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Selective laser melting (SLM) is an additive manufacturing method with rapid solidification properties, which is conducive to the preparation of alloys with fine microstructures and uniform chemical compositions. Magnesium alloys are lightweight materials that are widely used in the aerospace, biomedical and other fields due to their low density, high specific strength, and good biocompatibility. However, the poor laser formability of magnesium alloy restricts its application. This paper discusses the current research status both related to the theoretical understanding and technology applications. There are problems such as limited processable materials, immature process conditions and metallurgical defects on SLM processing magnesium alloys. Some efforts have been made to solve the above problems, such as adding alloy elements and applying postprocessing. However, the breakthroughs in these two areas are rarely reviewed. Due to the paucity of publications on postprocessing and alloy design of SLMed magnesium alloy powders, we review the current state of research and progress. Moreover, traditional preparation techniques of magnesium alloys are evaluated and related to the SLM process with a view to gaining useful insights, especially with respect to the postprocessing and alloy design of magnesium alloys. The paper also reviews the influence of process parameters on formability, densification and mechanical behavior of magnesium. In addition, the progress of microstructure and metallurgical defects encountered in the SLM processed parts is described. Finally, this article summarizes the research results, and with respect to materials and metallurgy, the new challenges and prospects in the SLM processing of magnesium alloy powders are proposed with respect to alloy design, base material purification, inclusion control and theoretical calculation, and the role of intermetallic compounds.

Ancient bronzes are invaluable for studying the cultures and history of ancient societies around the world. However, corrosion can diminish their research and aesthetic value, as well as affect their longevity. Therefore, it is crucial to study the corrosion behavior and mechanisms of these artifacts using advanced characterization techniques. This article provides a systematic review of the corrosion behavior of bronze artifacts and the advanced characterization techniques employed in their study. It summarizes the corrosion mechanisms of bronze artifacts and the factors affecting corrosion, including composition, structure, and the external environment. It also describes advanced analytical techniques for characterizing corrosion products and mechanisms, such as X-ray fluorescence (XRF), laser ablation coupled to quadrupole mass spectrometry (LAMQS), X-ray tomography (CT), and neutron diffraction. Bronze corrosion studies can be enhanced by the integration of artificial intelligence (AI) and machine learning (ML). Finally, it discusses potential future research directions in the field of bronze artifact corrosion and conservation.

Trace NO 2 detection is essential for the production and life, where the sensing strategy is appropriate for rapid detection but lacks molecular specificity. This investigation proposes a sensing mechanism dominated by surface-scattering to achieve the molecularly-specific detection. Two-dimensional Bi 2 O 2 Se is firstly fabricated into a Schottky-junction-based gas-sensor. Applied with an alternating excitation, the sensor simultaneously outputs multiple response signals (i.e., resistance, reactance, and the impedance angle). Their response times are shorter than 200 s at room temperature. In NO 2 sensing, these responses present the detection limit in ppt range and the sensitivity is up to 16.8 %·ppb −1 . This NO 2 sensitivity presents orders of magnitude higher than those of the common gases within the exhaled breath. The impedance angle is involved in the principle component analysis together with the other two sensing signals. Twelve kinds of typical gases containing NO 2 are acquired with molecular characteristics. The change in dipole moment of the target molecule adsorbed is demonstrated to correlate with the impedance angle via surface scattering. The proposed mechanism is confirmed to output ultra-sensitive sensing responses with the molecular characteristic. Here, trace detection of NO2 is achieved with a 2D Schottky-junction Bi 2 O 2 Se gas sensor. Simultaneous signals allow for detection in the ppt range, and impedance angle analysis allows for molecular specificity.

In this paper, the precipitation thermodynamics and growth kinetics of TiN inclusions in GCr15 bearing steel during solidification were calculated in more detail. A more reasonable formula for calculating the segregation of the solute elements was adopted and the stability diagram of TiN precipitation considering solidification segregation was given. By solving equations, the change of the solute element content before and after TiN inclusion precipitation was calculated, and the results were substituted into the kinetic formula of the inclusion growth, which made the kinetic calculation more accurate. Results showed that the most effective way to reduce the precipitation of TiN is to increase the cooling rate and decrease the contents of Ti and N in steel. The effect of Ti content on the size of TiN inclusions is greater than that of N content.

