) conditions. Both morphological and chemical changes of Ag and Cu species induced by diffusion mechanics are tracked during nanocatalyst operation. The initial spheroid Ag-Cu nanoparticles are composed of a Cu-rich shell and Ag-rich core. The reduction treatment promotes Ag migration toward the surface. During photocatalytic CO2 reduction reaction, Cu atoms migrate back to the surface, forming Ag-Cu-O species. The study observes the surface oxidation of Cu(0) to Cu+ and the presence of Ag at the sub-surface region. Furthermore, nanoparticles change their shape, decreasing their specific surface area, driven by Cu diffusion during the CO2 photoreduction reaction. The results provide invaluable insights into the dynamic restructuring of the catalyst under reaction conditions and into the active species responsible for CO2 conversion.

CO2 reduction reaction↗

Side-Chain and Ring-Size Effects on Permeability in Artificial Water Channels

Artificial water channels (AWCs) have emerged as a promising framework for stable water permeation, with water transport rates comparable to aquaporins (3.4–40.3 × 10 8 H 2 O/channel/s). In this study, we probe the influence of ring-size and side-chain length on the water permeability observed within a class of AWCs termed ligand-appended pillar[n]arenes (LAPs) that have an adjustable ring-size (m) and side-chain length (n). Through all-atom molecular dynamics simulations, we calculate the permeability of these channels using the collective diffusion model and find their permeabilities. We characterize the mechanistic influence of pillar[n]arene ring-size and side-chain length on the channel water permeability by analyzing the characteristics of the internal permeating water-wire and the surrounding channel structure. We observe that water permeability decreases as a function of increasing ring-size due to increases in hydrophilic contacts between the permeating water-wire and the oxygen groups on the channel wall. Further, we observe an increase in water permeability as a function of side-chain length due to increased partitioning of the channel terminal groups into the hydrophilic blocks of the surrounding bilayer. For the LAP6 channel, with increase in side-chain length, the distance between terminal groups increases and leads to an increase in pore size, thereby enhancing water permeability. In the case of LAP5, as side-chain length increases, the channel displays a compensatory effect between tilt and bend angle due to the flexible side-chains. Such flexibility leads to higher terminal group partitioning in the hydrophilic blocks of the bilayer and extends the permeating water-wire. Furthermore, this increase in water-wire length and hydrophilic block access overcomes the nonmonotonic pore size trend in pillar[5]arene channels.

36 MATERIALS SCIENCE↗

Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained. Generative machine learning models may act as surrogates to replace physics-based full simulation of particle detectors, and diffusion models have recently emerged as the state of the art for other generative tasks. Here, we introduce CaloDiffusion, a denoising diffusion model trained on the public CaloChallenge datasets to generate calorimeter showers. Our algorithm employs 3D cylindrical convolutions, which take advantage of symmetries of the underlying data representation. To handle irregular detector geometries, we augment the diffusion model with a new geometry latent mapping (GLaM) layer to learn forward and reverse transformations to a regular geometry that is suitable for cylindrical convolutions. The showers generated by our approach are nearly indistinguishable from the full simulation, as measured by several different metrics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Neuromorphic scaling advantages for energy-efficient random walk computations

Neuromorphic computing, which aims to replicate the computational structure and architecture of the brain in synthetic hardware, has typically focused on artificial intelligence applications. What is less explored is whether such brain-inspired hardware can provide value beyond cognitive tasks. Here we show that the high degree of parallelism and configurability of spiking neuromorphic architectures makes them well suited to implement random walks via discrete-time Markov chains. Overall, these random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Using IBM’s TrueNorth and Intel’s Loihi neuromorphic computing platforms, we show that our neuromorphic computing algorithm for generating random walk approximations of diffusion offers advantages in energy-efficient computation compared with conventional approaches. We also show that our neuromorphic computing algorithm can be extended to more sophisticated jump-diffusion processes that are useful in a range of applications, including financial economics, particle physics and machine learning.

