The new copper thioantimonates(III) and thioarsenate(III) Rb2Cu2Sb2S5 (1), Cs2Cu2Sb2S5 (2), and Rb8Cu6As8S19 (3) have been synthesized solvothermally. Compound 1 contains a [Cu2SbS3] net to which [Sb2S4] chains adhere through Cu–S bonds and Sb–Cu interactions to give a layered structure. In compound 2, the layer is formed by edge-sharing tetrahedral CuS4 chains linked by dimeric [Sb2S5] units. The layered structure in compound 3 is constructed by helical chains built from [Cu3S7] moieties and dimeric [As2S5] units which are further connected by tetrameric [As4S9] groups. Their optical and thermal properties have been investigated.

Mild Solvothermal Syntheses and Characterization of Layered Copper Thioantimonates(III) and Thioarsenate(III)

Open-framework chalcogenides with ion-exchange capacity are promising materials for removing hazardous heavy-metal ions and for capturing radioactive Cs+. However, research on the exchange mechanis...

Unusual Flexibility of Microporous Sulfides during Ion Exchange.

Hydrazine-hydrothermal syntheses, characterizations and photoelectrochemical properties of two quaternary chalcogenidoantimonates(III) BaCuSbQ3 (Q = S, Se)

Hydro(solvo)thermal syntheses of quaternary copper sulfides containing alkaline earth metal ions remain a great challenge because of the low solubility of Cu–S compounds. Herein, a new facile solvo...

Solvothermal Syntheses and Characterizations of Four Quaternary Copper Sulfides BaCu3MS4 (M = In, Ga) and BaCu2MS4 (M = Sn, Ge).

Two interesting non-centrosymmetric metal chalcogenides, Rb2Ag3Sb3S7 and Cs2Ag3Sb3S7, were synthesized by an ionothermal approach. Crystals of compounds Rb2Ag3Sb3S7 and Cs2Ag3Sb3S7 possess isomorph...

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Ionothermal Synthesis of Metal Chalcogenides M2Ag3Sb3S7 (M = Rb, Cs) Displaying Nonlinear Optical Activity in the Infrared Region.

A series sulfides of A–Ag–S (1–3; A = K, Rb, Cs), Na5AgGe2S7 (4), and Rb2Ag2GeS4 (5) have been prepared solvothermally in the presence of excess sulfur. Among these compounds, 4 is a new compound that has a novel layered structure; the others were obtained under mild conditions. The results showed that excess sulfur could increase the solubility of silver sulfide and lower the synthetic temperature effectively. The mineralizer effect of excess sulfur is discussed in detail.

Mild solvothermal syntheses of thioargentates A-Ag-S (A = K, Rb, Cs) and A-Ag-Ge-S (A = Na, Rb): crucial role of excess sulfur.

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AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images

. Reliable automatic classiﬁcation of colonoscopy images is of great signiﬁcance in assessing the stage of colonic lesions and formulat-ing appropriate treatment plans. However, it is challenging due to uneven brightness, location variability, inter-class similarity, and intra-class dissimilarity, aﬀecting the classiﬁcation accuracy. To address the above issues, we propose a F ourier-based F requency C omplex N etwork (FFC-Net) for colon disease classiﬁcation in this study. Speciﬁcally, FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning to overcome the loss of phase information caused by real convolution operations. Also, our Fourier transform transfers the average brightness of an image to a point in the spectrum (the DC component), alleviating the eﬀects of uneven brightness by decoupling image content and brightness. Moreover, the image patch scrambling module in FFCNet generates random local spectral blocks, empowering the network to learn long-range and local disease-speciﬁc features and improving the discriminative ability of hard samples. We evaluated the proposed FFCNet on an in-house dataset with 2568 colonoscopy images, showing our method achieves high performance out-performing previous state-of-the-art methods with an accuracy of 86 . 35% and an accuracy of 4 . 46% higher than the backbone. The project page with code is available at https://github.com/soleilssss/FFCNet.

FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

DLGNet: A dual-branch lesion-aware network with the supervised Gaussian Mixture model for colon lesions classification in colonoscopy images

Automatic segmentation of the left atrial (LA) cavity and atrial scars in late gadolinium enhancement magnetic resonance imaging has significant clinical relevance to diagnosing atrial fibrillation (AF). Nevertheless, automatic segmentation remains challenging because of the poor image quality, the shape variability of LA, and the small size of scars. Therefore, this study proposes a multi-task learning model in a coarse-to-fine framework, among which the fine model simultaneously segmenting the LA cavity and scars. Specially, we develop an edge-enhanced feature-guided module (EFGM) to exploit the spatial relationship between LA and scars using a 3D central difference convolution, exploring the feature dependence from multi-task learning. Also, a dilated inception module (DIM) is plugged in to learn multi-scale representation, further improving the joint segmentation considering the shape difference between the LA cavity and scar. We evaluate our model on the LAScarQS 2022 validation set. The average Dice scores of the LA cavity and scar are 0.875 and 0.631. Also, the Average Surface Distance (ASD) and Hausdorff Distance (HD) of the LA cavity are 2.233 mm and 24.731 mm, respectively. The accuracy, specificity, sensitivity, and generalized Dice score of LA scar are 0.999, 0.999, 0.603, and 0.916, respectively. 

Kai-Ni Wang

Papers

Mild solvothermal syntheses of thioargentates A-Ag-S (A = K, Rb, Cs) and A-Ag-Ge-S (A = Na, Rb): crucial role of excess sulfur.

AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images

FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

DLGNet: A dual-branch lesion-aware network with the supervised Gaussian Mixture model for colon lesions classification in colonoscopy images

Edge-Enhanced Feature Guided Joint Segmentation of Left Atrial and Scars in LGE MRI Images