Selenium Nanocomposite Cathode with Long Cycle Life for Rechargeable Li-Se Batteries

Selenium (Se) is a potential cathode material for high energy density rechargeable lithium batteries. In this study, a binder‐free Se‐carbon nanotube (CNT) composite electrode has been prepared by a facile chemical method. At initial state, Se is present in the form of branched nanowires with a diameter of <150 nm and a length of 1–2 μm, interwoven with CNTs. After discharge and re‐charge, the Se nanowires are converted to nanoparticles embedded in the CNT network. This synthesis method provides a path for fabricating the Se cathodes with controllable mass loading and thickness. By studying the composite electrodes with different Se loading and thickness, we found that the electrode thickness has a critical impact on the distribution of Se during repeated cycling. Promising cycling performance was achieved in thin electrodes with high Se loading. The composite electrode with 23 μm thickness and 60 % Se loading shows a high initial capacity of 537 mAh g−1 and stable cycling performance with a capacity of 401 mAh g−1 after 500 cycles at 1 C rate. This study reports a synthesis strategy to obtain Se/CNT composite cathode with long cycle life for rechargeable Li−Se batteries

Calibrating Multimodal Learning

Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial modalities when estimating confidence. Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted. To address the issue, we introduce an intuitive principle for multimodal learning, i.e., the confidence should not increase when one modality is removed. Accordingly, we propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness

Electrochemical behavior of tin foil anode in half cell and full cell with sulfur cathode

Tin-based (Sn) metal anode has been considered an attractive candidate for rechargeable lithium batteries due to its high specific capacity, safety and low cost. However, the large volume change of Sn during cycling leads to rapid capacity decay. To address this issue, Sn foil was used as a high capacity anode by controlling the degree of lithium uptake. We studied the electrochemical behavior of Sn foil anode in half cell and full cell with sulfur cathode, including phase transform, morphological change, discharge/charge profiles and cycling performance. Enhanced cycling performance has been achieved by limiting the lithiation capacity of the Sn foil electrode. A full cell consisting of a pre-lithiated Sn foil anode and a sulfur cathode was constructed and tested. The full cell exhibits an initial capacity of 1142 mAh g−1 (based on the sulfur mass in the cathode), followed by stable cycling performance with a capacity retention of 550 mAh g−1 after 100 cycles at C/2 rate. This study reports a potential prospect to utilize Sn and S as a combination in rechargeable lithium batteries