97 MATHEMATICS AND COMPUTING↗

Super-Resolved Single-Molecule Tracking Studies of Rhodamine B Accumulation on Fresh and Aged Polyethylene Terephthalate

It is well-known that toxic organic micropollutants (OMs) accumulate on the surfaces of microplastics. However, much remains to be learned about the exact molecular level mechanisms of OM accumulation and how these evolve as the plastics age. In this work, super-resolved single-molecule tracking (SMT) is used for the first time to investigate the accumulation of Rhodamine B (RhB) dye on fresh and artificially aged polyethylene terephthalate (PET) surfaces. PET thin films serve as models for microplastics, while RhB serves as a proxy for the OMs they accumulate. Artificial aging of the films is accomplished by exposing them in a UV-ozone chamber. Water contact angle, spectroscopic ellipsometry, and carbonyl index measurements reveal a gradual decrease in film hydrophobicity, thickness, and carbonyl content with age. Atomic force microscopy (AFM) data reveal an increase in surface roughness and confirm that the films remain largely intact and continuous across the aging times explored. In SMT experiments, wide-field fluorescence videos acquired from the water/PET interface under 7.5 pM RhB reveal both mobile and immobile dye molecules. Measurements of the frame-to-frame displacements of the dye show that diffusion occurs by a desorption-mediated mechanism and that the diffusion rate varies with PET film age. The surface density of mobile dye molecules decreases with increasing PET age, while the population of immobile molecules becomes relatively larger, suggesting an age-dependent transformation of the mechanism(s) by which the dye is accumulated. SMT data reveal that both mobile and immobile molecules repeatedly adsorb over the same surface sites, consistent with the emergence of nanoscale PET surface heterogeneity also revealed by AFM. Estimates of the adsorption coefficients are obtained using a nearest-neighbor analysis, giving values from 9.9 × 10 5 to 2.1 × 10 6 M –1 for immobile molecules and from 1.8 × 10 5 to 2.5 × 10 5 M –1 for mobile molecules on fresh and 5 min aged PET, respectively. Here, anomalous age-dependent variations in the velocity of molecular motion on the PET surface and in the population of immobile molecules are shown to correlate with changes in the strength of RhB adsorption.

Adsorption↗

Toward a microscopic picture of hadronization and multi-parton processes

This project advanced the understanding of how quarks and gluons produced in high-energy collisions transform into the hadrons observed in particle detectors, a fundamental process known as quantum chromodynamics (QCD) hadronization. By combining theoretical calculations, quantum simulation methods, and modern AI techniques, the research developed new tools to study multi-parton dynamics and nonperturbative effects that are essential for interpreting data from current and future nuclear physics experiments. Key outcomes include new theoretical frameworks for jet and hadron measurements, pioneering quantum simulation algorithms for real-time dynamics in field theories, and the development of advanced machine-learning models, such as diffusion models and explainable classifiers, to simulate and analyze collider events. These results are directly relevant to experiments at Jefferson Lab, Brookhaven National Laboratory, and the future Electron-Ion Collider, and they also have a broader impact in areas such as quantum information science and data-driven modeling of complex systems. The project supported the training of graduate students and postdoctoral fellows and contributed to the broader scientific community through publications, workshops, and collaborative activities. Overall, this work provides new insights into the microscopic mechanisms of hadron formation and establishes a foundation for future studies at the intersection of nuclear physics, artificial intelligence, and quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Application of Dislocation Theory to Minimize Defects in Artificial Solids Built with Nanocrystal Building Blocks

