Underlying burning resistant mechanisms for titanium alloy

The "titanium fire" as produced during high pressure and friction is the major failure scenario for aero-engines. To alleviate this issue, Ti-V-Cr and Ti-Cu-Al series burn resistant titanium alloys have been developed. However, which burn resistant alloy exhibit better property with reasonable cost needs to be evaluated. This work unveils the burning mechanisms of these alloys and discusses whether burn resistance of Cr and V can be replaced by Cu, on which thorough exploration is lacking. Two representative burn resistant alloys are considered, including Ti14(Ti-13Cu-1Al-0.2Si) and Ti40(Ti-25V-15Cr-0.2Si)alloys. Compared with the commercial non-burn resistant titanium alloy, i.e., TC4(Ti-6Al-4V)alloy, it has been found that both Ti14 and Ti40 alloys form "protective" shields during the burning process. Specifically, for Ti14 alloy, a clear Cu-rich layer is formed at the interface between burning product zone and heat affected zone, which consumes oxygen by producing Cu-O compounds and impedes the reaction with Ti-matrix. This work has established a fundamental understanding of burning resistant mechanisms for titanium alloys. Importantly, it is found that Cu could endow titanium alloys with similar burn resistant capability as that of V or Cr, which opens a cost-effective avenue to design burn resistant titanium alloys.Comment: 6 figure

Optimisation Models for Pathway Activity Inference in Cancer

BACKGROUND: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. METHODOLOGY: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. RESULTS: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction

Investigation of Microstructure Evolution and Phase Selection of Peritectic Cuce Alloy During High-Temperature Gradient Directional Solidification

In this work, a CuCe alloy was prepared using a directional solidification method at a series of withdrawal rates of 100, 25, 10, 8, and 5 μm/s. We found that the primary phase microstructure transforms from cellular crystals to cellular peritectic coupled growth and eventually, changes into dendrites as the withdrawal rate increases. The phase constituents in the directionally solidified samples were confirmed to be Cu2Ce, CuCe, and CuCe + Ce eutectics. The primary dendrite spacing was significantly refined with an increasing withdrawal rate, resulting in higher compressive strength and strain. Moreover, the cellular peritectic coupled growth at 10 μm/s further strengthened the alloy, with its compressive property reaching the maximum value of 266 MPa. Directional solidification was proven to be an impactful method to enhance the mechanical properties and produce well-aligned in situ composites in peritectic systems

Tribology and corrosion properties investigation of a pulse electrodeposition duplex hard-particle-reinforced Ni Mo nanocomposite coating

In this work, Ni-Mo-SiC-TiN composite coatings were prepared by pulse electrodeposition. Through analyzing the effect of particle content on the phase structure and morphology of NiMo coatings, the relationship between nanoparticles co-deposition amount and the mechanical properties and corrosion resistance of Ni matrix composite coatings was evaluated. The results show that the coating prepared at an electrolyte concentration of 20 g/L is flat and dense presenting the highest particle content. The crystallite size ranging from 28.27 nm to 11.85 nm is affected by different nanoparticle concentrations. As shown by the Tafel polarization and wear test, the incorporation of two hard particles improved the coating performance, and the corrosion current density was reduced by 74% to 1.84 μA/cm2. The wear rate decreased from 10.196 × 10−4 mm3/N·m to 2.65 × 10−4 mm3/N·m, and the average friction coefficient decreased to 0.11. The duplex hard particles play a bearing and hindering role during friction and corrosion. Based on the co-deposition kinetic model, a five-step pulse deposition model referring to nanoparticles was established. It is found that SiC/TiN particles co-deposited to the preferential Ni (111) crystal face and subordinate Ni (200) face. And the good binding ability between the matrix and the nanoparticles results in a high load transfer effect, thus enhancing the tribological properties of the coating. Moreover, the interaction between the duplex nanoparticles was further discussed

Identification of Important Biological Pathways for Ischemic Stroke Prediction through a Mathematical Programming Optimisation Model-DIGS

Stroke ranks second after heart disease as a cause of disability in high-income countries and as a cause of death worldwide. Identifying the biomarkers of ischemic stroke is possible to help diagnose stroke cases from non-stroke cases, as well as advancing the understanding of the underlying theory of the disease. In this study, a mathematical programming optimisation framework called DIGS is applied to build a phenotype classification and significant pathway inference model using stroke gene expression profile data. DIGS model is specifically designed for pathway activity inference towards supervised multi-class disease classification and is proved has great performance among the mainstream pathway activity inference methods. The highest accuracy of the prediction on determining stroke or non-stroke samples reaches 84.4% in this work, which is much better than the prediction accuracy produced by currently found stroke gene biomarkers. Also, stroke-related significant pathways are inferred from the outputs of DIGS model in this work. Taken together, the combination of DIGS model and expression profiles of stroke has better performance on the discriminate power of sample phenotypes and is capable of effective in-depth analysis on the identification of biomarkers

Thermal transport in 3D nanostructures

This work summarizes the recent progress on the thermal transport properties of 3D nanostructures, with an emphasis on experimental results. Depending on the applications, different 3D nanostructures can be prepared or designed to either achieve a low thermal conductivity for thermal insulation or thermoelectric devices or a high thermal conductivity for thermal interface materials used in the continuing miniaturization of electronics. A broad range of 3D nanostructures are discussed, ranging from colloidal crystals/assemblies, array structures, holey structures, hierarchical structures, to 3D nanostructured fillers for metal matrix composites and polymer composites. Different factors that impact the thermal conductivity of these 3D structures are compared and analyzed. This work provides an overall understanding of the thermal transport properties of various 3D nanostructures, which will shed light on the thermal management at nanoscale.</p