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Abstract Accurately controlling the product selectivity in syngas conversion, especially increasing the olefin selectivity while minimizing C1 byproducts, remains a significant challenge. Epsilon Fe2C is deemed a promising candidate catalyst due to its inherently low CO2 selectivity, but its use is hindered by its poor high-temperature stability. Herein, we report the successful synthesis of highly stable ε-Fe2C through a N-induced strategy utilizing pyrolysis of Prussian blue analogs (PBAs). This catalyst, with precisely controlled Mn promoter, not only achieved an olefin selectivity of up to 70.2% but also minimized the selectivity of C1 byproducts to 19.0%, including 11.9% CO2 and 7.1% CH4. The superior performance of our ε-Fe2C-xMn catalysts, particularly in minimizing CO2 formation, is largely attributed to the interface of dispersed MnO cluster and ε-Fe2C, which crucially limits CO to CO2 conversion. Here, we enhance the carbon efficiency and economic viability of the olefin production process while maintaining high catalytic activity.

Abstract Background This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. Materials and methods 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification. Results The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92. Conclusions The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model’s classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat’s impact on radiomic features in medical imaging.

External sulfate attack (ESA) coupled with wetting–drying alternation poses a significant threat to the concrete durability, while there are few models to simulate the above ESA behavior. In this paper, a simplified transport-mechanical model is proposed to numerically analyze the deterioration of concrete prism under ESA and dry-wet cycles. Firstly, a transport model of sulfate ion in concrete prism is established by considering the behaviors of ion diffusion, convection and chemical reaction, and numerically solved by the finite difference method. Secondly, the crystallization pressure of sulfate products in porous system is analyzed to calculate the equivalent expansion force. Then, the expansion strain and its-induced damage are calculated, and the criterion of boundary movement is further proposed. On the basis of model validation, a numerical simulation is performed to analyze the transport-mechanical response in concrete prism under ESA and dry-wet cycles. The results show that, there is no clear demarcation between the convection and diffusion zones in the prism. The spatial–temporal evolution of ESA-induced damage is similar to that of ion concentration. The longer the wetting time in one dry-wet cycles, the deeper the spalling of concrete prism caused by ESA.

Transition metal carbide/nitride cores within MXenes make them considerably useful for ultra-high-temperature reinforcement. However, extensive research on Ti3C2Tx MXene has revealed its tendency to undergo a phase transition to TiCy at temperatures above 800 ℃ due to high activity of a superficial Ti atomic layer. Herein, spark plasma sintering of Ti3C2Tx and TiC is performed to prevent the Ti3C2Tx phase transition at temperatures up to 1900 ℃ through the fabrication of composites at a pressure of 50 MPa. Using a focused ion beam scanning electron microscope to separate layered substances in the composites and examining selected area diffraction spots in a transmission electron microscope enabled identification of non-phase-transitioned MXene. First-principles calculations based on density functional theory indicated the formation of strong chemical bonding interfaces between Ti3C2Tx and TiC, which imposed a stability constraint on the Ti atomic layer at the Ti3C2Tx surface. Mechanical performance tests, such as three-point bending and fracture toughness analysis, demonstrated that the addition of Ti3C2Tx can effectively improve the cross-scale strengthening and toughening of the TiC matrix, providing a new path for designing and developing two-dimensional (2D) carbides cross-scale-enhanced three-dimensional (3D) carbides with the same elements relying on a wide variety of MXenes.

Abstract Diabetic cystopathy (DCP) is a prevalent etiology of bladder dysfunction in individuals with longstanding diabetes, frequently leading to bladder interstitial fibrosis. Research investigating the initial pathological alterations of DCP is notably scarce. To comprehend the development of fibrosis and find effective biomarkers for its diagnosis, we prepared streptozotocin-induced long-term diabetic SD rats exhibiting a type 1 diabetes phenotype and bladder fibrosis in histology detection. After observing myofibroblast differentiation from rats’ primary bladder fibroblasts with immunofluorescence, we isolated fibroblasts derived exosomes and performed exosomal miRNA sequencing. The co-differentially expressed miRNAs (DEMis) (miR-16-5p and let-7e-5p) were screened through a joint analysis of diabetic rats and long-term patients’ plasma data (GES97123) downloaded from the GEO database. Then two co-DEMis were validated by quantitative PCR on exosomes derived from diabetic rats’ plasma. Following with a series of analysis, including target mRNAs and transcription factors (TFs) prediction, hubgenes identification, protein–protein interaction (PPI) network construction and gene enrichment analysis, a miRNA-mediated genetic regulatory network consisting of two miRNAs, nine TFs, and thirty target mRNAs were identified in relation to fibrotic processes. Thus, circulating exosomal miR-16-5p and let-7e-5p are associated with bladder fibrosis of DCP, and the crucial genes in regulatory network might hold immense significance in studying the pathogenesis and molecular mechanisms of fibrosis, which deserves further exploration.