Conspectus. Oriented atomic attachment of colloidal inorganic nanocrystals represents a powerful synthetic method for preparing complex inorganic superstructures. Examples include fusion of nanocrystals into dimer and superlattice structures. If the attachment were perfect throughout, then the resulting materials would have single crystal-like alignment of the individual nanocrystals' atomic lattices. While individual colloidal nanocrystals typically are free of many defects, there are a multitude of pathways that can generate defects upon nanocrystal attachment. These attachment generated defects are typically undesirable, and thus developing strategies to favor defect-free attachment or heal defective interfaces are essential. There may also be some cases where attachment-derived defects are desirable. In this Account, we summarize our current understanding of how these defects arise, in order to offer guidance to those who are designing nanocrystal derived solids. The small size of inorganic nanocrystals means short diffusion lengths to the surface, which favor the formation of nanocrystal building blocks with pristine atomic structures. Upon attachment, however, there are numerous pathways that can lead to atomic scale defects, and bulk crystal dislocation theory provides an invaluable guide to understanding these phenomena. As an example, an atomic step edge can be incorporated into the interface leading to an extra half-plane of atoms, known as an edge dislocation. These dislocations can be well described by the Burgers vector description of dislocations, which geometrically identifies planes in which a dislocation can move. Our in situ measurements have verified that bulk dislocation theory predictions for 1D defects hold true at few-nanometer length scales in PbTe and CdSe nanocrystal interfaces. Ultimately, the applicability of dislocation theory to nanocrystal attachment enables the predictive design of attachment to prevent or facilitate healing of defects upon nanocrystal attachment. We applied similar logic to understand formation of planar (2D) defects such as stacking faults upon nanocrystal attachment. Again concepts from bulk crystal defect crystallography can identify attachment pathways that can prevent or deterministically form planar defects upon nanocrystal attachment. The concepts we discuss work well for identifying favorable attachment geometries for nanocrystal pairs ; however it is currently unclear how to translate these ideas to near-simultaneous multiparticle attachment. Geometric frustration, which prevents nanocrystal rotation, and yet-to-be considered defect generation pathways unique to multiparticle attachment complicate defect-free superlattice attachment. New imaging methods now allow for the direct observation of local attachment trajectories and may enable improved understanding of such multiparticle phenomena. With further refinement, a unified framework for understanding and ultimately eliminating structural defects in fused nanocrystal superstructures may well be achievable in coming years.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Nonintrusive manufactured solutions for ablation

The study of heat transfer and ablation plays an important role in many problems of scientific and engineering interest. As with the computational simulation of any physical phenomenon, the first step toward establishing credibility in ablation simulations involves code verification. Code verification is typically performed using exact and manufactured solutions. However, manufactured solutions generally require the invasive introduction of an artificial forcing term within the source code such that the code solves a modified problem for which the solution is known. In this paper, we present a nonintrusive method for manufacturing solutions for a non-decomposing ablation code, which does not require the addition of a source term.

42 ENGINEERING↗

Parasitic Effects in Impedance Spectrum of PEM Water Electrolysis Cells: Case Study of High-Frequency Inductive Effects

Electrochemical impedance spectroscopy (EIS) is a powerful tool to characterize and distinguish electrochemical, electrical, and diffusive processes in an electrolysis cell. The EIS response with small impedance (mΩ) depends on components and materials of the cell, but often also on the electrical setup, cables, and connectors, leading to incorrect conclusions about the performance of the electrolysis cells. These parasitic effects are assessed via a short-circuit measurement and confirmed to be external from the membrane electrode assembly (MEA). In the setup, the inductive characteristic is described by a modified inductive reactance and must be included in the equivalent circuit model (ECM) which is fitting the cell EIS spectra in operation (in situ). If points of the spectra showing inductive characteristic (below x-axis in Nyquist plot) are excluded from the ECM evaluation, an artificially increased ohmic resistance at high frequencies is obtained. In addition, considerations on other components of the ECM can be misleading and significantly incorrect. The only way to avoid that inductive behavior caused by the external setup is incorrectly assigned to the ECM describing properties of the MEA is to include the inductive effects in the ECM analysis of in situ EIS measurements.

