Regulating Anionic Redox via Mg Substitution in Mn-Rich Layered Oxide Cathodes Enabling High Electrochemical Stability for Sodium-Ion Batteries.

With the limited resources and high cost of lithium-ion batteries (LIBs) and the ever-increasing market demands, sodium-ion batteries (SIBs) gain much interest due to their economical sustainability, and similar chemistry and manufacturing processes to LIBs. As cathodes play a vital role in determining the energy density of SIBs, Mn-based layered oxides are promising cathodes due to their low cost, environmental friendliness, and high theoretical capacity. However, the main challenge is structural instability upon cycling at high voltage. Herein, Mg is introduced into the P2-type Na0.62 Ni0.25 Mn0.75 O2 cathode to enhance electrochemical stability. By combining electrochemical testing and material characterizations, it is found that substituting 10 mol% Mg can effectively alleviate the P2-O2 phase transition, Jahn-Teller distortion, and irreversible oxygen redox. Moreover, structural integrity is greatly improved. These lead to enhanced electrochemical performances. With the optimized sample, a remarkable capacity retention of 92% in the half cell after 100 cycles and 95% in the full cell after 170 cycles can be achieved. Altogether, this work provides an alternative way to stabilize P2-type Mn-based layer oxide cathodes, which in turn, put forward the development of this material for the next-generation SIBs.

Achieving High Stability and Performance in P2-Type Mn-Based Layered Oxides with Tetravalent Cations for Sodium-Ion Batteries.

P2-type sodium-manganese-based layered cathodes, owing to their high capacity from both cationic and anionic redox, are a potential candidate for Na-ion batteries (NIBs) to replace Li-ion technology in certain applications. Still, the structure instability originating from irreversible oxygen redox at high voltage remains a challenge. Here, a high sustainability cobalt-free P2-Na0.72 Mn0.75 Li0.24 X0.01 O2  (X = Ti/Si) cathode is developed. The outstanding capacity retention and voltage retention after 150 cycles are obtained in half-cells. The finding shows that Ti localizes on the surface while Si diffuses to the bulk of the particles. Thus, Ti can act as a protective layer that alleviates side reactions in carbonate-based electrolyte. Meanwhile, Si can regulate the local electronic structure and suppress oxygen redox activities. Notably, full-cells with hard carbon (≈300-335 W h kg-1 based on the cathode mass) deliver the capacity retention of 83% for P2-Na0.72 Mn0.75 Li0.24 Si0.01 O2  and 66% for P2-Na0.72 Mn0.75 Li0.24 Ti0.01 O2  after 500 cycles; this electrochemical stability is the best compared to other reported cathodes based on oxygen redox at present. The superior cycle performance also stems from the ability to inhibit microcracking and planar gliding within the particles. Altogether, this finding offers a new composition for developing high-performance low-cost cathodes for NIBs and highlights the unique role of Ti/Si ions.

Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks.

Automatic retinal vessel segmentation is important for the diagnosis and prevention of ophthalmic diseases. The existing deep learning retinal vessel segmentation models always treat each pixel equally. However, the multi-scale vessel structure is a vital factor affecting the segmentation results, especially in thin vessels. To address this crucial gap, we propose a novel Fully Attention-based Network (FANet) based on attention mechanisms to adaptively learn rich feature representation and aggregate the multi-scale information. Specifically, the framework consists of the image pre-processing procedure and the semantic segmentation networks. Green channel extraction (GE) and contrast limited adaptive histogram equalization (CLAHE) are employed as pre-processing to enhance the texture and contrast of retinal blood images. Besides, the network combines two types of attention modules with the U-Net. We propose a lightweight dual-direction attention block to model global dependencies and reduce intra-class inconsistencies, in which the weights of feature maps are updated based on the semantic correlation between pixels. The dual-direction attention block utilizes horizontal and vertical pooling operations to produce the attention map. In this way, the network aggregates global contextual information from semantic-closer regions or a series of pixels belonging to the same object category. Meanwhile, we adopt the selective kernel (SK) unit to replace the standard convolution for obtaining multi-scale features of different receptive field sizes generated by soft attention. Furthermore, we demonstrate that the proposed model can effectively identify irregular, noisy, and multi-scale retinal vessels. The abundant experiments on DRIVE, STARE, and CHASE_DB1 datasets show that our method achieves state-of-the-art performance.

Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks.

Uveal melanoma is the most common primary intraocular malignancy in adults, with nearly half of all patients eventually developing metastases, which are invariably fatal. Manual assessment of the level of expression of the tumor suppressor BRCA1-associated protein 1 (BAP1) in tumor cell nuclei can identify patients with a high risk of developing metastases, but may suffer from poor reproducibility. In this study, we verified whether artificial intelligence could predict manual assessments of BAP1 expression in 47 enucleated eyes with uveal melanoma, collected from one European and one American referral center. Digitally scanned pathology slides were divided into 8176 patches, each with a size of 256 × 256 pixels. These were in turn divided into a training cohort of 6800 patches and a validation cohort of 1376 patches. A densely-connected classification network based on deep learning was then applied to each patch. This achieved a sensitivity of 97.1%, a specificity of 98.1%, an overall diagnostic accuracy of 97.1%, and an F1-score of 97.8% for the prediction of BAP1 expression in individual high resolution patches, and slightly less with lower resolution. The area under the receiver operating characteristic (ROC) curves of the deep learning model achieved an average of 0.99. On a full tumor level, our network classified all 47 tumors identically with an ophthalmic pathologist. We conclude that this deep learning model provides an accurate and reproducible method for the prediction of BAP1 expression in uveal melanoma.

Potassium perylene-tetracarboxylate with two-electron redox behaviors as a highly stable organic anode for K-ion batteries.

Potassium perylene-3,4,9,10-tetracarboxylate was exploited as a new organic anode for potassium-ion batteries, exhibiting two-electron redox behavior and a theoretical capacity of 93 mA h g-1. After optimization, the K4PTC electrode showed an average of 50 mA h g-1 over 2500 cycles at a current density of 0.5 A g-1.