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For lithium-ion capacitors, electrode potential tuning has been widely regarded as an efficient technology to improve their performance where the anode is usually pre-lithiated for desired potentials. However, the attention on the cathode is quite rare which denies the further excavation of this promising strategy. Herein, we reported the precious regulation of the potential of a cathode made of nitrogen-doped carbon nanosheets to fully leverage the merit of the electrode materials and the electrolyte potential window. Specifically, the nitrogen-doped carbon nanosheets in the optimized potential range of 1.6–4.5 V vs Li/Li+ show a specific capacity of around 150 mA h g−1 at 0.2 A g−1, which is four-time higher than that of the typical activated carbon cathode without potential tuning, and could be stably cycled for over 10 000 cycles without obvious decay. After pairing with a pre-lithiated anode, the as-assembled lithium-ion capacitors based on such a cathode can lead to simultaneously optimize the specific capacitance and voltage window of the device that contributes to a 17-fold increase in energy density, large power density, and long-term stability. The simple and effective strategy demonstrated here may inspire an alternative avenue to regulate the real performance of hybrid energy storage devices. The elaborately regulated carbon cathode is capable of working in a reasonable potential window for stably delivering a large specific capacity. After carefully pairing with the pre-lithiated anode, the device based on these electrodes exhibits high energy, large power, and long cyclic life. [Display omitted] [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing has stood out as a reliable technology to construct carbon microlattice electrodes for supercapacitors (SCs) in the field of custom areal electrochemical performance needed. The complex structural parameters of 3D-printed (3DP) electrodes make customization of the 3DP electrodes low efficient and time-consuming. Herein, we integrate machine learning (ML) to deeply unravel the influence of typical structural parameters of 3DP electrodes made of graphene and carbon nanotubes (CNTs). The dependence of areal performance on electrode structures was established through selecting only 9 experimental points combined with random forest (RF) algorithm, where the valuable information and the hidden correlations were quickly extracted. By using the as-established model, the electrodes with desired performance could be printed based on structural parameters directly selected from the model, offering an essentially improved performance for target performance. Specifically, the areal performance could be tuned from 0.032 to 1.6 F cm−2, covering an extremely large range. Moreover, the electrochemical surface area (ECSA) and finite element analysis (FEA) were employed to analyze the dependence of areal performance on structural parameters, agreeing well with the model information. The idea proposed in this work could largely increase the efficiency of developing new electrode architectures for desired performance. [Display omitted] A machine learning framework is established to develop a prediction model between structural parameters and areal capacitance of 3DP electrodes. The well-fitted machine learning model constructed by random forest indicated that the feature importance is geometric area > electrode thickness > gap distance. Based on this model, the customization of areal capacitance could be realized, fulfilling the diverse requirements of actual applications. • 3D printing exhibits huge advantages in fabricating customized supercapacitors. • Machine learning model is established to guide 3D printing supercapacitors with desired performance. • Structural parameters influence areal capacitance by regulating electrochemical surface area and current distribution. [ABSTRACT FROM AUTHOR]