In recent years,a series of aqueous metal ion batteries(AMIBs)has been developed to improve the safety and cost-efficiency of portable electronics and electric vehicles.However,the significant gaps in energy density,p...In recent years,a series of aqueous metal ion batteries(AMIBs)has been developed to improve the safety and cost-efficiency of portable electronics and electric vehicles.However,the significant gaps in energy density,power density,and cycle stability of AMIBs directly hinder them from replacing the currently widely used non-aqueous metal ion batteries,which stems from the lack of reasonable configuration and performance optimization of electrode materials.First-row transition metal compounds(FRTMCs),with the advantages of optional voltage ranges(from low to high),adjustable crystal structures(layered and tunnel types with large spacing),and designable morphology(multi-dimensional nanostructures),are widely used to construct high-performance AMIBs.However,no comprehensive review papers were generated to highlight their specific and significant roles in AMIBs.In this review,we first summarize the superiority and characteristics of FRTMCs in AMIBs.Then,we put forward control strategies of FRTMCs from subsurface engineering to inner construction to promote capacitance control and diffusion control energy storage.After that,the electrochemical performance of the FRTMCs regulation strategies in AMIBs is reviewed.Finally,we present potential directions and challenges for further enhancements of FRTMCs in AMIBs.The review aims to provide an in-depth understanding of regulation strategies for enhancing energy storage to build high-performance AMIBs that meet practical applications.展开更多
Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed di...Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt.Here,we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.Methods:An endoscopic images-based nasopharyngeal malignancy detection model(eNPM-DM)consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation.Briefly,a total of 28,966 qualified images were collected.Among these images,27,536 biopsy-proven images from 7951 individuals obtained from January 1st,2008,to December 31st,2016,were split into the training,validation and test sets at a ratio of 7:1:2 using simple randomiza-tion.Additionally,1430 images obtained from January 1st,2017,to March 31st,2017,were used as a prospective test set to compare the performance of the established model against oncologist evaluation.The dice similarity coef-ficient(DSC)was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images,by comparing automatic segmentation with manual segmenta-tion performed by the experts.Results:All images were histopathologically confirmed,and included 5713(19.7%)normal control,19,107(66.0%)nasopharyngeal carcinoma(NPC),335(1.2%)NPC and 3811(13.2%)benign diseases.The eNPM-DM attained an overall accuracy of 88.7%(95%confidence interval(CI)87.8%-89.5%)in detecting malignancies in the test set.In the prospective comparison phase,eNPM-DM outperformed the experts:the overall accuracy was 88.0%(95%CI 86.1%-89.6%)vs.80.5%(95%CI 77.0%-84.0%).The eNPM-DM required less time(40 s vs.110.0±5.8 min)and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background,with an average DSC of 0.78±0.24 and 0.75±0.26 in the test and prospective test sets,respectively.Conclusions:The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant,and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52071171,22109060)the Liaoning Revitalization Talents Program-Pan Deng Scholars(XLYC1802005)+5 种基金the Liaoning Bai Qian Wan Talents Program(LNBQW2018B0048)the Natural Science Fund of Liaoning Province for Excellent Young Scholars(2019-YQ-04)the Key Project of Scientific Research of the Education Department of Liaoning Province(LZD201902)the Research Fund for the Doctoral Program of Liaoning Province(2020-BS-085)the Australian Research Council(ARC)Future Fellowship(FT210100298)the CSIRO Energy Centre。
文摘In recent years,a series of aqueous metal ion batteries(AMIBs)has been developed to improve the safety and cost-efficiency of portable electronics and electric vehicles.However,the significant gaps in energy density,power density,and cycle stability of AMIBs directly hinder them from replacing the currently widely used non-aqueous metal ion batteries,which stems from the lack of reasonable configuration and performance optimization of electrode materials.First-row transition metal compounds(FRTMCs),with the advantages of optional voltage ranges(from low to high),adjustable crystal structures(layered and tunnel types with large spacing),and designable morphology(multi-dimensional nanostructures),are widely used to construct high-performance AMIBs.However,no comprehensive review papers were generated to highlight their specific and significant roles in AMIBs.In this review,we first summarize the superiority and characteristics of FRTMCs in AMIBs.Then,we put forward control strategies of FRTMCs from subsurface engineering to inner construction to promote capacitance control and diffusion control energy storage.After that,the electrochemical performance of the FRTMCs regulation strategies in AMIBs is reviewed.Finally,we present potential directions and challenges for further enhancements of FRTMCs in AMIBs.The review aims to provide an in-depth understanding of regulation strategies for enhancing energy storage to build high-performance AMIBs that meet practical applications.
基金supported by the National Natural Science Foundation of China[Grant Nos.81572665,81672680,81472525,81702873]the International Cooperation Project of Science and Technology Plan of Guangdong Province[Grant No.2016A050502011]the Health&Medical Collaborative Innovation Project of Guangzhou City,China(Grant No.201604020003).
文摘Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt.Here,we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.Methods:An endoscopic images-based nasopharyngeal malignancy detection model(eNPM-DM)consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation.Briefly,a total of 28,966 qualified images were collected.Among these images,27,536 biopsy-proven images from 7951 individuals obtained from January 1st,2008,to December 31st,2016,were split into the training,validation and test sets at a ratio of 7:1:2 using simple randomiza-tion.Additionally,1430 images obtained from January 1st,2017,to March 31st,2017,were used as a prospective test set to compare the performance of the established model against oncologist evaluation.The dice similarity coef-ficient(DSC)was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images,by comparing automatic segmentation with manual segmenta-tion performed by the experts.Results:All images were histopathologically confirmed,and included 5713(19.7%)normal control,19,107(66.0%)nasopharyngeal carcinoma(NPC),335(1.2%)NPC and 3811(13.2%)benign diseases.The eNPM-DM attained an overall accuracy of 88.7%(95%confidence interval(CI)87.8%-89.5%)in detecting malignancies in the test set.In the prospective comparison phase,eNPM-DM outperformed the experts:the overall accuracy was 88.0%(95%CI 86.1%-89.6%)vs.80.5%(95%CI 77.0%-84.0%).The eNPM-DM required less time(40 s vs.110.0±5.8 min)and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background,with an average DSC of 0.78±0.24 and 0.75±0.26 in the test and prospective test sets,respectively.Conclusions:The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant,and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.
