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Corn stover could be pyrolysed to prepare biochar for removing pollutants in water and realizing the resource utilization of biomass. The aims of the present study were to investigate the optimal preparation and adsorption conditions of biochar and to reveal the adsorption characteristics and mechanisms of Cd2+ in water by biochar. For this purpose, with Cd2+ as the target pollutant, the pyrolysis conditions involved in the pyrolysis temperature, retention time, and heating rate were evaluated and optimized. Additionally, the characteristics, mechanisms and optimal adsorption conditions of Cd2+ by biochar were determined. A series of characterization techniques was employed, including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and specific surface area analysis (SBET). The optimum pyrolysis parameters were a pyrolysis temperature of 700 °C, a retention time of 2.5 h, and a heating rate of 5 °C/min. Acid/base modification did not improve the adsorption capacity of biochar. The Langmuir and the Elovich model were the most suitable isotherm and kinetic models for equilibrium data, respectively. The maximum adsorption capacity fitted by Langmuir model was 13.4 mg/g. Furthermore, mineral precipitation and π electron interactions were shown to be the main adsorption mechanisms of Cd2+. The optimum adsorption conditions for Cd2+ in water were a CaCl2 electrolyte solution of 0.01 mol/L, a pH level of 6.7, and a biochar dosage of 0.4 g. Our results indicated that corn stover biochar was an appropriate approach for improving the status of water with Cd2+ contamination in the short term and for promoting a new perspective for the rational utilization of corn stover and the low-cost pollution control of heavy metals in water. [ABSTRACT FROM AUTHOR]

Gestational diabetes mellitus (GDM) is conventionally confirmed with oral glucose tolerance test (OGTT) in 24 to 28 weeks of gestation, but it is still uncertain whether it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy. To this purpose, the cost-sensitive hybrid model (CSHM) and five conventional machine learning methods are used to construct the predictive models, capturing the future risks of GDM in the temporally aggregated EHRs. The experimental data sources from a nested case-control study cohort, containing 33,935 gestational women in West China Second Hospital. After data cleaning, 4,378 cases and 50 attributes are stored and collected for the data set. Through selecting the most feasible method, the cost parameter of CSHM is adapted to deal with imbalance of the dataset. In the experiment, 3940 samples are used for training and the rest 438 samples for testing. Although the accuracy of positive samples is barely acceptable (62.16%), the results suggest that the vast majority (98.4%) of those predicted positive instances are real positives. To our knowledge, this is the first study to apply machine learning models with EHRs to predict GDM, which will facilitate personalized medicine in maternal health management in the future.