RESUMEN
The physiochemical nature of reactive metal electrodeposits during the early stages of electrodeposition is rarely studied but known to play an important role in determining the electrochemical stability and reversibility of electrochemical cells that utilize reactive metals as anodes. We investigated the early-stage growth dynamics and reversibility of electrodeposited lithium in liquid electrolytes infused with brominated additives. On the basis of equilibrium theories, we hypothesize that by regulating the surface energetics and surface ion/adatom transport characteristics of the interphases formed on Li, Br-rich electrolytes alter the morphology of early-stage Li electrodeposits; enabling late-stage control of growth and high electrode reversibility. A combination of scanning electron microscopy (SEM), image analysis, X-ray photoelectron spectroscopy (XPS), electrochemical impedance spectroscopy (EIS), and contact angle goniometry are employed to evaluate this hypothesis by examining the physical-chemical features of the material phases formed on Li. We report that it is possible to achieve fine control of the early-stage Li electrodeposit morphology through tuning of surface energetic and ion diffusion properties of interphases formed on Li. This control is shown further to translate to better control of Li electrodeposit morphology and high electrochemical reversibility during deep cycling of the Li metal anode. Our results show that understanding and eliminating morphological and chemical instabilities in the initial stages of Li electroplating via deliberately modifying energetics of the solid electrolyte interphase (SEI) is a feasible approach in realization of deeply cyclable reactive metal batteries.
RESUMEN
Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy diagnosis important. This study used whole-slide images (WSIs) of 187 FA and 100 PT core biopsies, to investigate the potential role of artificial intelligence (AI) in FEL diagnosis. A total of 9228 FA patches and 6443 PT patches was generated from WSIs of the training subset, with each patch being 224 × 224 pixel in size. Our model employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction from the patches, and a recurrent neural network (RNN) component for whole-slide classification using activation values from the global average pooling layer in the CNN model. It achieved an overall slide-level accuracy of 87.5%, with accuracies of 80% and 95% for FA and PT slides respectively. This affirms the potential role of AI in diagnostic discrimination between FA and PT on core biopsies which may be further refined for use in routine practice.