The metal–organic framework (MOF) is a class of materials that exhibits a notable capacity for electron transfer. This unique framework design offers potential applications in various fields, including catalysis, gas storage, and sensing. Herein, we focused on a specific type of MOF called Ti-MOF. To enhance its properties and functionality, the composite material was prepared by incorporating graphitic carbon nitride (g-C₃N₄) into the Ti-MOF structure. This composite, known as g-C₃N₄@Ti-MOF, was selected as the active material for ion detection, specifically targeting calcium ions (Ca²⁺). To gain a comprehensive understanding of the structural and chemical properties of the g-C₃N₄@Ti-MOF composite, several analytical techniques were employed to characterize the prepared g-C₃N₄@Ti-MOF composite, including X-ray diffraction (XRD), SEM-EDX, and FT-IR. For comparison, different pastes were prepared by mixing Ti-MOF or g-C₃N₄@Ti-MOF, graphite, and o-NPOE as a plasticizer. The divalent Nernstian responses of the two best electrodes, I and II, were 28.15 ± 0.47 and 29.80 ± 0.66 mV decade⁻¹, respectively, with concentration ranges of 1 µM–1 mM and 0.1 µM–1 mM with a content 1.0 mg Ti-MOF: 250 mg graphite: 0.1 mL o-NPOE and 0.5 mg g-C₃N₄@Ti-MOF: 250 mg graphite: 0.1 mL o-NPOE, respectively. The electrodes showed high sensitivity and selectivity for Ca²⁺ ions over different species. The suggested electrodes have been successfully employed for Ca²⁺ ion measurement in various real samples with excellent precision (RSD = 0.74–1.30%) and accuracy (recovery = 98.5–100.2%), and they exhibited good agreement with the HPLC.

Nucleus accurate segmentation is a crucial task in biomedical image analysis. While convolutional neural networks (CNNs) have achieved notable progress in this field, challenges remain due to the complexity and heterogeneity of cell images, especially in overlapping regions of nuclei. To address the limitations of current methods, we propose a mechanism of multiple differential convolution and local-variation attention in CNNs, leading to the so-called multiple differential convolution and local-variation attention U-Net (MDLA-UNet). The multiple differential convolution employs multiple differential operators to capture gradient and direction information, improving the network’s capability to detect edges. The local-variation attention utilizes Haar discrete wavelet transforms for level-1 decomposition to obtain approximate features, and then derives high-frequency features to enhance the global context and local detail variation of the feature maps. The results on the MoNuSeg, TNBC, and CryoNuSeg datasets demonstrated superior segmentation performance of the proposed method for cells having complex boundaries and details with respect to existing methods. The proposed MDLA-UNet presents the ability of capturing fine edges and details in feature maps and thus improves the segmentation of nuclei with blurred boundaries and overlapping regions.