Decoupling, quantifying, and restoring aging-induced Zn-anode losses in rechargeable aqueous zinc batteries

The search for batteries beyond Li-ion that offer better performance, reliability, safety, and/or affordability has led researchers to explore a diverse array of candidates. The advantages of Zn-ion batteries reside in zinc’s relatively low reactivity, raising the prospect of a rechargeable battery with a simple aqueous electrolyte and a cheaper, safer option to the organic electrolytes that must be paired with reactive lithium. However, water still reacts with the zinc in corrosion reactions. These consume zinc, lowering the battery’s capacity, and generate gas that accumulates in the sealed cell. We diagnose the contribution of corrosion to performance decay in zinc batteries and reveal the critical role of gas accumulation in deactivating large sections of electrode, which cripples cell performance. Fortunately, electrodes can be reactivated by removal of the gas, demonstrating the importance of designing future cells that either prevent gas formation or facilitate its safe release

The role of an elastic interphase in suppressing gas evolution and promoting uniform electroplating in sodium metal anodes †

Ether solvent based electrolytes exhibit excellent performance with sodium battery anodes, outperforming the carbonate electrolytes that are routinely used with the analogous lithium-ion battery. Uncovering the mechanisms that facilitate this high performance for ether electrolytes, and conversely diagnosing the causes of the poor cycling with carbonate electrolytes, is crucial for informing the design of optimized electrolytes that promote fully reversible sodium cycling. An important contributor to the performance difference has been suggested to be the enhanced elasticity of the ether-derived solid–electrolyte interphase (SEI) layer, however experimental demonstration of exactly how this translates to improving the microscopic dynamics of a cycled anode remain less explored. Here, we reveal how this more elastic SEI prevents gas evolution at the interface of the metal anode by employing operando electrochemical transmission electron microscopy (TEM) to image the cycled electrode–electrolyte interface in real time. The high spatial resolution of TEM imaging reveals the rapid formation of gas bubbles at the interface during sodium electrostripping in carbonate electrolyte, a phenomenon not observed for the higher performance ether electrolyte, which impedes complete Na stripping and causes the SEI to delaminate from the electrode. This non-conformal and inflexible SEI must thus continuously reform, leading to increased Na loss to SEI formation, as supported by mass spectrometry measurements. The more elastic ether interphase is better able to maintain conformality with the electrode, preventing gas formation and facilitating flat electroplating. Our work shows why an elastic and flexible interphase is important for achieving high performance sodium anodes

Achieving ultra‐high rate planar and dendrite‐free zinc electroplating for aqueous zinc battery anodes

Despite being one of the most promising candidates for grid-level energy storage, practical aqueous zinc batteries are limited by dendrite formation, which leads to significantly compromised safety and cycling performance. In this study, by using single-crystal Zn-metal anodes, reversible electrodeposition of planar Zn with a high capacity of 8 mAh cm−2 can be achieved at an unprecedentedly high current density of 200 mA cm−2. This dendrite-free electrode is well maintained even after prolonged cycling (>1200 cycles at 50 mA cm−2). Such excellent electrochemical performance is due to single-crystal Zn suppressing the major sources of defect generation during electroplating and heavily favoring planar deposition morphologies. As so few defect sites form, including those that would normally be found along grain boundaries or to accommodate lattice mismatch, there is little opportunity for dendritic structures to nucleate, even under extreme plating rates. This scarcity of defects is in part due to perfect atomic-stitching between merging Zn islands, ensuring no defective shallow-angle grain boundaries are formed and thus removing a significant source of non-planar Zn nucleation. It is demonstrated that an ideal high-rate Zn anode should offer perfect lattice matching as this facilitates planar epitaxial Zn growth and minimizes the formation of any defective regions

The dynamic evolution of multipoint interplanetary coronal mass ejections observed with BepiColombo, Tianwen-1, and MAVEN

We present two multipoint interplanetary coronal mass ejections (ICMEs) detected by the Tianwen-1 and Mars Atmosphere and Volatile Evolution spacecraft at Mars and the BepiColombo (0.56 au ∼0.67 au) upstream of Mars from 2021 December 5 to 31. This is the first time that BepiColombo is used as an upstream solar wind monitor ahead of Mars and that Tianwen-1 is used to investigate the magnetic field characteristics of ICMEs at Mars. The Heliospheric Upwind Extrapolation time model was used to connect the multiple in situ observations and the coronagraph observations from STEREO/SECCHI and SOHO/LASCO. The first fast coronal mass ejection event (∼761.2 km s−1), which erupted on December 4, impacted Mars centrally and grazed BepiColombo by its western flank. The ambient slow solar wind decelerated the west flank of the ICME, implying that the ICME event was significantly distorted by the solar wind structure. The second slow ICME event (∼390.7 km s−1) underwent an acceleration from its eruption to a distance within 0.69 au and then traveled with the constant velocity of the ambient solar wind. These findings highlight the importance of background solar wind in determining the interplanetary evolution and global morphology of ICMEs up to Mars distance. Observations from multiple locations are invaluable for space weather studies at Mars and merit more exploration in the future

Physiological records-based situation awareness evaluation under aviation context: A comparative analysis

Situational Awareness (SA) assessment is of paramount importance in various domains, with particular significance in the military for safe aviation decision-making. It involves encompassing perception, comprehension, and projection levels in human beings. Accurate evaluation of SA statuses across these three levels is crucial for mitigating human false-positive and false-negative rates in monitoring complex scenarios in the aviation context. This study proposes a comprehensive comparative analysis by involving two types of physiological records: electroencephalogram (EEG) signals and brain electrical activity mapping (BEAM) images. These two modalities are leveraged to automate precise SA evaluation using both conventional machine learning and advanced deep learning techniques. Benchmarking experiments reveal that the BEAM-based deep learning models attain state-of-the-art performance scores of 0.955 for both SA perception and comprehension levels, respectively. Conversely, the EEG signals-based manual feature extraction, selection, and classification approach achieved a superior accuracy of 0.929 for the projection level of SA. These findings collectively highlight the potential of deploying diverse physiological records as valuable computational tools for enhancing SA evaluation throughout aviation decision-making safety

User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix

With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods

Numerical Study Of Lumped Dispersion Compensation For 40-Gb/S Return-To-Zero Differential Phase-Shift Keying Transmission

Lumped dispersion compensation is numerically investigated for 40-Gb/s return-to-zero differential phase-shift-keying transmission. Using pseudorandom binary sequence lengths up to 211 - 1, simulation results indicate that better long-distance transmission performance can be achieved using lumped dispersion compensation than using conventional periodic inline dispersion compensation. Improved performance is found to be a result of reduced nonlinear effects and the elimination of periodic accumulation of nonlinear effects. The lumped compensation scheme provides a simple, flexible, and potentially low-cost solution for transmission link design. © 2007 IEEE