Because of its high compatibility with conventional microfabrication processing technology, epitaxial graphene (EG) grown on SiC shows exceptional promise for graphene-based electronics. However, to date, a detailed understanding of the transformation from three-layer SiC to monolayer graphene is still lacking. Here, we demonstrate the direct atomic-scale observation of EG growth on a SiC (11̅00) surface at 1,000 °C by in situ transmission electron microscopy in combination with ab initio molecular dynamics (AIMD) simulations. Our detailed analysis of the growth dynamics of monolayer graphene reveals that three SiC (11̅00) layers decompose successively to form one graphene layer. Sublimation of the first layer causes the formation of carbon clusters containing short chains and hexagonal rings, which can be considered as the nuclei for graphene growth. Decomposition of the second layer results in the appearance of new chains connecting to the as-formed clusters and the formation of a network with large pores. Finally, the carbon atoms released from the third layer lead to the disappearance of the chains and large pores in the network, resulting in a whole graphene layer. Our study presents a clear picture of the epitaxial growth of the monolayer graphene from SiC and provides valuable information forfuture developments in SiC-derived EG technology.

A universal and facile methodology for highly flexible supercapacitors with silver current collectors fabricated by ion-exchange surface metallization has been developed. The supercapacitors show a high conductivity of 1.68 × 10⁵ S cm⁻¹ and high mechanical strength with more than 16 000 bending cycles. With multiple-walled carbon nanotubes as the electrode material, micro-supercapacitors (MSCs) with symmetric interdigitated electrodes were fabricated at room temperature. MSCs allow operations at high rates of more than 200 V s⁻¹, which is two orders of magnitude higher than that of conventional supercapacitors. With robust Ag current collectors, high cycling properties had been obtained, which have been verified by CV testing. MSCs can bear more than 15 000 cycling tests and can retain more than 97% of the original capacitance. The developed polymer metallization technique has a potential application to construct a whole electronic system on one chip.

Resistive switching in transition metal oxides could form the basis for next-generation non-volatile memory (NVM). It has been reported that the current in the high-conductivity state of several technologically relevant oxide materials flows through localized filaments, but these filaments have been characterized only individually, limiting our understanding of the possibility of multiple conductive filaments nucleation and rupture and the correlation kinetics of their evolution. In this study, direct visualization of uncorrelated multiple conductive filaments in ultra-thin HfO₂-based high-κ dielectric resistive random access memory (RRAM) device has been achieved by high-resolution transmission electron microscopy (HRTEM), along with electron energy loss spectroscopy (EELS), for nanoscale chemical analysis. The locations of these multiple filaments are found to be spatially uncorrelated. The evolution of these microstructural changes and chemical properties of these filaments will provide a fundamental understanding of the switching mechanism for RRAM in thin oxide films and pave way for the investigation into improving the stability and scalability of switching memory devices.

Background: Differential cell counts (DCCs) on bone marrow aspirate (BMA) smear is a critical step in the diagnosis and treatment of blood and bone marrow diseases. However, manual counts relies on the experience of pathologists and is very time-consuming. In recent years, deep learning-based intelligent cell detection models have achieved high detection accuracy on datasets of specific diseases and medical centers, but these models depend on a large amount of annotated data and have poor generalization. When the detection task changes or model is applied in different medical centers, we need to re-annotate a large amount of data and retrain the model to ensure detection accuracy.Methods: To address the above issues, we designed an automated framework for whole-slide bone marrow aspirate smear differential cell counts (BMADCC), called VFM-SSL-BMADCC-Framework. This framework only requires whole-slide images (WSIs) as input to generate DCCs. The vision foundation model SAM, known for its strong generalization ability, precisely segments cells within the countable regions of the BMA. The MAE, pre-trained on a large unlabeled cell dataset, excels in generalized feature extraction, enabling accurate classification of cells for counting. Additionally, TextureUnet and TCNet, with their powerful texture feature extraction capabilities, effectively segment the body-tail junction areas from WSIs and classify suitable tiles for DCCs. The framework was trained and validated on 40 WSIs from Chongqing Cancer Hospital. To assess its generalization ability across different medical centers and diseases, correlation tests were conducted using 13 WSIs from Chongqing Cancer Hospital and 5 WSIs from Southwest Hospital.Results: The framework demonstrated high accuracy across all stages: The IoU for region of interest (ROI) segmentation was 46.19%, and the accuracy for tile of interest (TOI) classification was 90.45%, the Recall75 for cell segmentation was 99.01%, and the accuracy for cell classification was 77.92%. Experimental results indicated that the automated framework had excellent cell classification and counts performance, suitable for BMADCC across different medical centers and diseases. The differential cell counts results from all centers were highly consistent with manual analysis.Conclusion: The proposed VFM-SSL-BMADCC-Framework effectively automates differential cell counts on bone marrow aspirate smears, reducing reliance on extensive annotations and improving generalization across medical centers.