30 DIRECT ENERGY CONVERSION↗

The design of a photoionization front experiment using the Z-Machine as a driving source and estimated measurements

Radiation-driven heat fronts are present in the early universe during reionization, the circumstellar medium of supernovae, and in high-energy-density physics experiments. Dedicated experiments to observe and diagnose the behavior of these types of heat fronts can improve our understanding of these phenomena. A simulation study of photoionization fronts using the HELIOS-CR radiation hydrodynamics code provides an experimental design for the Z-Machine at Sandia National Laboratory using a measurement-calibrated input radiation flux to drive the photoionization front. The simulations use detailed atomic physics and non-diffusive radiation transport in 1D to determine an optimal gas pressure of 0.75 atm for an experiment in N gas as well as the effects of increasing the thickness of the window that seals the gas cell. Post-processing of these simulations demonstrates that ratios of atomic rate coefficients place the heat front in a physics regime where photoionization dominates the energy deposition. Here, to see the sensitivity of the simulations to changes in the model and spatial grid, this analysis performed resolution, atomic model detail, and radiation transport angular grid studies showing less than 10% deviation from the nominal model for increased complexity, when possible. An effort to emulate 3D geometric effects on the radiation flux using an artificial attenuation scheme has shown that, even for conservative estimates of the flux, simulations still produce a photoionization front. Estimations of a streaked, visible spectroscopy measurement using SPECT3D showed that line emission measurements are present early in time and that later in time thermal emission should become dominant.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Structural differences between human and mouse neurons and their implementation in generative AIs

Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

generative AI↗

Re-entrance to a ferromagnetic insulator with oxygen-vacancy ordering in the La 0.7 Sr 0.3 MnO 3 /SrTiO 3 superlattice

The charge-density wave is correlated with the properties in transition metal oxides, and regulating this state is important to explore the potential of oxides. Defects could modulate the local environment, in which the effect is subject to the concentration and arrangement, and an artificial arrangement of defect-ordering is highly expected to design the material properties. In this work, the ordering of oxygen vacancies with a preferred location on the (001) La/SrO plane by self-structural optimization was induced in La 0.7 Sr 0.3 MnO 3 /SrTiO 3 superlattices with medium oxygen pressure during SrTiO 3 deposition, and then the ferromagnetic charge-ordering (CO)/charge-density-wave (CDW) state was formed in La 0.7 Sr 0.3 MnO 3 with susceptibility to magnetic field, temperature, and superlattice configuration. In addition, re-entrance to the ferromagnetic insulator CO/CDW could be induced by a high magnetic field. The selective occupancy of oxygen vacancies on the (001) La/SrO plane and the phase fluctuation around the CO/CDW-to-ferromagnetic metal phase transition were critical to the above properties. The interfacial diffusion with atomic-level resolution to control oxygen vacancies could be used in other magnetoelectric systems and corresponding device applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Towards C P -violation studies on superheavy molecules: Theoretical and experimental perspectives

Molecules containing superheavy atoms can be artificially created to serve as sensitive probes to study symmetry-violating phenomena. Here, we provide detailed theoretical studies of quantities relevant to the electron electric dipole moment (eEDM) and nucleus-electron scalar-pseudoscalar interactions in diatomic molecules containing superheavy lawrencium nuclei. The sensitivity to parity and time (or, equivalently, CP) reversal violating properties is studied for different neutral and ionic molecules. The effective electric fields in these systems are found to be about 3–4 times larger than other known molecules on which eEDM experiments are being performed. Similarly, these superheavy molecules exhibit an enhancement of more than 3 times for CP-violating scalar-pseudoscalar nucleus-electron interactions. Our preliminary analysis using the Woods-Saxon nuclear model also demonstrates that these results are sensitive to the diffuse surface interactions inside the Lr nucleus. We also briefly comment on some experimental aspects by discussing the production of these systems.

74 ATOMIC AND MOLECULAR PHYSICS↗

Glancing Angle Deposition in Gas Sensing: Bridging Morphological Innovations and Sensor Performances

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.

