The superior cycle and rate performance of a novel sulfur cathode by immobilizing sulfur into porous N-doped carbon microspheres.

A novel composite of sulfur immobilized into porous N-doped carbon microspheres (NCMSs-S) was synthesized. This composite cathode for lithium-sulfur batteries delivers a high specific capacity and superior rate capability and cycle stability, with a reversible capacity of ~605 mA h g(-1) at 2 C and 85% capacity retention after 500 cycles.

Graphene oxide-immobilized NH₂-terminated silicon nanoparticles by cross-linked interactions for highly stable silicon negative electrodes.

There is a great interest in the utilization of silicon-based anodes for lithium-ion batteries. However, its poor cycling stability, which is caused by a dramatic volume change during lithium-ion intercalation, and intrinsic low electric conductivity hamper its industrial applications. A facile strategy is reported here to fabricate graphene oxide-immobilized NH2-terminated silicon nanoparticles (NPs) negative electrode (Si@NH2/GO) directed by hydrogen bonding and cross-linked interactions to enhance the capacity retention of the anode.

Wastewater quality monitoring system using sensor fusion and machine learning techniques.

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables.