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Metal–organic gels (MOG) as new types of soft materials have shown promising applications in various fields such as chemosensors, environmental remediation, and gas adsorption/separation, owing to their high porosity, low density, and high surface area. However, the application of MOG materials in energy electrocatalysis and the active components made from them are rarely perceived. Herein, a new electrochemistry-driven reconstruction strategy to synthesize the NiOOH/FeOOH heterostructure from MOG materials is reported. The reconstructed NiOOH/FeOOH exhibits superior oxygen evolution reaction activity and excellent stability, owing to the synergistic effect of bimetallic centers, the abundant interface between NiOOH and FeOOH, and the plentiful defects. Impressively, the activated Re–FeNi–MOG-4 electrocatalyst displays remarkable catalytic activity with a low overpotential of 220 mV at a current density of 10 mA cm−2 and a small Tafel slope of 48 mV dec−1 in alkaline electrolyte, outperforming most recently reported electrocatalysts. Herein, a facile and effective electrochemical reconstruction engineering of pre-catalysts is provided and the evolution of self-reconstruction of MOG materials for accelerating the kinetics of the electrocatalytic process is highlighted.

Vivianite crystallization has been regarded as a suitable option for recovering phosphorus (P) from P-containing wastewater. However, the presence of humic substances (HS) would inevitably affect the formation of vivianite crystals. Therefore, the influences of HS on vivianite crystallization and the changes in the harvested vivianite crystals were investigated in this study. The results suggested the inhibition effect of 70 mg/L HS on vivianite crystallization reached 12.24%, while it could be attenuated by increasing the pH and Fe/P ratio of the solution. Meanwhile, the addition of HS altered the size, purity, and morphology of recovered vivianite crystals due to the blockage of the growth sites on the crystal surface. Additionally, the formation of phosphate ester group, hydrogen bonding, and COOH-Fe2+ complexes are the potential mechanisms of HS interaction with vivianite crystals. The results obtained herein will help to elucidate the underlying mechanism of HS on vivianite crystallization from P-containing wastewater.

Recovery of phosphorus (P) via vivianite crystallization is an effective strategy to recycle resources from the anaerobic fermentation supernatant. However, the presence of different components in the anaerobic fermentation supernatant (e.g., polysaccharides and proteins) might alter conditions for optimal growth of vivianite crystals, resulting in distinct vivianite characteristics. In the present study, the effect of different components on vivianite crystallization was explored. Then, the reaction parameters (pH, Fe/P, and stirring speed) for P recovery from synthetic anaerobic fermentation supernatant as vivianite were optimized using response surface methodology, and the relationship between crystal properties and supersaturation was elucidated using a thermodynamic equilibrium model. The optimized values for pH, Fe/P, and stirring speed were found to be 7.8, 1.74, and 500 rpm respectively, resulting in 90.54 % P recovery efficiency. Moreover, the variation of reaction parameters did not change the crystalline structure of the recovered vivianite but influenced its morphology, size, and purity. Thermodynamic analysis suggested the saturation index (SI) of vivianite increased with increasing pH and Fe/P ratio, leading to a facilitative effect on vivianite crystallization. However, when the SI was >11, homogenous nucleation occurred so that the nucleation rate was much higher than the crystal growth rate, causing a smaller crystal size. The findings presented herein will be highly valued for the future large-scale application of the vivianite crystallization process for wastewater treatment.

The enrichment of polyhydroxyalkanoates (PHA) accumulating bacteria (PAB) in mixed microbial cultures (MMC) is extremely difficult to be predicted and optimized. Here we demonstrate that mechanistic and deep learning models can be integrated innovatively to accurately predict the dynamic enrichment of PAB. Well-calibrated activated sludge models (ASM) of the PAB enrichment process provide time-dependent data under different operating conditions. Recurrent neural network (RNN) models are trained and tested based on the time-dependent dataset generated by ASM. The accurate prediction performance is achieved (R2 > 0.991) for three different PAB enrichment datasets by the optimized RNN model. The optimized RNN model can also predict the equilibrium concentration of PAB (R2 = 0.944) and corresponding time, which represents the end of the PAB enrichment process. This study demonstrates the strength of integrating mechanistic and deep learning models to predict long-term variations of specific microbes, helping to optimize their selection process for PHA production.

Twisted bilayer graphene (TBLG) is one of the simplest van der Waals heterostructures, yet it yields a complex electronic system with intricate interplay between moiré physics and interlayer hybridization effects. We report on electronic transport measurements of high mobility small angle TBLG devices showing clear evidence for insulating states at the superlattice band edges, with thermal activation gaps several times larger than theoretically predicted. Moreover, Shubnikov–de Haas oscillations and tight binding calculations reveal that the band structure consists of two intersecting Fermi contours whose crossing points are effectively unhybridized. We attribute this to exponentially suppressed interlayer hopping amplitudes for momentum transfers larger than the moiré wave vector., National Science Foundation (U.S.) (Grant DMR-1405221), Nature Society (Singapore), Gordon and Betty Moore Foundation (Grant GBMF4541), National Science Foundation (U.S.) (Harvard University. Materials Research Science and Engineering Center. Grant DMR-0819762), National Science Foundation (U.S.) (Harvard University. Center for Integrated Quantum Material. Grant ECS-0335765), National Science Foundation (U.S.) (STC Center for Integrated Quantum Materials. Grant DMR- 1231319), United States. Army Research Office. Multidisciplinary University Research Initiative (Award W911NF-14-0247)