Engineering Transition Metal Layers for Long Lasting Anionic Redox in Layered Sodium Manganese Oxide

Oxygen-redox-based-layered cathode materials are of great importance in realizing high-energy-density sodium-ion batteries (SIBs) that can satisfy the demands of next-generation energy storage technologies. However, Mn-based-layered materials (P2-type Na-poor Na_y[A_xMn_1−x]O₂, where A = alkali ions) still suffer from poor reversibility during oxygen-redox reactions and low conductivity. In this work, the dual Li and Co replacement is investigated in P2-type-layered Na_xMnO₂. Experimentally and theoretically, it is demonstrated that the efficacy of the dual Li and Co replacement in Na_0.6[Li_0.15Co_0.15Mn_0.7]O₂ is that it improves the structural and cycling stability despite the reversible Li migration from the transition metal layer during de-/sodiation. Operando X-ray diffraction and ex situ neutron diffraction analysis prove that the material maintains a P2-type structure during the entire range of Na⁺ extraction and insertion with a small volume change of ≈4.3%. In Na_0.6[Li_0.15Co_0.15Mn_0.7]O₂, the reversible electrochemical activity of Co³⁺/Co⁴⁺, Mn³⁺/Mn⁴⁺, and O^2-/(O₂)^n- redox is identified as a reliable mechanism for the remarkable stable electrochemical performance. From a broader perspective, this study highlights a possible design roadmap for developing cathode materials with optimized cationic and anionic activities and excellent structural stabilities for SIBs.     

Fast Li+ Transport via Silica Network-Driven Nanochannels in Ionomer-in-Framework for Lithium Metal Batteries

For the development of all-solid-state lithium metal batteries (LMBs), a high-porous silica aerogel (SA)-reinforced single-Li⁺ conducting nanocomposite polymer electrolyte (NPE) is prepared via two-step selective functionalization. The mesoporous SA is introduced as a mechanical framework for NPE as well as a channel for fast lithium cation migration. Two types of monomers containing weak-binding imide anions and Li⁺ cations are synthesized and used to prepare NPEs, where these monomers are grafted in SA to produce SA-based NPEs (SANPEs) as ionomer-in-framework. This hybrid SANPE exhibits high ionic conductivities (≈10⁻³ S cm⁻¹), high modulus (≈10⁵ Pa), high lithium transference number (0.84), and wide electrochemical window (>4.8 V). The resultant SANPE in the lithium symmetric cell possesses long-term cyclic stability without short-circuiting over 800 h under 0.2 mA cm⁻². Furthermore, the LiFePO₄|SANPE|Li solid-state batteries present a high discharge capacity of 167 mAh g⁻¹ at 0.1 C, good rate capability up to 1 C, wide operating temperatures (from −10 to 40 °C), and a stable cycling performance with 97% capacity retention and 100% coulombic efficiency after 75 cycles at 1 C and 25 °C. The SANPE demonstrates a new design principle for solid-state electrolytes, allowing for a perfect complex between inorganic silica and organic polymer, for high-energy-density LMBs.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

An unbiased parametric imaging algorithm for nonuniformly sampled biomedical system parameter estimation

An unbiased algorithm of generalized linear least squares (GLLS) for parameter estimation of nonuniformly sampled biomedical systems is proposed. The basic theory and detailed derivation of the algorithm are given. This algorithm removes the initial values required and computational burden of nonlinear least regression and achieves a comparable estimation quality in terms of the estimates' bias and standard deviation. Therefore, this algorithm is particular useful in image-wide (pixel-by-pixel based) parameter estimation, e.g., to generate parametric images from tracer dynamic studies with positron emission tomography. An example is presented to demonstrate the performance of this new technique. This algorithm is also generally applicable to other continuous system parameter estimation.