Search arXivSearch

arXiv · 2206.09850

Metallization of hydrogen by intercalating ammonium ions in metal fcc lattices at low pressure

Abstract

Metallic hydrogen is capable of showing room temperature superconductivity, but its experimental syntheses are extremely hard. Therefore, it is desirable to reduce the synthesis pressure of metallic hydrogen by adding other chemical elements. However, for most hydrides, the metallization of hydrogen by "chemical precompression" to achieve high-temperature superconductivity still requires relatively high pressure, making experimental synthesis difficult. How to achieve high-temperature superconductivity in the low-pressure range (0-50 GPa) is a key issue for realizing practical applications of superconducting materials. Toward this end, this work proposes a strategy for inserting ammonium ions in the fcc crystal of metals. High-throughput calculations of the periodic table reveal 12 elements which can form kinetically stable and superconducting hydrides at low pressures, where the highest superconducting transition temperatures of AlN2H8, MgN2H8 and GaN2H8 can reach up to 118.40, 105.09 and 104.39 K. Pressure-induced bond length changes and charge transfer reveal the physical mechanism of high-temperature superconductivity, where the H atom continuously gains electrons leading to the transition of H+ ions to atomic H, facilitating metallization of hydrogen under mild pressure. Our results also reveal two strong linear scalar relationships, one is the H-atom charge versus superconducting transition temperature and the other is the first ionization energy versus the highest superconducting transition temperature. Besides, ZnN2H8, CdN2H8, and HgN2H8 were found to be superconductors at ambient pressure, and the presence of interstitial electrons suggests that they are also electrides, whose relatively low work functions (3.03, 2.78, and 3.05 eV) imply that they can be used as catalysts for nitrogen reduction reactions as well.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhongyu Wan, Ruiqin Zhang. 2022-07-05. Metallization of hydrogen by intercalating ammonium ions in metal fcc lattices at low pressure. https://doi.org/10.1063/5.0127365

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Collective excitation-mediated transport in nanoscale Josephson junctions that exhibit quantum confinement

Quantum confinement can strongly modify transport through Josephson junctions. Here, we study local tunneling transport through nanoscale Josephson junction stacks in the Coulomb blockade regime, where the metallic layers exhibit strong vertical quantum confinement. We find that quasiparticle transport is strongly enhanced by a collective excitation mode intrinsic to the junction and localized in the isolated metallic overlayer. We quantify both the collective-mode energy and the Coulomb gap and show that both exhibit strong layer-dependent modulation, consistent with the modulation of the underlying quantum well states. We further investigate how the collective excitation responds to various perturbations, including mechanical motion and an applied magnetic field. Our results suggest that this collective mode is sensitive to quasiparticles near the Fermi level and may therefore provide an indirect probe of the superconducting state.

cond-mat.supr-con

Universal Dzyaloshinski-Moriya interaction dictates pairing in unconventional superconductor families

The collinear-antiferromagnetic spin-fluctuation paradigm has long guided unconventional superconductivity research, yet fails to reconcile the noncollinear spin phenomena observed across cuprates, iron-based superconductors, and nickelates. Using extensive first-principles calculations and unbiased large-scale DMRG simulations, we show that Dzyaloshinski-Moriya interaction (DMI)-arising from local inversion-symmetry breaking-is a common ingredient across these families. This DMI unifies hallmark observations in parent compounds-incommensurate orders, spin-wave gaps, and noncollinear textures. Under hole doping, strong DMI drives spin vortices to merge with pi-shifted hole stripes, forming hybrid vortex-hole stripe phases. These phases stabilize charge order while supporting, not suppressing, superconductivity. By contrast, under electron doping, these vortices pin holes and suppress long-range superconductivity. Our results establish DMI as a unifying link between noncollinear magnetism and superconductivity, identifying hole-strip-vortex coupling as a microscopic pairing engine. Given that DMI is common across major superconductor families, these findings challenge the prevailing pairing mechanism and offer an experimentally testable roadmap for materials optimization.

cond-mat.supr-con

Digital-analog concept for superconducting perceptron-like neural networks

A promising route to superconducting artificial neural networks is a hybrid digital-Analog architecture that combines digital single-flux-quantum (SFQ) communication with compact Analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-Analog-to-digital (DAD) converter, in which the role of the Analog cell was performed by a $Σ$-neuron with a nonlinear transfer function -- the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an Analog-to-digital converter (ADC), and re-encodes the Analog $Σ$-neuron waveforms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $Σ$-neuron, and mapped back to pulse-based outputs. As a key experimental step, we fabricated and characterised a redesigned $Σ$-neuron and measured a sigmoid-like transfer characteristic suitable for activation-function implementation. The extracted response was incorporated into system-level simulations to assess the influence of realistic device parameters on the conversion process. We delineate the operating-range matching requirements for the DAC, neuron, and ADC blocks, supporting the feasibility of the proposed interface as a building block for perceptron-like superconducting neural networks with digital inputs and outputs. Finally, we developed two perceptron networks, one using a mathematical sigmoid activation and the other the measured $Σ$-neuron transfer characteristic, which reached classification accuracies of $97.0\%$ and $91.9\%$, respectively, on the MNIST handwritten digit dataset.

cond-mat.supr-con