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Fenton reaction has been widely used for efficient treatment of organic wastewater. However, its applications are limited by such key factors as pH < 3. In this study, we developed, tested, and optimized an alginate/CNporphyrin bead (CNpor-SA) as a recyclable photocatalyst in a photocatalysis-self-Fenton process to overcome these limitations. Porphyrin-modified CN (CNpor) was used as the HO donator, while Fe(III) nodes served as the Fenton reagent. The as-prepared floating alginate/CNpor bead utilized the light source as a driving force for the catalysis. Under visible light irradiation for 6 h, the model pollutant atrazine was degraded by 70.96 % by the optimized photocatalyst (named as CNpor-SA-Fe1Ca5), demonstrating better photocatalytic performance than alginate/CN beads. This improvement was attributed to the higher HO yield from CNpor. The alginate/CNpor bead showed better photocatalytic activity even after several consecutive cycles and could easily be recovered for reuse. Furthermore, Fe(III)/Ca(II) bimetallic alginate bead exhibited better photocatalytic activity and a higher content of •OH radicals than the Ca(II) monometallic alginate beads, due to the ability of Fe(III) nodes to serve as a Fenton reagent. The influences of light sources, and commonly existing matters (namely SO, Cl, CO, NO, and humic acid) were investigated. Moreover, the alginate/CNpor bead demonstrated good photocatalytic performance in a simulated natural environment without the addition of extra HO, with an atrazine removal percentage of up to 96.3 % after 3-h irradiation. These findings indicated the great potential of alginate/CNpor bead in practical applications.
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http://dx.doi.org/10.1016/j.scitotenv.2024.173112 | DOI Listing |
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
ACS Appl Mater Interfaces
September 2025
School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China.
The development of anode materials for lithium-ion batteries must meet the demands for high safety, high energy density, and fast-charging performance. TiNbO is notable for its high theoretical specific capacity, low structural strain, and exceptional fast-charging capability, attributed to its Wadsley-Roth crystal structure. However, its inherently poor conductivity has hindered its practical application.
View Article and Find Full Text PDFJMIR Res Protoc
September 2025
Department of Urology, Faculty of Medicine, Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Background: Circumcision is a widely practiced procedure with cultural and medical significance. However, certain penile abnormalities-such as hypospadias or webbed penis-may contraindicate the procedure and require specialized care. In low-resource settings, limited access to pediatric urologists often leads to missed or delayed diagnoses.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
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