Unlocking Bimetallic Active Centers via Heterostructure Engineering for Exceptional Phosphate Electrosorption: Internal Electric Field-Induced Electronic Structure Reconstruction.

Development of electrode materials exhibiting exceptional phosphate removal performance represents a promising strategy to mitigate eutrophication and meet ever-stricter stringent emission standards. Herein, we precisely designed a novel LaCeOx heterostructure-decorated hierarchical carbon composite (L8C2PC) for high-efficiency phosphate electrosorption. This approach establishes an internal electric field within the LaCeOx heterostructure, where the electrons transfer from Ce atoms to neighboring La atoms through superexchange interactions in La-O-Ce coordination units. The modulatory heterostructure endows a positive shift of the d band of La sites and the increase of electron density at Fermi level, promoting stronger orbital overlap and binding interactions. The introduction of oxygen vacancies during the in situ nucleation process reduces the kinetic barrier for phosphate-ion migration and supplies additional active centers. Moreover, the hierarchical carbon framework ensures electrical double-layer capacitance for phosphate storage and interconnected ion migration channels. Such synergistically multiple active centers grant the L8C2PC electrode with high-efficiency record in phosphate electrosorption. As expected, the L8C2PC electrode demonstrates the highest removal capability among the reported electrode materials with a saturation capacity of 401.31 mg P g-1 and a dynamic capacity of 91.83 mg P g-1 at 1.2 V. This electrochemical system also performs well in the dephosphorization in natural water samples with low concentration that enable effluent concentration to meet the first-class discharge standard for China (0.5 mg P L-1). This study advances efficient dephosphorization techniques to a new level and offers a deep understanding of the internal electric field that regulates metal orbitals and electron densities in heterostructure engineering.

A novel sustainable N recycling process: Upcycling ammonia to ammonium fertilizer from dilute wastewater and simultaneously realizing phenol degradation via a visible solar-driven PECMA system with efficient Ag2S-BiVO4 photoanodes.

The selective recovery of NH4+ as N fertilizers from dilution wastewater is a promising but challenging topic. Herein, a novel visible-light driven photo-electrochemical membrane stripping cell (designated "PECMA") with Ag2S-BiVO4 heterojunction photoanode was proposed to recover ammonium from dilute wastewater, which comprised an anode chamber for organics treatment, intermediate chamber for separating ammonium, cathode chamber for upcycling NH4+ into NH3, and recovery chamber for converting NH3 into (NH4)2SO4. The NH4+ is concentrated by 21.5 times and recovered as (NH4)2SO4 with a concentration of 7103 mg L-1 after 10 cycles. At a current density of 3.86 A m-2, PECMA system achieves excellent NH4+ removal and recovery rates of 97.5 and 37.2 g N m-2 d-1 in 100 mgN L-1 wastewater. Moreover, PECMA degrades refractory organic pollutants through ClO· generated by Ag2S-BiVO4 photoanode, which effectively decompose phenol to CO2 with a degradation rate of 93 %. Although tested as a proof-of-concept, the hybrid system opens up a novel field involving a sunlight-water-energy nexus, promising high efficiency NH4+ recovery and wastewater remediation.

mHealth Visual Discovery Dashboard.

We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do - in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.