Rechargeable aqueous zinc-ion batteries are a promising candidate for next-generation energy storage devices. However, their practical application is limited by the severe safety issue caused by uncontrollable dendrite growth on zinc anodes. Here we develop faceted titanium dioxide with relatively low zinc affinity, which can restrict dendrite formation and homogenize zinc deposition when served as the protective layer on zinc anodes. The asprepared zinc anodes can be stripped and plated steadily for more than 460 h with low voltage hysteresis and flat voltage plateau in symmetric cells. This work reveals the key role of crystal orientation in zinc affinity and its internal mechanism is suitable for various crystal materials applied in the surface modification of other metal anodes such as lithium and sodium.

No abstract

We present measurements of traffic-related pollutants made near the Long Island Expressway (LIE, I-495), in Queens, NewYork. The Aerodyne Research Inc. (ARI) mobile laboratory (AML) was deployed to map spatial and temporal gradients of gas-phase species and particulate matter (PM) associated with vehicular exhaust in the residential areas near the LIE. We observe that pollutant levels build up during the early morning hours under stable boundary layer conditions yet fall off quickly within 150 m downwind of the highway. An ARI soot particle aerosol mass spectrometer (SP-AMS) provided measurements of the size-resolved chemical composition of refractory black carbon (rBC) and the associated coating species. The average size distribution of the traffic related PM is characterized by a rBC mode centered at ∼100 nm in vacuum aerodynamic diameter, D va (rBC mass fraction ∼50%). A second rBC mode (rBC mass fraction ∼5%) more heavily coated with organic material is also observed at D va ∼500 nm. Positive matrix factorization (PMF) analyses of the traffic-related PM indicates that rBC is mostly associated with hydrocarbon-like organic (HOA) PM. These results are discussed in the context of

A combination of a Cu–Zn solid solution interface on copper mesh with good zinc affinity and a polyacrylamide electrolyte additive (PAM) is developed to modify the zinc anode. As shown by H. Wang et al. in their Communication on page 15841 ff., the Cu–Zn solid solution formed in the initial process can regulate zinc nucleation and PAM can suppress the growth of zinc dendrites in the subsequent cycles. The prepared zinc anodes show a dendrite‐free behavior.

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs (i.e., liver, kidney, and spleen) seems to be a solved problem as the state-of-the-art (SOTA) methods have achieved comparable results with inter-observer variability on existing benchmark datasets. However, most of the existing abdominal organ segmentation benchmark datasets only contain single-center, single-phase, single-vendor, or single-disease cases, thus, it is unclear whether the excellent performance can generalize on more diverse datasets. In this paper, we present a large and diverse abdominal CT organ segmentation dataset, termed as AbdomenCT-1K, with more than 1000 (1K) CT scans from 11 countries, including multi-center, multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation, as well as reveal the unsolved segmentation problems of the SOTA method, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the introduction of the AbdomenCT-1K dataset will promote the future in-depth research towards clinical applicable abdominal organ segmentation methods. Moreover, the datasets, codes and trained models of baseline methods will be publicly available.