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Graphene-based electrodes have been extensively investigated for supercapacitor applications. However, their ion diffusion efficiency is often hindered by the graphene restacking phenomenon. Even though holey graphene is fabricated to address this issue by providing ion transport channels, those channels could still be blocked by densely stacked graphene nanosheets. To tackle this challenge, this research aims at improving the ion diffusion efficiency of microwave-synthesized holey graphene films by tuning the water interlayer spacer towards the improved supercapacitor performance. By controlling the vacuum filtration during graphene-based electrode fabrication, we obtain dry films with dense packing and wet films with sparse packing. The SEM images reveal that 20 times larger interlayer distance is constructed in the wet film compared to that in the dry counterpart. The holey graphene wet film delivers a specific capacitance of 239 F/g, ~82% enhancement over the dry film (131 F/g). By an integrated experimental and computational study, we quantitatively show that the interlayer spacing in combination with the nanoholes in the basal plane dominates the ion diffusion rate in holey graphene-based electrodes. Our study concludes that novel hierarchical structures should be further considered even in holey graphene thin films to fully exploit the superior advantages of graphene-based supercapacitors.

CO₂electroreduction (CO₂R) operating in acidic media circumvents the problems of carbonate formation and CO₂crossover in neutral/alkaline electrolyzers. Alkali cations have been universally recognized as indispensable components for acidic CO₂R, while they cause the inevitable issue of salt precipitation. It is therefore desirable to realize alkali‐cation‐free CO₂R in pure acid. However, without alkali cations, stabilizing *CO₂intermediates by catalyst itself at the acidic interface poses as a challenge. Herein, we first demonstrate that a carbon nanotube‐supported molecularly dispersed cobalt phthalocyanine (CoPc@CNT) catalyst provides the Co single‐atom active site with energetically localizeddstates to strengthen the adsorbate‐surface interactions, which stabilizes *CO₂intermediates at the acidic interface (pH=1). As a result, we realize CO₂conversion to CO in pure acid with a faradaic efficiency of 60 % at pH=2 in flow cell. Furthermore, CO₂is successfully converted in cation exchanged membrane‐based electrode assembly with a faradaic efficiency of 73 %. For CoPc@CNT, acidic conditions also promote the intrinsic activity of CO₂R compared to alkaline conditions, since the potential‐limiting step, *CO₂to *COOH, is pH‐dependent. This work provides a new understanding for the stabilization of reaction intermediates and facilitates the designs of catalysts and devices for acidic CO₂R.

One of the key ideas for reducing downlink channel acquisition overhead for FDD massive MIMO systems is to exploit a combination of two assumptions: (i) the dimension of channel models in propagation domain may be much smaller than the next-generation base-station array sizes (e.g., 64 or more antennas), and (ii) uplink and downlink channels may share the same low-dimensional propagation domain. Our channel measurements demonstrate that the two assumptions may not always hold, thereby impacting the predicted performance of methods that rely on the above assumptions. In this paper, we analyze the error in modeling the downlink channel using uplink measurements, caused by the mismatch from the above two assumptions. We investigate how modeling error varies with base-station array size and provide both numerical and experimental results. We observe that modeling error increases with the number of base-station antennas, and channels with larger angular spreads have larger modeling error. Utilizing our modeling error analysis, we then investigate the resulting beamforming performance rate loss. Accordingly, we observe that the rate loss increases with the number of base-station antennas, and channels with larger angular spreads suffer from higher rate loss.

The quantitative description of correlated electron materials remains a modern computational challenge. We demonstrate a numerical strategy to simulate correlated materials at the fully ab initio level beyond the solution of effective low-energy models and apply it to gain a detailed microscopic understanding across a family of cuprate superconducting materials in their parent undoped states. We uncover microscopic trends in the electron correlations and reveal the link between the material composition and magnetic energy scales through a many-body picture of excitation processes involving the buffer layers. Our work illustrates a path toward a quantitative and reliable understanding of more complex states of correlated materials at the ab initio many-body level.