Enhanced removal of sulfonamide antibiotics from water by phosphogypsum modified biochar composite.

Antibiotic pollution has become a global eco-environmental issue. To reduce sulfonamide antibiotics in water and improve resource utilization of solid wastes, phosphogypsum modified biochar composite (PMBC) was prepared via facile one-step from distillers grains, wood chips, and phosphogypsum. The physicochemical properties of PMBC were characterized by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), Zeta potential, X-ray diffraction (XRD), etc. The influencing factors, adsorption behaviors, and mechanisms of sulfadiazine (SD) and sulfamethazine (SMT) onto PMBC were studied by batch and fixed bed column adsorption experiments. The results showed that the removal rates of SD and SMT increased with the increase of phosphogypsum proportion, while decreased with the increase of solution pH. The maximum adsorption capacities of modified distillers grain and wood chips biochars for SD were 2.98 and 4.18 mg/g, and for SMT were 4.40 and 8.91 mg/g, respectively, which was 9.0-22.3 times that of pristine biochar. Fixed bed column results demonstrated that PMBC had good adsorption capacities for SD and SMT. When the solution flow rate was 2.0 mL/min and the dosage of PMBC was 5.0 g, the removal rates of SD and SMT by modified wood chips biochar were both higher than 50% in 4 hr. The main mechanisms of SD and SMT removal by PMBC are hydrogen bonding, π-π donor-acceptor, electrostatic interaction, and hydrophobic interaction. This study provides an effective method for the removal of antibiotics in water and the resource utilization of phosphogypsum.

Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic.

Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.