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Strong enhancement of exciton binding has been observed in valence-excitons in the optical regime of 2D materials. We report direct observation of long-lived core-exciton states in transition metal dichalcogenides by attosecond transient absorption spectroscopy in the XUV.

The tantalizing prospect of harnessing the unique properties of graphene crumpled nanostructures continues to fuel tremendous interest in energy storage and harvesting applications. However, the paper ball-like, hard texture, and closed-sphere morphology of current 3D graphitic nanostructure production not only constricts the conductive pathways but also limits the accessible surface area. Here, we report new insights into electrohydrodynamically-generated droplets as colloidal nanoreactors in that the stimuli-responsive nature of reduced graphene oxide can lead to the formation of crumpled nanostructures with a combination of open structures and doubly curved, saddle-shaped edges. In particular, the crumpled nanostructures dynamically adapt to non-spherical, polyhedral shapes under continuous deposition, ultimately assembling into foam-like microstructures with a highly accessible surface area and spatially interconnected transport pathways. The implementation of such crumpled nanostructures as three-dimensional rear contacts for solar conversion applications realize benefits of a high aspect ratio, electrically addressable and energetically favorable interfaces, and substantial enhancement of both short-circuit currents and fill-factors compared to those made of planar graphene counterparts. Further, the 3D crumpled nanostructures may shed lights onto the development of effective electrocatalytic electrodes due to their open structure that simultaneously allows for efficient water flow and hydrogen escape.

Simple Summary: This study presented an artificial intelligence-based classification emphasizing error identification and quantification of cellularity and nuclear morphological features in digital pathological images of common astrocytic tumors. The identification of incorrect predictions was essential for the subsequent development of better techniques. Quantifying cellularity and nuclear morphological features brought deeper insights into neoplastic morphology and paved the way for further development of a scoring system for objective classification and precision diagnosis to improve interobserver variations. Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors. [ABSTRACT FROM AUTHOR]