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Sample pretreatment is a key step for qualitative and quantitative analysis of trace substances in complex samples. Cis-dihydroxyl (cis-diol) group-containing substances exist widely in biological samples and can be selectively bound by boronate affinity adsorbents. Based on this, in this article, we proposed a simple method for the preparation of novel spherical three-dimensionally ordered macropore (3DOM) materials based on a combination of the boronate affinity technique and colloidal crystal template method. The prepared 3DOM materials were characterized using Fourier transform–infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and thermo-gravimetric analysis, and results showed that they possessed the characteristics of a high specific surface area, high porosity, and more boronic acid recognition sites. The adsorption performance evaluation results showed that the maximum adsorption capacity of the boron affinity 3DOMs on ovalbumin (OVA) could reach to 438.79 mg/g. Kinetic and isothermal adsorption experiments indicated that the boronate affinity 3DOM material exhibited a high affinity and selectivity towards OVA and adenosine. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis analysis of the proteins in egg whites was conducted and proved that the glycoprotein in the egg whites could be separated and enriched with a good performance. Therefore, a novel boronate affinity 3DOM material a with highly ordered and interconnected pore structure was prepared and could be applied in the separation and enrichment of molecules with cis-diol groups from complex samples with a good selectivity, efficiency, and high throughput. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density (MDD) and optimum moisture content (OMC), which are critical for stabilizing construction foundations. Traditional methods for determining MDD and OMC are labor‐intensive and often influenced by factors such as soil type, plasticity, and compaction energy (E). The research employed AI models, including random forest regression (RF‐R), gradient boosting regression (GB‐R), XGBoosting regressor (XGB‐R), and multilinear regression (ML‐R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB‐R demonstrated the highest prediction accuracy for MDD and OMC, outperforming RF‐R, XGB‐R, and ML‐R. The performance of built‐in models has been measured by three new index performance metrics: the a20‐index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that MDD and OMC were most influenced by plastic limit (PL), compaction energy (E), liquid limit (LL), and plasticity index (PI). Additionally, curve‐fitting techniques were applied to model the relationship between MDD, OMC, and these key factors. The results indicated that the GB‐R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction. [ABSTRACT FROM AUTHOR]