Cobalt Ion-Stabilized VO2 for Aqueous Ammonium Ion Hybrid Supercapacitors.

Aqueous ammonium ion hybrid supercapacitor (A-HSC) is an efficient energy storage device based on nonmetallic ion carriers (NH4+), which combines advantages such as low cost, safety, and sustainability. However, unstable electrode structures are prone to structural collapse in aqueous electrolytes, leading to fast capacitance decay, especially in host materials represented by vanadium-based oxidation. Here, the Co2+ preintercalation strategy is used to stabilize the VO2 tunnel structure and improve the electrochemical stability of the fast NH4+ storage process. In addition, the understanding of the NH4+ storage mechanism has been deepened through ex situ structural characterization and electrochemical analysis. The results indicate that Co2+ preintercalation effectively enhances the conductivity and structural stability of VO2, and inhibits the dissolution of V in aqueous electrolytes. In addition, the charge storage mechanisms of NH4+ intercalation/deintercalation and the reversible formation/fracture of hydrogen bonds were revealed.

Deep learning algorithm applied to plain CT images to identify superior mesenteric artery abnormalities.

OBJECTIVES: Atypical presentations, lack of biomarkers, and low sensitivity of plain CT can delay the diagnosis of superior mesenteric artery (SMA) abnormalities, resulting in poor clinical outcomes. Our study aims to develop a deep learning (DL) model for detecting SMA abnormalities in plain CT and evaluate its performance in comparison with a clinical model and radiologist assessment. MATERIALS AND METHODS: A total of 1048 patients comprised the internal (474 patients with SMA abnormalities, 474 controls) and external testing (50 patients with SMA abnormalities, 50 controls) cohorts. The internal cohort was divided into the training cohort (n = 776), validation cohort (n = 86), and internal testing cohort (n = 86). A total of 5 You Only Look Once version 8 (YOLOv8)-based DL submodels were developed, and the performance of the optimal submodel was compared with that of a clinical model and of experienced radiologists. RESULTS: Of the submodels, YOLOv8x had the best performance. The area under the curve (AUC) of the YOLOv8x submodel was higher than that of the clinical model (internal test set: 0.990 vs 0.878, P =.002; external test set: 0.967 vs 0.912, P =.140) and that of all radiologists (P <.001). The YOLOv8x submodel, when compared with radiologist assessment, demonstrated higher sensitivity (internal test set: 100.0 % vs 70.7 %, P =.002; external test set: 96.0 % vs 68.8 %, P <.001) and specificity (internal test set: 90.7 % vs 66.0 %, P =.025; external test set: = 88.0 % vs 66.0 %, P <.001). CONCLUSION: Using plain CT images, YOLOv8x was able to efficiently identify cases of SMA abnormalities. This could potentially improve early diagnosis accuracy and thus improve clinical outcomes.