In situ Analysis of Major Elements, Trace Elements and Sr Isotopic Compositions of Apatite from the Granite in the Chengchao Skarn-Type Fe Deposit, Edong Ore District： Implications for Petrogenesis and Mineralization

Major elements, trace elements and Sr isotopic compositions of apatite from the granite in the Chengchao skarn-type Fe deposit of Edong ore district of Middle-Lower Yangtze River metallogenic belt were measured using EMPA （electron microprobe）, LA-ICP-MS （laser ablation inductively coupled plasma mass spectrometer） and LA-MC （multicollector）-ICP-MS methods in order to reveal the petroge- netic and metallogenic significance of the skarn-type iron deposits. The results show that the apatite in Chengchao granite is fluorapatite, which displays slight variation in major elements. The REE distribution pattern of the apatite is similar to that of the whole rocks, with strong negative Eu anomaly and low Sr/Y ratio. The concentration of Mn in apatite is low （140 ppm-591 ppm） and the Sr isotopic composition shows a limited variation from 0.706 9 to 0.708 2. The high oxygen fugacity of the Chengchao granite, implied by the low Mn content in apatite, is possibly attributed to contamination of the gypsum from sedimentary rock strata, which has long been thought to be an important factor that controls the Fe mineralization in the Middle-Lower Yangtze River metallogenic bell This study also proves that the Eu/Eu＊ value and Sr/Y ra- tio in apatite can be effectively used to identify the adakitie affinity. The in situ Sr isotope analysis of apatite is in consistent with the bulk rock analysis, which indicates that the apatite Sr isotope can represent the ini- tial Sr isotopic compositions of the magma. The Sr isotope and negative Eu anomaly in apatite imply that the Chengchao granite is likely sourced from crust-mantle mixed materials.

Deep learning based wavefront sensor for complex wavefront detection in adaptive optical microscopes

The Shack-Hartmann wavefront sensor(SHWS)is an essential tool for wavefront sensing in adaptive optical microscopes.However,the distorted spots induced by the complex wavefront challenge its detection performance.Here,we propose a deep learning based wavefront detection method which combines point spread function image based Zernike coefficient estimation and wavefront stitching.Rather than using the centroid displacements of each micro-lens,this method first estimates the Zernike coefficients of local wavefront distribution over each micro-lens and then stitches the local wavefronts for reconstruction.The proposed method can offer low root mean square wavefront errors and high accuracy for complex wavefront detection,and has potential to be applied in adaptive optical microscopes.