Chemistry↗

Modeling the aqueous interface of amorphous TiO 2 using deep potential molecular dynamics

Amorphous titanium dioxide (a-TiO 2 ) is widely used as a coating material in applications such as electrochemistry and self-cleaning surfaces where its interface with water has a central role. However, little is known about the structures of the a-TiO 2 surface and aqueous interface, particularly at the microscopic level. In this work, we construct a model of the a-TiO 2 surface via a cut-melt-and-quench procedure based on molecular dynamics simulations with deep neural network potentials (DPs) trained on density functional theory data. After interfacing the a-TiO 2 surface with water, we investigate the structure and dynamics of the resulting system using a combination of DP-based molecular dynamics (DPMD) and ab initio molecular dynamics (AIMD) simulations. Both AIMD and DPMD simulations reveal that the distribution of water on the a-TiO 2 surface lacks distinct layers normally found at the aqueous interface of crystalline TiO 2 , leading to an ~10 times faster diffusion of water at the interface. Bridging hydroxyls (Ti 2 –O b H) resulting from water dissociation decay several times more slowly than terminal hydroxyls (Ti–O w H) due to fast Ti–O w H 2 → Ti–O w H proton exchange events. These results provide a basis for a detailed understanding of the properties of a-TiO 2 in electrochemical environments. Moreover, the procedure of generating the a-TiO 2 -interface employed here is generally applicable to studying the aqueous interfaces of amorphous metal oxides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use data sets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (a) a form of Nonlinear Vector Autoregression (NVAR), and (b) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures.

58 GEOSCIENCES↗

Boosting the sodium storage behaviors of carbon materials in ether-based electrolyte through the artificial manipulation of microstructure

The porous carbon blacks rationally designed by a facile yet efficient NH 3 thermal etching route have been investigated as anode materials in an ether-based electrolyte for sodium-ion batteries. The as-synthesized CBN35 carbon black with a 35% weight loss after NH3 thermal etching exhibited a large specific charge capacity of 352 mAh g -1 at 50 mA g -1 and a superior rate capability of 101 mAh g -1 at 16000 mA g -1 , due to its highest microporosity, an appropriate surface area, a desirable microstructure, and a promising hybrid intercalation mechanism. Impressively, even cycled at 1600 mA g -1 over 3200 cycles, an outstanding reversible capacity of 103 mAh g -1 with a negligible 0.0162% capacity loss per cycle can still be achieved. Based on the multimodal characterizations including the structural probes of phase evolution for carbon materials, the electrochemical techniques, and the surface-sensitive XAS measurements, the exceptional electrochemical properties should stem from several merits of modified carbon black system. While the particular microporous structure provides relatively more accessible sodium storage sites, a novel hybrid intercalation mechanism in ether-based electrolyte would incorporate the sodium ion insertion into the disordered structure with the solvated sodium ion species co-intercalation into the graphitic phase. In addition to the diffusion-controlled redox reactions, the noticeable surface-induced pseudocapacitive reactions also significantly contribute to the charge storage upon sodiation and guarantee the rapid migrations of sodium ions/solvated compounds. In conclusion, this system further features a controlled emergence of a robust but thin solid electrolyte interphase layer, which could suppress the side reactions of active electrode with reactive electrolyte, maintain the fragile porous structure upon cycling, and facilitate the migrations of sodium ions and solvated sodium ion compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Structure and dynamics of $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquids close to Earth's liquid core conditions

Using an artificial neural-network machine learning interatomic potential, we have performed molecular dynamics simulations to study the structure and dynamics of $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquid close to the Earth's liquid core conditions. Here, the simulation results reveal that the short-range structural order (SRO) in the $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquid is very strong. About 80% of the atoms are arranged in crystallinelike SRO motifs. In particular, ~ $70$% of Fe-centered clusters can be classified as either hexagonal-close-packed-like or icosahedrallike SRO motifs. The SRO clusters centered on Fe, Si, or O atoms are strongly intermixed and homogenously distributed throughout the liquid. The atomic structure of the liquid and the fractions of dominant SRO clusters are not sensitive to pressure/temperature used in the simulations except that the SRO of the O-centered clusters is enhanced close to inner core pressures. The O-diffusion coefficient is about two to three times larger than the Fe and Si ions and increases more rapidly in the deeper core regions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