Rechargeable aqueous zinc-metal batteries (AZMBs) are promising candidates for large-scale energy storage systems due to their low cost and high safety.However,their performance and sustainability are significantly hi...Rechargeable aqueous zinc-metal batteries (AZMBs) are promising candidates for large-scale energy storage systems due to their low cost and high safety.However,their performance and sustainability are significantly hindered by the sluggish desolvation kinetics at the electrode/electrolyte interface and the corresponding hydrogen evolution reaction where active water molecules tightly participate in the Zn(H_(2)O)_(6)^(2+)solvation shell.Herein,learnt from self-generated solid electrolyte interphase (SEI) in anodes,the dielectric but ion-conductive zinc niobate nanoparticles artificial layer is constructed on metallic Zn surface (ZNB@Zn),acting as a rapid desolvation promotor.The zincophilic and dielectric-conductive properties of ZNB layer accelerate interfacial desolvation/diffusion and suppress surface corrosion or dendrite formation,achieving uniform Zn plating/stripping behavior,as confirmed by electronic/optical microscopies and interface spectroscopical measurements together with theoretical calculations.Consequently,the as-prepared ZNB@Zn electrode exhibits excellent cycling stability of over 2000 h and robust reversibility (99.54%) even under high current density and depth of discharge conditions.Meanwhile,the assembled ZNB@Zn-based full cell displays high capacity-retention rate of 80.21%after 3000 cycles at 5 A g^(-1)and outstanding rate performance up to 10 A g^(-1).The large-areal pouch cell is stabilized for hundreds of cycles,highlighting the bright prospects of the dielectric but ion-conductive layer in further application of AZMBs.展开更多
Precise tuning of dielectric constants(Er)in oxide glasses is critical for highfrequency devices in 5G/6G systems,whereεp directly governs signal propagation efficiency.A machine learning framework combining data aug...Precise tuning of dielectric constants(Er)in oxide glasses is critical for highfrequency devices in 5G/6G systems,whereεp directly governs signal propagation efficiency.A machine learning framework combining data augmentation and physicochemical descriptor integration is developed to address data scarcity.Validated pseudo-labels are generated via ensemble learning,expanding the dataset from 1503 to 11,029 compositions without distributional shif.The XGBoost model trained on the augmented dataset achieved superior accuracy,with an R of 0.96 and an MSE of 0.14.For prediction tasks on unseen data,it reduced the error rate by 48%compared to the non-augmented model and improved generalization performance by 43%over GlassNet.B_(2)O_(3)and SiO_(2)are identified as E,suppressors and BaO and TiO_(2)as enhancers through SHAP analysis,aligning with network former/modifier roles.Cation-specific polarizabilities are derived via ClausiusMossotti regression(R^(2)=0.909).Integration of physicochemicaldescriptors(coordination number and bond strength)enables transferable predictionsfor Y_(2)O_(3)and La_(2)O_(3)containing glasses,with mean deviation 2.46%-4.76%.Crucially,structural descriptors dominate polarizability with 69.9%feature importance,establishing network engineering as the optimal design paradigm.A data-driven pathway for rational dielectric glass development is thus established.展开更多
A generalized theory of power-nonorthogonal coupled-modes in a lossless multi-waveguide structure is presented. A set of orthogonal eigenmodes in a multi-wave-guide structure with constant parameters is formed by comb...A generalized theory of power-nonorthogonal coupled-modes in a lossless multi-waveguide structure is presented. A set of orthogonal eigenmodes in a multi-wave-guide structure with constant parameters is formed by combining the nonorthogonal modes. In the waveguide structure with slowly varying parameters, however, the local modes formed by the same combination are still coupled. Finally, an example of two-waveguide coupler is given.展开更多
基金National Key R&D Program of China (2021YFA1201503)National Natural Science Foundation of China (21972164, 22279161, 12264038, 22309144)+4 种基金Natural Science Foundation of Jiangsu Province (BK. 20210130)China Postdoctoral Science Foundation (2023M733189)Jiangsu Double-Innovation PhD Program in 2022 (JSSCBS20221241)Senior Talents Fund of Jiangsu University (5501220014)fellowship funding provided by the Alexander von Humboldt Foundation。
文摘Rechargeable aqueous zinc-metal batteries (AZMBs) are promising candidates for large-scale energy storage systems due to their low cost and high safety.However,their performance and sustainability are significantly hindered by the sluggish desolvation kinetics at the electrode/electrolyte interface and the corresponding hydrogen evolution reaction where active water molecules tightly participate in the Zn(H_(2)O)_(6)^(2+)solvation shell.Herein,learnt from self-generated solid electrolyte interphase (SEI) in anodes,the dielectric but ion-conductive zinc niobate nanoparticles artificial layer is constructed on metallic Zn surface (ZNB@Zn),acting as a rapid desolvation promotor.The zincophilic and dielectric-conductive properties of ZNB layer accelerate interfacial desolvation/diffusion and suppress surface corrosion or dendrite formation,achieving uniform Zn plating/stripping behavior,as confirmed by electronic/optical microscopies and interface spectroscopical measurements together with theoretical calculations.Consequently,the as-prepared ZNB@Zn electrode exhibits excellent cycling stability of over 2000 h and robust reversibility (99.54%) even under high current density and depth of discharge conditions.Meanwhile,the assembled ZNB@Zn-based full cell displays high capacity-retention rate of 80.21%after 3000 cycles at 5 A g^(-1)and outstanding rate performance up to 10 A g^(-1).The large-areal pouch cell is stabilized for hundreds of cycles,highlighting the bright prospects of the dielectric but ion-conductive layer in further application of AZMBs.
基金the National Natural Science Foundation of China(No.52172019)Shandong Provincial Youth Innovation Team Development Plan of Colleges and Universities(No.2022KJ100).
文摘Precise tuning of dielectric constants(Er)in oxide glasses is critical for highfrequency devices in 5G/6G systems,whereεp directly governs signal propagation efficiency.A machine learning framework combining data augmentation and physicochemical descriptor integration is developed to address data scarcity.Validated pseudo-labels are generated via ensemble learning,expanding the dataset from 1503 to 11,029 compositions without distributional shif.The XGBoost model trained on the augmented dataset achieved superior accuracy,with an R of 0.96 and an MSE of 0.14.For prediction tasks on unseen data,it reduced the error rate by 48%compared to the non-augmented model and improved generalization performance by 43%over GlassNet.B_(2)O_(3)and SiO_(2)are identified as E,suppressors and BaO and TiO_(2)as enhancers through SHAP analysis,aligning with network former/modifier roles.Cation-specific polarizabilities are derived via ClausiusMossotti regression(R^(2)=0.909).Integration of physicochemicaldescriptors(coordination number and bond strength)enables transferable predictionsfor Y_(2)O_(3)and La_(2)O_(3)containing glasses,with mean deviation 2.46%-4.76%.Crucially,structural descriptors dominate polarizability with 69.9%feature importance,establishing network engineering as the optimal design paradigm.A data-driven pathway for rational dielectric glass development is thus established.
基金Project supported by the Science Foundation of China State Education Commission.
文摘A generalized theory of power-nonorthogonal coupled-modes in a lossless multi-waveguide structure is presented. A set of orthogonal eigenmodes in a multi-wave-guide structure with constant parameters is formed by combining the nonorthogonal modes. In the waveguide structure with slowly varying parameters, however, the local modes formed by the same combination are still coupled. Finally, an example of two-waveguide coupler is given.
