Adsorption of COD and Ammonia Nitrogen in Landfill Leachate on Red Mud Modified by Sulfonation Coupling

Using polysodium p-styrene sulfonate and silane coupling agent (KH560) as modifiers, red mud was modified by sulfonation and coupling, and used in the adsorption treatment of landfill leachate. The effects of pH value, dosage of adsorbent, adsorption time and reaction temperature on the adsorption of COD and ammonia nitrogen were studied. The modified red mud was characterized by infrared spectroscopy and scanning electron microscope and the isotherm, kinetic and thermodynamic of adsorption were also discussed. The results showed that the adsorption effect of red mud modified by sulfonation coupling (SCRM) on COD and ammonia nitrogen in leachate was significantly improved. At the conditions of pH value 8, dosage of 60 g/L, adsorption time of 90 min and reaction temperature of 20 ℃, the equilibrium adsorption capacity of COD and ammonia nitrogen were 81.51 mg/g and 19.24 mg/g, and the removal rate reached 87.57% and 72.05%, respectively. The pore structure of red mud particles changed from shallow pores to penetrating pores, and obvious sulfonated characteristic groups appeared after modification. The adsorption of COD and ammonia nitrogen by SCRM belonged to monolayer adsorption and multilayer adsorption, respectively. The adsorption kinetics of the two adsorption processes were more consistent with the pseudo-second-order kinetic model, and both were endothermic and entropic spontaneous reaction processes

Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach

ObjectivesInflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF.MethodsPatients diagnosed with NIHF without infection during hospitalization were included. The primary outcome was defined as all-cause mortality and heart transplantations. We used elastic net Cox regression with cross-validation to select inflammatory biomarkers and construct the best biomarker risk score model. Discrimination, calibration, and reclassification were evaluated to assess the predictive value of the biomarker risk score.ResultsOf 1,250 patients included (median age, 53 years, 31.9% women), 436 patients (34.9%) experienced the primary outcome during a median of 2.8 years of follow-up. The final biomarker risk score included high-sensitivity C-reactive protein-to-albumin ratio (CAR) and red blood cell distribution width-standard deviation (RDW-SD), both of which were 100% selected in 1,000 times cross-validation folds. Incorporating the biomarker risk score into the best basic model improved the discrimination (ΔC-index = 0.012, 95% CI 0.003–0.018) and reclassification (IDI, 2.3%, 95% CI 0.7%–4.9%; NRI, 17.3% 95% CI 6.4%–32.3%) in risk identification. In the cross-validation sets, the mean time-dependent AUC ranged from 0.670 to 0.724 for the biomarker risk score and 0.705 to 0.804 for the basic model with a biomarker risk score, from 1 to 8 years. In multivariable Cox regression, the biomarker risk score was independently associated with the outcome in patients with NIHF (HR 1.76, 95% CI 1.49–2.08, p < 0.001, per 1 score increase).ConclusionsAn inflammatory biomarker-derived risk score significantly improved prognosis prediction and risk stratification, providing potential individualized therapeutic targets for NIHF patients

A unified model of Grignard reagent formation

Understanding the selectivity and kinetics: a unified ionic model of Grignard reagent formation.</p