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3. Lattice structures and mechanical properties of FeCrNi medium-entropy alloy prepared by selective laser melting

Author

SUN Chi, WANG Jian, HE He, QIN Dongyang, CAO Yuankui, FU Ao, and LIU Bin

Subjects
selective laser melting, medium entropy alloy, lattice structure, mechanical property, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Metal lattice structural materials are widely used in aerospace, automotive industry, and other fields due to their advantages of lightweight, high specific strength, energy absorption, and porosity. High strength and toughness FeCrNi medium entropy alloy (MEA) was taken as the research object, and selective laser melting (SLM) was used to prepare FeCrNi medium entropy alloy lattice structure materials with four simulated lattice structures: BCC, BCCZ, FCC and FCC. The microstructure, mechanical properties, and deformation behavior of these materials were systematically studied.The results indicate that the FeCrNi medium entropy alloy lattice structure prepared by the skip scanning strategy has high node overlap quality, dense interlaced stacking of molten pools, and uniform and fine grains. When the relative density is similar, the specific strength and specific energy absorption values of BCC, FCC, BCCZ, and FCCZ lattice structures increase sequentially.The specific energy absorption of FeCrNi medium entropy alloy material with FCCZ lattice structure reaches 49.8J·g-1, significantly higher than that of Ti6Al4V and 316L stainless steel lattice materials.The finite element simulation analysis shows that the presence of Z-shaped pillars increases the apparent strength and stiffness of the lattice material, and leads to a transition in deformation behavior from bending dominated to tensile dominated, which is the main reason for the strength improvement of the FCCZ lattice structure.

Published
2024
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29. Fast Dynamic Response Based on Active Disturbance Rejection Control of Dual Active Bridge DC-DC Converter

Author

Zhang, Zongfeng, Fu, Ao, Tian, Guofeng, Gong, Fan, Zhang, Rui, Xing, Yu, Sun, Yue, Chen, Yulan, Cheng, Xingong, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhang, Haijun, editor, Chen, Yuehui, editor, Chu, Xianghua, editor, Zhang, Zhao, editor, Hao, Tianyong, editor, Wu, Zhou, editor, and Yang, Yimin, editor

Published
2022
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45. Exploring the NRF2‐TP53 Signaling Network Through Machine Learning and Pan‐Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress.

Author

Cao, Yuchen, Fu, Ao, and Liu, Chunjun

Subjects
CANCER prognosis, OXIDATIVE stress, MACHINE learning, RANDOM forest algorithms, ANTINEOPLASTIC agents, REGRESSION analysis
Abstract

Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer‐related OXS. Hence, constructing a TP53‐NRF2 integrated OXS signature of pan‐cancer is essential in predicting survival prognosis and facilitating cancer drug treatment. The pan‐cancer analysis acquired the Cancer Genome Atlas (TCGA) transcriptome sequencing data from UCSC Xena, which consisted of 33 cancer types (n = 10 440). The Random Forest, Lasso regression, and Cox regression analyses are used to construct an OXS score based on 25 OXS genes. Following this, based on the OXS signature, patients are categorized into low‐ and high‐risk groups. The disparities between the two cohorts regarding survival prognosis, immune infiltration, and drug sensitivity are delved deeply. The expression level of genes is confirmed using immunohistochemistry. The prognosis of pan‐cancer patients is adequately predicted by the OXS signature with the assistance of the machine‐learning algorithm. A highly accurate nomogram is developed by combining the OXS signature and clinical features. The presence of immune cells indicated that the OXS signature can be associated with the critical pathways of immunotherapy for all types of cancer, and BCL2 showed promising results. Distinct inter‐group differences are observed in the OXS signature for frequently utilized antineoplastic medications in clinical settings, including first‐line drugs suggested in the guidelines. In summary, by conducting a thorough analysis of OXS genes, a new model based on OXSscore is successfully developed. This model can predict the clinical prognosis and drug sensitivity of pan‐cancer with high accuracy. Potential stars in the field of cancer‐related anti‐OXS may include drugs that target BCL2. [ABSTRACT FROM AUTHOR]

Published
2024
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