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1.
Materials (Basel) ; 16(23)2023 Nov 23.
Article in English | MEDLINE | ID: mdl-38068042

ABSTRACT

Vacuum induction melting in a refractory crucible is an economical method to produce TiAl-based alloys, aiming to reduce the preparation cost. In this paper, a Sr2CeZrO6 refractory was synthesized by a solid-state reaction method using SrCO3, CeO2 and ZrO2 as raw materials, and its interaction with TiAl alloy melt was investigated. The results showed that a single-phase Sr2CeZrO6 refractory could be fabricated at 1400 °C for 12 h, and its space group was Pnma with a = 5.9742(3) Å, b = 8.3910(5) Å and c = 5.9069(5) Å. An interaction layer with a 40µm thickness and dense structure could be observed in Sr2CeZrO6 crucible after melting TiAl alloy. Additionally, the interaction mechanism showed that the Sr2CeZrO6 refractory dissolved in the alloy melt, resulting in the generation of Sr3Zr2O7, SrAl2O4 and CeO2-x, which attached to the surface of the crucible.

2.
Biol Trace Elem Res ; 116(3): 257-72, 2007 Jun.
Article in English | MEDLINE | ID: mdl-17709906

ABSTRACT

A change in the normal concentration of essential trace elements in the human body might lead to major health disturbances. In this study, hair samples were collected from 115 human subject, including 55 healthy people and 60 patients with prostate cancer. The concentrations of 20 trace elements (TEs) in these samples were measured by inductively coupled plasma-mass spectrometry. Asupport vector machine was used to investigate the relationship between TEs and prostate cancer. It is found that, among the 20 TEs, 10 (Mg P, K, Ca, Cr, Mn, Fe. Cu, Zn, and Se) are related to the risk of prostate cancer. These 10 TEs were used to build the prediction model for prostate cancer. The model obtained can satisfactorily distinguish the healthy samples from the cancer samples. Furthermore, the cross-validation by leaving-one method proved that the prediction ability of this model reaches as high as 95.8%. It is practical to predict the risk of prostate cancer using this model in the clinics.


Subject(s)
Hair/metabolism , Prostate/metabolism , Prostatic Neoplasms/metabolism , Trace Elements/analysis , Data Interpretation, Statistical , Decision Support Techniques , Humans , Male , Mass Spectrometry/methods , Metals/analysis , Models, Statistical , Risk
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