We report on the efficient removal of heavy metal ions from simulated wastewater with a nanostructured assembly. The nanoassembly was obtained via direct assembling the performed anisotropic layered double hydroxide nanocrystals (LDH-NCs) onto the surface of carbon nanospheres (labeled as LDH-NCs@CNs). It was found that the maximum adsorption capacity of the nanoassembly toward Cu(2+) was ∼ 19.93 mg g(-1) when the initial Cu(2+) concentration was 10.0 mg L(-1), displaying a high efficiency for the removal of heavy metal ions. The Freundlich adsorption isotherm was applicable to describe the removal processes. Kinetics of the Cu(2+) removal was found to follow pseudo-second-order rate equation. Furthermore, the as-prepared building unit of the assembly, including LDH-NCs, CNs, and the assembly, as well as Cu(2+)-adsorbed assembly, were carefully examined by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), nitrogen sorption measurements, and X-ray photoelectron spectroscopy (XPS). Based on the characterization results, a possible mechanism of Cu(2+) removal with the assembly of LDH-NCs@CNs was proposed. Comparison experiments show that the adsorption capacity of the resulting LDH-NCs@CNs assembly was much higher than its any building unit alone (CNs or LDH-NCs), exhibiting the deliberation of the assembly on water decontamination. This work provides a very efficient, fast and convenient approach for exploring promising nanoassembly materials for water treatment.

We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a nonrecommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies. 1 * This work was done at Baidu.

Electroactive biofilms play essential roles in determining the power output of microbial fuel cells (MFCs). To engineer the electroactive biofilm formation of Shewanella oneidensis MR-1, a model exoelectrogen, we herein heterologously overexpressed a c-di-GMP biosynthesis gene ydeH in S. oneidensis MR-1, constructing a mutant strain in which the expression of ydeH is under the control of IPTG-inducible promoter, and a strain in which ydeH is under the control of a constitutive promoter. Such engineered Shewanella strains had significantly enhanced biofilm formation and bioelectricity generation. The MFCs inoculated with these engineered strains accomplished a maximum power density of 167.6 ± 3.6 mW/m(2) , which was ∼ 2.8 times of that achieved by the wild-type MR-1 (61.0 ± 1.9 mW/m(2) ). In addition, the engineered strains in the bioelectrochemical system at poised potential of 0.2 V vs. saturated calomel electrode (SCE) generated a stable current density of 1100 mA/m(2) , ∼ 3.4 times of that by wild-type MR-1 (320 mA/m(2) ).

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