Spin-Related Electron Transfer and Orbital Interactions in Oxygen Electrocatalysis

Oxygen evolution and reduction reactions play a critical role in determining the efficiency of the water cycling (H₂O ⇔ H₂ + $\frac{1}{2}$O₂), in which the hydrogen serves as the energy carrier. That calls for a comprehensive understanding of oxygen electrocatalysis for efficient catalyst design. Current opinions on oxygen electrocatalysis have been focused on the thermodynamics of the reactant/intermediate adsorption on the catalysts. Because the oxygen molecule is paramagnetic, its production from or its reduction to diamagnetic hydroxide/water involves spin-related electron transfer. Both electron transfer and orbital interactions between the catalyst and the reactant/intermediate show spin-dependent character, making the reaction kinetics and thermodynamics sensitive to the spin configurations. Herein, a brief introduction on the spintronic explanation of the catalytic phenomena on oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is given. The local spin configurations and orbital interactions in the benchmark transition-metal-based catalysts for OER and ORR are analyzed as examples. To further understand the spintronic oxygen electrocatalysis and to develop more efficient spintronic catalysts, the challenges are summarized and future opportunities proposed. Spin electrocatalysis may emerge as an important topic in the near future and help integrate a comprehensive understanding of oxygen electrocatalysis.     

Intramuscular EMG classifier for detecting myopathy and neuropathy

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ($1$-D) signals. In this approach, the $1$-D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated $10$-fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of $92.80$%, $94.25$%, and $93.71$% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.     

A High-Performance Li–Mn–O Li-rich Cathode Material with Rhombohedral Symmetry via Intralayer Li/Mn Disordering

The search for new high-performance and low-cost cathode materials for Li-ion batteries is a challenging issue in materials research. Commonly used cobalt- or nickel-based cathodes suffer from limited resources and safety problems that greatly restrict their large-scale application, especially for electric vehicles and large-scale energy storage. Here, a novel Li–Mn–O Li-rich cathode material with $R\overline{3}m$ symmetry is developed via intralayer Li/Mn disordering in the Mn-layer. Due to the special atomic arrangement and higher $R\overline{3}m$ symmetry with respect to the C2/m symmetry, the oxygen redox activity is modulated and the Li in the Li-layer is preferentially thermodynamically extracted from the crystal structure instead of Li in the Mn-layer. The as-obtained material delivers a reversible capacity of over 300 mAh g⁻¹ at 25 mA g⁻¹ and rate capability of up to 260 mAh g⁻¹ at 250 mA g⁻¹ within 2.0–4.8 V. The excellent performance is attributed to its highly structural reversibility, mitigation of Jahn–Teller distortion, lower bandgap, and faster Li-ion 2D channels during the lithium-ion de/intercalation process. This material is not only a promising cathode material candidate but also raises new possibilities for the design of low-cost and high-performance cathode materials.     

Exploiting a Single-Crystal Environment to Minimize the Charge Noise on Qubits in Silicon

Electron spins in silicon offer a competitive, scalable quantum-computing platform with excellent single-qubit properties. However, the two-qubit gate fidelities achieved so far have fallen short of the 99% threshold required for large-scale error-corrected quantum computing architectures. In the past few years, there has been a growing realization that the critical obstacle in meeting this threshold in semiconductor qubits is charge noise arising from the qubit environment. In this work, a notably low level of charge noise of S₀ = 0.0088 ± 0.0004 μeV² Hz⁻¹ is demonstrated using atom qubits in crystalline silicon, achieved by separating the qubits from surfaces and interface states. The charge noise is measured using both a single electron transistor and an exchange-coupled qubit pair that collectively provide a consistent charge noise spectrum over four frequency decades, with the noise level S₀ being an order of magnitude lower than previously reported. Low-frequency detuning noise, set by the total measurement time, is shown to be the dominant dephasing source of two-qubit exchange oscillations. With recent advances in fast (≈μs) single-shot readout, it is shown that by reducing the total measurement time to ≈1 s, 99.99% two-qubit $\text{◂√▸}\sqrt{SWAP}$ gate fidelities can be achieved in single-crystal atom qubits in silicon.