Constructing core@shell structured photothermal nanosphere with thin carbon layer confined Co-Mn bimetals for pollutant degradation and solar interfacial water evaporation

Photothermal material applied in environmental governance has attracted growing attention.By combining the Stober method and dopamine-triggered coating strategy,Co-Mn precursor was in situ incorporated into the poly dopamine(PDA)layer over the surface of silica cores.Afterwards,a unique photothermal nanosphere with SiO_(2)core and thin carbon layer and dual Co-Mn oxides shell was allowed to form by sequential heat treatment in the inert atmosphere(SiO_(2)@CoMn/C).The bimetallic fraction of Co/Mn in the carbon layer and post-treatment calcination temperature was comprehensively tuned to optimize the peroxymonosulfate(PMS)activation performance of the catalyst.The state of bimetallic species was studied including their physical distribution,chemical valence,and interplay by various characterizations.Impressively,Co oxides appear as dominant monodispersed nanoparticles(~10 nm),while Mn with cluster-like morphology is observed to uniformly distribute over thin-layer carbon and adhered to the surface of SiO_(2)nanospheres(~250 nm).The calcined temperature could tune the oxidized state of Co species,leading to the optimization of the catalytic performance of introduced dual metal species.As a result,this obtained optimal catalyst integrated the advantages of exposed bimetallic CoMn species and N-doped thin carbon to deliver excellent catalytic PMS activation performance and photothermal synergetic catalytic mineralization ability for diversiform pollutants.Further reactions condition controls and anion interference studies were conducted to identify the adaptability of the optimal catalyst.Moreover,the application of solar-driven interfacial water evaporation using optimal SiO_(2)@Co_3Mn_1/C-600 catalyst was explored,showing a high water evaporation rate of 1.48 kg·m^(-2)·h^(-1)and an efficiency of 95.2%,further revealing a comprehensive governance functionality of obtained material in the complex pollution condition.

Rare earth element-modified MOF materials:synthesis and photocatalytic applications in environmental remediation

Metal-organic framework-like materials(MOFs)have been developed in the fields of photocatalysis for their excellent optical properties and physicochemical properties,including environmental remediation,CO_(2)photoreduction,water splitting,and so on.With their important roles in various fields,rare earth elements have received growing interests from scientists.Modifying MOFs with rare earth elements for modification allows broadening the absorption spectrum,while the active electrons on their empty 4f orbitals can act as traps to capture photoexcited carriers to inhibit the recombination of electron-hole pairs,thus promoting photocatalytic activity.Therefore,rare earth elements modified MOFs provide an attractive way to achieve their high value utilization.In this mini-review,the synthesis of rare earth element-modified MOFs photocatalysts and corresponding applications in the removal of antibiotics,CO_(2)reduction,and hydrogen production are constructively summarized and discussed.Finally,the latest advancements and current difficulties of these materials as well as the application prospects are also provided.

Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion

The early detection of diabetic retinopathy is crucial for preventing blindness.However,it is time-consuming to analyze fundus images manually,especially considering the increasing amount of medical images.In this paper,we propose an automatic diabetic retinopathy screening method using color fundus images.Our approach consists of three main components:edge-guided candidate microaneurysms detection,candidates classification using mixed features,and diabetic retinopathy prediction using fused features of image level and lesion level.We divide a screening task into two sub-classification tasks:(1)verifying candidate microaneurysms by a naive Bayes classifier;(2)predicting diabetic retinopathy using a support vector machine classifier.Our approach can effectively alleviate the imbalanced class distribution problem.We evaluate our method on two public databases:Lariboisière and Messidor,resulting in an area under the curve of 0.908 on Lariboisière and 0.832 on Messidor.These scores demonstrate the advantages of our approach over the existing methods.

Supervised Vessels Classification Based on Feature Selection

Arterial-venous classification of retinal blood vessels is important for the automatic detection of cardiovascular diseases such as hypertensive retinopathy and stroke. In this paper, we propose an arterial-venous classification （AVC） method, which focuses on feature extraction and selection from vessel centerline pixels. The vessel centerline is extracted after the preprocessing of vessel segmentation and optic disc （OD） localization. Then, a region of interest （ROI） is extracted around OD, and the most efficient features of each centerline pixel in ROI are selected from the local features, grey-level co-occurrence matrix （GLCM） features, and an adaptive local binary patten （A-LBP） feature by using a max-relevance and min-redundancy （mRMR） scheme. Finally, a feature-weighted K-nearest neighbor （FW-KNN） algorithm is used to classify the arterial-venous vessels. The experimental results on the DRIVE database and INSPIRE-AVR database achieve the high accuracy of 88.65% and 88.51% in ROI, respectively.