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Abstract: This paper reports a novel hydrothermal method to synthesize nanorod like Gd2O2S:Eu,Ti,Mg persistent phosphor and the characterization of the same. Hydrothermally prepared Gd(OH)3 nanorod precursor, codoped with Eu, Ti and Mg, was converted into the aimed phosphor by calcinating the precursor in CS2 atmosphere. XRD investigation revealed a pure phase of Gd2O2S, indicating that the codoped ions, Eu, Ti and Mg, occupy the lattice position within Gd2O2S crystal, rather than form secondary phase in the obtained phosphor powder. SEM observation showed that the phosphor calcined in CS2 atmosphere inherited the rod-like shape from the precursor, indicating the advantage of the CS2 routine in controlling the phosphor morphology. HRTEM investigation further revealed a crystalline nature of the nanorods, as well as a preferred growth along c axis. When the nanorods were exposed to UV illumination, bright orange emission was generated, due to the intrinsic transitions of Eu3+. The Eu3+ concentration and the calcination temperature were two key factors that greatly influent the photo luminescent behavior of the prepared Gd2O2S:Eu,Ti,Mg phosphor. Persistent luminescence was readily observed after the UV source was switched off, due to the formation of electron traps with suitable trap depth within the matrix by the codoped Ti4+ and Mg2+ ions. [Copyright &y& Elsevier]

Background: Early relapse after hepatectomy presents a significant challenge in the treatment of hepatocellular carcinoma (HCC). The aim of this study was to construct and validate a novel nomogram model for predicting early relapse and survival after hepatectomy for HCC., Patients and Methods: We conducted a large-scale, multicenter retrospective analysis of 1,505 patients with surgically treated HCC from 4 medical centers. All patients were randomly divided into either the training cohort (n=1,053) or the validation cohort (n=452) in a 7:3 ratio. A machine learning-based nomogram model for prediction of HCC was established by integrating multiple risk factors that influence early relapse and survival, which were identified from preoperative clinical data and postoperative pathologic characteristics of the patients., Results: The median time to early relapse was 7 months, whereas the median time from early relapse to death was only 19 months. The concordance indexes of the postoperative nomogram for predicting disease-free survival and overall survival were 0.741 and 0.739, respectively, with well-calibrated curves demonstrating good consistency between predicted and observed outcomes. Moreover, the accuracy and predictive performance of the postoperative nomograms were significantly superior to those of the preoperative nomogram and the other 7 HCC staging systems. The patients in the intermediate- and high-risk groups of the model had significantly higher probabilities of early and critical recurrence (P<.001), whereas those in the low-risk group had higher probabilities of late and local recurrence (P<.001)., Conclusions: This postoperative nomogram model can better predict early recurrence and survival and can serve as a useful tool to guide clinical treatment decisions for patients with HCC.