A facile and elegant method for synthesis of novel N-aryl phenothiazine derivatives from 2-phenylindolizines and phenothiazines through direct electrochemical oxidation has been developed.This approach was performed s...A facile and elegant method for synthesis of novel N-aryl phenothiazine derivatives from 2-phenylindolizines and phenothiazines through direct electrochemical oxidation has been developed.This approach was performed smoothly at room temperature without external oxidant and catalyst.Cyclic voltammetry and in situ FTIR techniques were applied to analyze the cross-coupling process of phenothiazines and 2-phenylindolizines,which helped to select the appropriate reaction potential.Under the optimized conditions,a broad range of substrates were well tolerated,affording the desired products in moderate to excellent isolated yields(up to 91%)with high regioselectivity.Meanwhile,a plausible mechanism involving a radical pathway has been proposed.展开更多
Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limi...Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.展开更多
基金the generous financial support of the National Natural Science Foundations of China(Nos.22178321,21773211 and 21776260)。
文摘A facile and elegant method for synthesis of novel N-aryl phenothiazine derivatives from 2-phenylindolizines and phenothiazines through direct electrochemical oxidation has been developed.This approach was performed smoothly at room temperature without external oxidant and catalyst.Cyclic voltammetry and in situ FTIR techniques were applied to analyze the cross-coupling process of phenothiazines and 2-phenylindolizines,which helped to select the appropriate reaction potential.Under the optimized conditions,a broad range of substrates were well tolerated,affording the desired products in moderate to excellent isolated yields(up to 91%)with high regioselectivity.Meanwhile,a plausible mechanism involving a radical pathway has been proposed.
文摘Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.
