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Previous research on iron oxides/hydroxides has focused on the crystalline rather than the amorphous phase, despite that the latter could have superior electrochemical activity due to the disordered structure. In this work, a simple and scalable synthesis route is developed to prepare amorphous FeOOH quantum dots (QDs) and FeOOH QDs/graphene hybrid nanosheets. The hybrid nanosheets possess a unique heterostructure, comprising a continuous mesoporous FeOOH nanofilm tightly anchored on the graphene surface. The amorphous FeOOH/graphene hybrid nanosheets exhibit superior pseudocapacitive performance, which largely outperforms the crystalline iron oxides/hydroxides‐based materials. In the voltage range between −0.8 and 0 V versus Ag/AgCl, the amorphous FeOOH/graphene composite electrode exhibits a large specific capacitance of about 365 F g−1, outstanding cycle performance (89.7% capacitance retention after 20 000 cycles), and excellent rate capability (189 F g−1 at a current density of 128 A g−1). When the lower cutoff voltage is extended to −1.0 and −1.25 V, the specific capacitance of the amorphous FeOOH/graphene composite electrode can be increased to 403 and 1243 F g−1, respectively, which, however, compromises the rate capability and cycle performance. This work brings new opportunities to design high‐performance electrode materials for supercapacitors, especially for amorphous oxides/hydroxides‐based materials.

Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multiphase flow problems is hindered by the curse of dimensionality, the saturation discontinuity due to capillarity effects, and the time dependence of the multi‐output responses. In this paper, we propose a deep convolutional encoder‐decoder neural network methodology to tackle these issues. The surrogate modeling task is transformed to an image‐to‐image regression strategy. This approach extracts high‐level coarse features from the high‐dimensional input permeability images using an encoder and then refines the coarse features to provide the output pressure/saturation images through a decoder. A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field. To characterize the high‐dimensional time‐dependent outputs of the dynamic system, time is treated as an additional input to the network that is trained using pairs of input realizations and of the corresponding system outputs at a limited number of time instances. The proposed method is evaluated using a geological carbon storage process‐based multiphase flow model with a 2,500‐dimensional stochastic permeability field. With a relatively small number of training data, the surrogate model is capable of accurately characterizing the spatiotemporal evolution of the pressure and discontinuous CO2 saturation fields and can be used efficiently to compute the statistics of the system responses.

Here we proposed a novel architectural design of a ternary MnO2-based electrode – a hierarchical Co3O4@Pt@MnO2 core-shell-shell structure, where the complemental features of the three key components (a well-defined Co3O4 nanowire array on the conductive Ti substrate, an ultrathin layer of small Pt nanoparticles, and a thin layer of MnO2 nanoflakes) are strategically combined into a single entity to synergize and construct a high-performance electrode for supercapacitors. Owing to the high conductivity of the well-defined Co3O4 nanowire arrays, in which the conductivity was further enhanced by a thin metal (Pt) coating layer, in combination with the large surface area provided by the small MnO2 nanoflakes, the as-fabricated Co3O4@Pt@MnO2 nanowire arrays have exhibited high specific capacitances, good rate capability, and excellent cycling stability. The architectural design demonstrated in this study provides a new approach to fabricate high-performance MnO2–based nanowire arrays for constructing next-generation supercapacitors.