In an acidic environment, Ni-ZIF/NC yielded a η10 of 177.4 mV and Tafel slope of 83.9 mV dec-1, which were comparable to those of 20 wt.% Pt/C. Moreover, Ni-ZIF/NC and Cu-ZIF/NC also exhibited superior stabilities in alkaline environments. This work offers a valuable strategy for controlling the morphology and implanting M-Nx active sites into carbon for designing novel catalysts for use in alternative new energy applications.We investigate the organisation of clay nanoplatelets within a hydrogel based on modified ionenes, cationic polyelectrolytes forming physically crosslinked hydrogels induced by hydrogen bonding and π-π stacking. Combination of small angle X-ray and neutron scattering (SAXS, SANS) reveals the structure of the polyelectrolyte network as well as the organisation of the clay additives. The clay-free hydrogel network features a characteristic mesh-size between 20 and 30 nm, depending on the polyelectrolyte concentration. Clay nanoplatelets inside the hydrogel organise in a regular face-to-face stacking manner, with a large repeat distance, following rather closely the hydrogel mesh-size. The presence of the nanoplatelets does not modify the hydrogel mesh size. Further, the clay-compensating counterions (Na+, Ca2+ or La3+) and the clay type (montmorillonite, beidellite) both have a significant influence on nanoplatelet organisation. The degree of nanoplatelet ordering in the hydrogel is very sensitive to the negative charge location on the clay platelet (different for each clay type). Increased nanoplatelet ordering leads to an improvement of the elastic properties of the hydrogel. On the contrary, the presence of dense clay aggregates (tactoids), induced by multi-valent clay counterions, destroys the hydrogel network as seen by the reduction of the elastic modulus of the hydrogel.A clear understanding of the crystal formation pathways of zeolites remains one of the most challenging issues to date. Here we investigate the synthesis of nanosized chabazite (CHA) zeolites using organic template-free colloidal suspensions by varying the time of aging at room temperature and the time of hydrothermal treatment at 90 °C. The role of mixed alkali metal cations (Na+, K+, Cs+) on the formation of CHA in the colloidal suspensions was studied. Increasing the aging time of the precursor colloidal suspension from 4 to 17 days resulted in faster crystallization of CHA nanocrystals (3 h instead of 7 h at 90 °C) to afford significantly smaller particles (60 nm vs 600 nm). During the crystallization a considerable change in the content of inorganic cations in the recovered solid material was observed to coincide with the formation of the CHA nanocrystals. The Na+ cations were found to direct the formation of condensed and pre-shaped aluminosilicate particles in the colloidal precursor suspensions, while K+ cations facilitated the formation of secondary building units (SBUs) of the CHA type framework structure such as d6r and cha cages, and the Cs+ cations promoted the long-range crystalline order facilitating the crystallization of stable zeolite nanocrystals.Mechanistic and data-driven models have been developed to provide predictive insights into the design and optimization of engineered bioprocesses. These two modeling strategies can be combined to form hybrid models to address the issues of parameter identifiability and prediction interpretability. Herein, we developed a novel and robust hybrid modeling strategy by incorporating microbial population dynamics into model construction. The hybrid model was constructed using bioelectrochemical systems (BES) as a platform system. We collected 77 samples from 13 publications, in which the BES were operated under diverse conditions, and performed holistic processing of the 16S rRNA amplicon sequencing data. Community analysis revealed core populations composed of putative electroactive taxa Geobacter, Desulfovibrio, Pseudomonas, and Acinetobacter. Primary Bayesian networks were trained with the core populations and environmental parameters, and directed Bayesian networks were trained by defining the operating parameters to improve the prediction interpretability. Both networks were validated with Bray-Curtis similarly, relative root-mean-square error (RMSE), and a null model. A hybrid model was developed by first building a three-population mechanistic component and subsequently feeding the estimated microbial kinetic parameters into network training. The hybrid model generated a simulated community that shared a Bray-Curtis similarity of 72% with the actual microbial community at the genus level and an average relative RMSE of 7% for individual taxa. When examined with additional samples that were not included in network training, the hybrid model achieved accurate prediction of current production with a relative error-based RMSE of 0.8 and outperformed the data-driven models. The genomics-enabled hybrid modeling strategy represents a significant step toward robust simulation of a variety of engineered bioprocesses.High water turbidity in aquatic ecosystems is a global challenge due to its harmful impacts. A cost-effective manner to rapidly and accurately measure water turbidity is thus of particular useful in water management with limited resources. This study developed a novel framework aiming to predict water turbidity in various aquatic ecosystems. The framework predicted water turbidity and quantified the uncertainty of the prediction through Bayesian modeling. To improve model performance, a model-update method was implemented in the framework to update the model structure and parameters once more measured data were available. 120 paired records (an image from smartphone and a measured water turbidity value by standard turbidimeters for each record) were collected from rivers, lakes and ponds across China to evaluate the performance of the developed framework. Our cross-validation results revealed a well prediction of water turbidity with Nash-Sutcliffe efficiency (NS) >0.87 (p0.73 (p less then 0.001) during the validation period. https://www.selleckchem.com/products/abt-199.html The model-update method (in case of more measured data) for the developed Bayesian models in the framework resulted in a decreasing trend of model uncertainty and a stable mode fit. This study demonstrated a high value of the Bayesian-based framework in predicting water turbidity in a robust and easy manner.