Energy consumption is directly related to the energy supply and production costs of gas-based direct reduction ironmaking, which is an effective choice to reduce the energy consumption of iron making. In this paper, the minimum Gibbs free energy principle was used to calculate the equilibrium composition under the conditions of reduction gas consisting of hydrogen and carbon monoxide (hydrogen concentration of 0–100%, reduction gas amount of 0–6.0 mol, reduction temperature of 790–1100 °C, and 0.5 mol Fe2O3). According to the enthalpy change, a simplified energy consumption model of a gas-based direct reduction ironmaking process was established, and the energy consumption per mole of metallic iron produced was calculated in detail. The following conclusions were drawn: at the stage when the reduction reaction occurred, the utilization rate of hydrogen or carbon monoxide remained unchanged with the increase in the amount of reduction gas or the increase in the hydrogen concentration of initial gas. The direct energy consumption increased with the increase in the hydrogen concentration at 790–980 °C and the opposite was true at 980–1100 °C. At 790–980 °C, the total energy consumption per ton of iron was greater than 0 and increased with the increase in initial hydrogen concentration from 40% to 100%, and it was less than 0 and increased with the increase in initial hydrogen concentration from 0% to 30%. It was possible to achieve zero total energy consumption with a hydrogen concentration of 30% and a 973 °C reduction.

The properties of carbides, such as morphology, size, and type, in H13 hot work die steel were studied with optical microscopy, transmission electron microscopy, electron diffraction, and energy dispersive X-ray analysis; their size distribution and quantity after tempering, at different positions within the ingot, were analyzed using Image-Pro Plus software. Thermodynamic calculations were also performed for these carbides. The microstructures near the ingot surface were homogeneous and had slender martensite laths. Two kinds of carbide precipitates have been detected in H13: (1) MC and M6C, generally smaller than 200 nm; and (2) M23C6, usually larger than 200 nm. MC and M6C play the key role in precipitation hardening. These are the most frequent carbides precipitating at the halfway point from the center of the ingot, and the least frequent at the surface. From the center of the ingot to its surface, the size and volume fraction of the carbides decrease, and the toughness improves, while the contribution of the carbides to the yield strength increases.

Metallographic, electrolytic method and RTO(room temperature organic) technique were applied in the present study to more accurately determine non-metallic inclusion in a ultra-low carbon interstitial free(IF) steel and further to confirm their origination in a Compact Strip Production Process (CSP Process) continuous casting (CC) slab. Results show that inclusions detected by metallographic method usually appear relative smaller size and are mainly Al 2 O 3 based or TiN based in composition, whereas those extracted by electrolytic method usually have larger size and are much more calcium-silicate based in composition. In addition, inner structures of extracted inclusions were detected by applying of RTO technique. The large size calcium-silicate based inclusions are confirmed high possibilities originating from mold flux and/or tundish flux entrapment, which are less affected by the liquid steel composition; while the smaller ones are generally endogenous inclusion precipitating during the refining or solidification process that strongly depending on the liquid steel composition and temperature.

Metal additive manufacturing (metal-AM) technology has made significant progress in the field of biomedicine in recent years. Originally, it was only used as an innovative resource for prototypes. With the development of technology, custom orthopedic implants could be produced for different patients. Titanium alloy is non-toxic and harmless in the human body. It has excellent biocompatibility and can promote the growth and regeneration of bones in its interior. Therefore, it is widely used in the medical industry. However, in the process of additive manufacturing and printing titanium alloys, there are often cases where the powder is not completely melted or the powder adheres to the product structure after printing, which introduces new biological risks. This paper summarizes the causes of powder adhesion from the perspective of the process involved in additive manufacturing, expounds the influence of different processes on the powder adhesion of titanium alloy forming parts, introduces the mainstream methods of powder sticking removal and summarizes the application of the additive manufacturing of titanium alloy in the medical field, which provides a theoretical basis for further development of the application of titanium alloy additive manufacturing technology in the medical industry.

The mechanical properties and microstructures, in particular precipitation in the AISI H13 steel, quenched and tempered from 773 K to 973 K for different periods, were systematically investigated by scanning electron microscopy, electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The results indicate a sharp decrease in hardness at temperatures > 863 K during the first 2 h of tempering. Ultimate tensile strength and yield strength decrease, while elongation and impact energy increase with the increase of tempering temperature. The volume fraction of static recrystallization increases from the EBSD result. Regarding precipitates, the coarsening rate of M 23 C 6 was much faster than that of MC and M 2 C and was verified by using the Ostwald ripening model. In addition, kinetic modeling of the softening of H13 during tempering from 863 K to 973 K was performed. This model was also applied successfully to predict the hardness of double-tempered H13 steel samples.

BackgroundBreast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis.MethodsWe used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level.ResultsOur single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets.ConclusionsThis study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.