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. The NH2-modified Si NPs first form strong hydrogen bonds and covalent bonds with GO. The Si@NH2/GO composite further forms hydrogen bonds and covalent bonds with sodium alginate, which acts as a binder, to yield a stable composite negative electrode. These two chemical cross-linked/hydrogen bonding interactions-one between NH2-modified Si NPs and GO, and another between the GO and sodium alginate-along with highly mechanically flexible graphene oxide, produced a robust network in the negative electrode system to stabilize the electrode during discharge and charge cycles. The as-prepared Si@NH2/GO electrode exhibits an outstanding capacity retention capability and good rate performance, delivering a reversible capacity of 1000 mAh g(-1) after 400 cycles at a current of 420 mA g(-1) with almost 100% capacity retention. The results indicated the importance of system-level strategy for fabricating stable electrodes with improved electrochemical performance.

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. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality.