Effects of casting process on microstructures and flow stress behavior of Mg–9Gd–3Y–1.5Zn–0.8Zr semi-continuous casting billets

Mg–9Gd–3Y–1.5Zn–0.8Zr alloys own high strength, good heat and corrosion resistance. However, it is difficult for the fabrication of large-scale billets, due to the poor deformation ability and strong hot-crack tendency. This work investigated the casting process on the microstructures and flow stress behaviors of the semi-continuous casting billets for the fabrication of large-scale Mg–9Gd–3Y–1.5Zn–0.8Zr billets. The casting process (electromagnetic intensity and casting speed) shows outstanding effects on the microstructures and flow stress behavior of the billets. The billets with the specific casting process (I = 68 A, V = 65 mm/min) exhibit uniform microstructures and good deformation uniformity

Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids

Viscosity is one of the most important fundamental properties of fluids. However, accurate acquisition of viscosity for ionic liquids (ILs) remains a critical challenge. In this study, an approach integrating prior physical knowledge into the machine learning (ML) model was proposed to predict the viscosity reliably. The method was based on 16 quantum chemical descriptors determined from the first principles calculations and used as the input of the ML models to represent the size, structure, and interactions of the ILs. Three strategies based on the residuals of the COSMO-RS model were created as the output of ML, where the strategy directly using experimental data was also studied for comparison. The performance of six ML algorithms was compared in all strategies, and the CatBoost model was identified as the optimal one. The strategies employing the relative deviations were superior to that using the absolute deviation, and the relative ratio revealed the systematic prediction error of the COSMO-RS model. The CatBoost model based on the relative ratio achieved the highest prediction accuracy on the test set (R2 = 0.9999, MAE = 0.0325), reducing the average absolute relative deviation (AARD) in modeling from 52.45 % to 1.54 %. Features importance analysis indicated the average energy correction, solvation-free energy, and polarity moment were the key influencing the systematic deviation.Funder: Horizon-EIC (101070976); Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province (KYCX23-1467); STINT (CH2019-8287); National Natural Science Foundation of China (21838004); Full text license: CC BY-NC-ND 4.0; </p

Direct incorporating small amount of Ce (III) in Cu-SAPO-18 catalysts for enhanced low-temperature NH3-SCR activity: Influence on Cu distribution and Si coordination

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Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysisResearch in context

Summary: Background: Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration disorder with varied rates of deterioration, either between subjects or within different stages of a certain subject. Estimating the course of AD at early stages has treatment implications. We aimed to analyze disease progression to identify distinct patterns in AD trajectory. Methods: We proposed a deep learning model to identify underlying patterns in the trajectory from cognitively normal (CN) to a state of mild cognitive impairment (MCI) to AD dementia, by jointly predicting time-to-conversion and clustering out distinct subgroups characterized by comprehensive features as well as varied progression rates. We designed and validated our model on the ADNI dataset (1370 participants). Prediction of time-to-conversion in AD trajectory was used to validate the expression of the identified patterns. Causality between patterns and time-to-conversion was further inferred using Mendelian randomization (MR) analysis. External validation was performed on the AIBL dataset (233 participants). Findings: The proposed model clustered out patterns characterized by significantly different biomarkers and varied progression rates. The discovered patterns also showed a strong prediction ability, as indicated by hazard ratio (CN→MCI, HR = 3.51, p < 0.001; MCI→AD, HR = 8.11, p < 0.001), C-Index (CN→MCI, 0.618; MCI→AD, 0.718), and AUC (CN→MCI, 3 years 0.802, 5 years 0.876; MCI→AD, 3 years 0.914, 5 years 0.957). In the external validation cohort, our model demonstrated competitive performance on conversion time prediction (CN→MCI, C-Index = 0.693; MCI→AD, C-Index = 0.752). Moreover, suggestive associations between CN→MCI/MCI→AD patterns with four/three SNPs were mediated and MR analysis indicated a causal link between MCI→AD patterns and time-to-conversion in the first three years. Interpretation: Our proposed model identifies biologically and clinically meaningful patterns from real-world data and provides promising performance on time-to-conversion prediction in AD trajectory, which could promote the understanding of disease progression, facilitate clinical trial design, and provide potential for decision-making. Funding: The National Key Research and Development Program of China, the Key R&D Program of Zhejiang, and the National Nature Science Foundation of China

Effect of Mixing Order of Si and Al Sources on the Inner Architecture and Catalytic Performance of ZSM‑5 Zeolites

The performance of zeolites in catalysis and adsorption is closely related to their inner architecture beneath the crystal surface, which however remains less studied due to characterization limitations. Here we report the synthesis of two ZSM-5 zeolite samples by changing only the order of mixing of Si and Al sources, resulting not only in morphological differences of the zeolite crystals but most importantly in defined distinction in their inner architecture. The spatial Si and Al distributions and structural properties of the ZSM-5 zeolite crystals were characterized by high-resolution microscopy under chemically unbiased defect-selective NH4F etching. The Al-zoning and structural features in the ZSM-5 zeolite crystals were explained by the biased nucleation in the Si-rich aluminosilicate amorphous precursor followed by multistage crystal growth in a heterogeneous feedstock. This observation was associated with the different solubility and reactivity of the microscopic aluminosilicate domains with various Si/Al ratios in the amorphous precursors. The zeolites with diverse structural properties showed a high cracking activity in n-hexane cracking reaction and different activity, stability, and product selectivity in the ethylene dehydroaromatization (EDA) reaction. The comprehensive understanding of the zeolite synthesis history and their performance in the EDA reaction revealed the chemical mixing-dependent synthesis–structure–performance correlation of the zeolite catalyst