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Equilibrium Sn isotope fractionation properties between aqueous Sn (2+, 4+) species and Sn-bearing minerals are the key to using tin isotopes to trace the transportation, enrichment, and precipitation of tin in various geological processes. However, the application of Sn isotope geochemistry has been impeded by the absence of equilibrium Sn isotopic fractionation factors between Sn-bearing minerals and fluid and between mineral pairs. In this contribution, we conducted first-principles calculations based on the density functional theory to obtain the equilibrium Sn isotopic fractionation factors between aqueous Sn complexes and minerals. For Sn-bearing complexes in solution, the reduced partition function ratios (β) are determined by taking snapshots from the molecular dynamics trajectories and computing the average β of the snapshots based on the lowest energy atomic coordinates. For Sn-bearing minerals, static first-principles periodic density functional theory methods are performed. The results show that the β factors decrease in the sequence of malayaite (s) S n 4 + > cassiterite (s) S n 4 + > S n 4 + C l 4 H 2 O 2 (a q) > S n 2 + F 3 (a q) − > S n 2 + (O H) 2 (a q) > S n 2 + C O 3 (a q) > stannite (s) S n 4 + > S n 2 + C l 3 (a q) − . The predicted Sn isotope fractionation follows several distinct patterns. (1) For minerals, the Sn isotope fractionations (1000lnαminerals-stannite) of cassiteritestannite and malayaite-stannite mineral pairs are controlled by the properties of elements coordinating with tin, and the equilibrium Sn isotope fractionation factors between mineral pairs are large enough to make them powerful Sn isotope thermometers. (2) For Sn-bearing aqueous species, the β values of tin (4+) complexes are remarkably larger than those of all aqueous Sn2+ species, indicating that higher valence tin is preferentially enriched heavy tin isotopes. For aqueous Sn2+ species, the aqueous species with shorter bonds are more-enriched in heavy Sn isotopes than those with longer bonds. When both the valence state and bond length are different, the valence state is the main factor controlling tin isotope fractionation. (3) During the precipitation of various Sn2+ aqueous complexes into cassiterite or malayaite, heavy Sn isotopes tend to be enriched in minerals, while there are two situations for the precipitation of Sn2+ complexes into stannite. When Sn is transported in hydrothermal solution as Sn2+Cl3–, stannite precipitation leads to the enrichment of light tin isotopes in the residual solution and late minerals. On the contrary, other S n 2 + species S n 2 + F 3 − , S n 2 + (O H) 2 and S n 2 + C O 3 that precipitate as stannite will result in the enrichment of heavy tin isotopes in the residual solutions. In addition, the direct precipitation of Sn4+ complexes into cassiterite, malayaite, or stannite also produces considerable tin isotope fractionation. During precipitation, Sn4+ aqueous complexes form cassiterite or malayaite, and heavy Sn isotopes tend to be enriched in minerals; whereas when aqueous Sn4+ species are precipitated into stannite, heavy Sn isotopes are enriched in the residual fluid and late minerals. The calculated results are essential for further understanding the mechanisms of Sn isotopic fractionation in various Sn-involved geological processes. [ABSTRACT FROM AUTHOR]

To explore the application of neural network algorithm model in lung cancer imaging, and provide reference for the application and development of artificial neural network (ANN) algorithm model in lung cancer medical mirroring, so as to promote the development of ANN in this field. Meanwhile, it is hoped that the application of neural network algorithms in medical imaging can improve the survival rate and cure rate of lung cancer. In this study, an ANN algorithm model was selected to establish a lung cancer recognition model. After determining the lung cancer lesion area, the image segmentation algorithm was used to separately display the lung cancer lesion area, and a comparison experiment was designed to verify the accuracy of the model. ANNs were used to identify lung cancer, which can be concluded that the accuracy is 94.6%, the sensitivity is 95.7%, and the specificity is 93.5%. By combining image retrieval methods with lung cancer image segmentation algorithms, the lesion area of lung cancer can be clearly displayed. Therefore, the lung cancer image segmentation algorithm based on the neural network model has good recognition performance. This research can provide reference for the application of neural network algorithm model in the field of cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The Shizhuyuan polymetallic deposit is located in the central part of the Nanling region, southeastern China, and consists of proximal W-Sn-Mo-Bi skarns and greisens and distal Pb-Zn-Ag veins. The sulfides and sulfosalts in the distal veins formed in three distinct stages: (1) an early stage of pyrite and arsenopyrite, (2) a middle stage of sphalerite and chalcopyrite, and (3) a late stage of galena, Ag-, Sn-, and Bi-bearing sulfides and sulfosalts, and pyrrhotite. Combined sulfide and sulfosalt geothermometry and fluid inclusion analyses indicate that the early stage of mineralization occurred at a temperature of ~400 °C and involved boiling under hydrostatic pressure (~200 bar), with the temperature of the system dropping during the late stage to ~200 °C. Laser Raman analysis indicates that the fluid inclusions within the studied minerals are dominated by HO, although some contain carbonate solids and CH gas. Vein-hosted sulfides have δS values of 3.8-6.3‰ that are interpreted as indicative of a magmatic source of sulfur. The mineralization process can be summarized as follows: an aqueous fluid exsolved on final crystallization of the Qianlishan pluton, ascended along fracture zones, cooled to <400 °C, and boiled under hydrostatic conditions, and with decreasing temperature and sulfur fugacity, sulfide and sulfosalt minerals precipitated successively from the Ag-Cu-Zn-Fe-Pb-Sb-As-S-bearing fluid system. [ABSTRACT FROM AUTHOR]