Lina Chi

Visible-light photocatalysis on C-doped ZnO derived from polymer-assisted pyrolysis†

C-doped ZnO with a large surface area was prepared via F127-assisted pyrolysis at 500 °C and used for visible-light-responsive photocatalytic water purification. The band structure of the C-doped ZnO was investigated using valance band XPS and DFT simulation. The C-doped ZnO possessed enhanced absorption of UV and visible light, though it showed lower visible-light-responsive photocatalytic activity than ZnO because of significant recombination of photogenerated charge carriers arising from overloaded C-dopant and oxygen vacancies.

Enhanced methane production from Taihu Lake blue algae by anaerobic co-digestion with corn straw in continuous feed digesters.

Anaerobic digestion of Taihu blue algae was tested in laboratory scale, continuous feed digesters (hydraulic retention time 10 days) at 35°C and various organic loading rates (OLR). The methane production and biomass digestion performed well at OLR below 4.00 gVSL(-1)d(-1) but deteriorated as OLR increased due to the increased ammonia concentration, causing inhibition mainly to acetate and propionate degradation. Supplementing corn straw as co-feedstock significantly improved the digestion performance. The optimal C/N ratio for the co-digestion was 20:1 at OLR of 6.00 gVSL(-1) d(-1). Methane yield of 234 mL CH4 gVS(-1) and methane productivity of 1404 mL CH4 L(-1) d(-1) were achieved with solid removal of 63%. Compared with the algae alone, the methane productivity was increased by 46% with less accumulation of ammonia and fatty acids. The reactor rate-limiting step was acetate and propionate degradation.

Investigation of soluble microbial products in a full-scale UASB reactor running at low organic loading rate

Investigation on a full-scale UASB treating industrial wastewater at a low organic loading rate (OLR) was conducted. Excellent treatment performance was achieved when treating the evaporator condensate of distillery wastewater at the OLR of less than 1 kg COD/m(3)d. Anaerobic effluent could be discharged without further treatment, which saved energy and running cost considerably. GC-MS analysis showed that the soluble microbial products (SMPs) were decreased to a low level at the low OLR. The main SMP in the anaerobic effluent were long chain carbohydrates and esters, accounting for 55-65% of the total organic matters. Anaerobic SMP was more complex than the aerobic ones. Soluble COD, protein and polysaccharide showed an obvious decrease at the sludge layer from 10 to 15m despite the low MLSS/MLVSS content. Methanogens were found to be predominant in this layer, which indicated that the methanogens might be the main consumers of the SMP in anaerobic reactors. Economic comparison confirmed that the anaerobic treatment at low OLR could be a good option.

Black N/H-TiO2 Nanoplates with a Flower-Like Hierarchical Architecture for Photocatalytic Hydrogen Evolution.

A facile two-step strategy was used to prepare black of hydrogenated/nitrogen-doped TiO2 nanoplates (NHTA) with a flower-like hierarchical architecture. In situ nitriding and self-assembly was realized by hydrothermal synthesis using tripolycyanamide as a N source and as a structure-directing agent. After thorough characterization, it was found that the hydrogenation treatment did not damage the flower-like architecture but distorted the anatase crystal structure and significantly changed the band structure of NHTA owing to the increased concentration of oxygen vacancies, hydroxyl groups, and Ti3+ cations. Under AM 1.5 illumination, the photocatalytic H2 evolution rate on the black NHTA was approximately 1500 μmol g-1  h-1 , which was much better than the N-doped TiO2 nanoplates (≈690 μmol g-1  h-1 ). This improvement in the hydrogen evolution rate was attributed to a reduced bandgap, enhanced separation of the photogenerated charge carriers, and an increase in the surface-active sites.

Modeling and optimizing the performance of PVC/PVB ultrafiltration membranes using supervised learning approaches

Mathematical models play an important role in performance prediction and optimization of ultrafiltration (UF) membranes fabricated via dry/wet phase inversion in an efficient and economical manner. In this study, a systematic approach, namely, a supervised, learning-based experimental data analytics framework, is developed to model and optimize the flux and rejection rate of poly(vinyl chloride) (PVC) and polyvinyl butyral (PVB) blend UF membranes. Four supervised learning (SL) approaches, namely, the multiple additive regression tree (MART), the neural network (NN), linear regression (LR), and the support vector machine (SVM), are employed in a rigorous fashion. The dependent variables representing membrane performance response with regard to independent variables representing fabrication conditions are systematically analyzed. By comparing the predicting indicators of the four SL methods, the NN model is found to be superior to the other SL models with training and testing R-squared values as high as 0.8897 and 0.6344, respectively, for the rejection rate, and 0.9175 and 0.8093, respectively, for the flux. The optimal combination of processing parameters and the most favorable flux and rejection rate for PVC/PVB ultrafiltration membranes are further predicted by the NN model and verified by experiments. We hope the approach is able to shed light on how to systematically analyze multi-objective optimization issues for fabrication conditions to obtain the desired ultrafiltration membrane performance based on complex experimental data characteristics.