Search for: All records

Abstract This study reports the superior performance of graphene nanosheet (GNS) materials over Vulcan XC incorporated as a cathode catalyst in Li–O2 battery. The GNSs employed were synthesized from a novel, eco-friendly, and cost-effective technique involving chamber detonation of oxygen (O2) and acetylene (C2H2) precursors. Two GNS catalysts i.e., GNS-1 and GNS-2 fabricated with 0.3 and 0.5 O2/C2H2 precursor molar ratios, respectively, were utilized in this study. Specific surface area (SSA) analysis revealed significantly higher SSA and total pore volume for GNS-1 (180 m2 g−1, 0.505 cm3 g−1) as compared with GNS-2 (19 m2 g−1, 0.041 cm3 g−1). GNS-1 exhibited the highest discharge capacity (4.37 Ah g-1) and superior cycling stability compared with GNS-2 and Vulcan XC. Moreover, GNS-1 demonstrated promising performance at higher current densities (0.2 and 0.3 mA cm−2) and with various organic electrolytes. The superior performance of GNS-1 can be ascribed to its higher mesopore volume, SSA, and optimum wettability compared to its counterparts. 

Abstract The remarkable surge in energy demand has compelled the quest for high‐energy‐density battery systems. The Li–O₂battery (LOB) and Li–air battery (LAB), owing to their extremely high theoretical energy density, have attracted extensive research in the past two decades. The commercial development of LOB is hampered due to the numerous challenges its components present. Ionic liquids (ILs) are considered potential electrolyte solvents of LOBs and LABs due to their excellent electrochemical stability, thermal stability, non‐flammability, low flammability, and O₂solubility. In addition to electrolyte solvents, ILs also have other applications in LOB and LAB systems. This review reports the progress of IL‐based LOBs and LABs over the years since treported for the first time in 2005. The impact of the physiochemical properties of ILs on the performance of LOB and LAB at various operating conditions is thoroughly discussed. The various methodologies are also summarized that are employed to tune ILs’ physiochemical properties to render them more favorable for rechargeable lithium batteries. Tunable properties of ILs create the possibility of designing cost‐effective batteries with excellent safety, high energy density and high power density, and long‐term stability. 

Abstract Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administration and food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time steps ahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia. In this study, we present a novel multi-component deep learning model that predicts the BG levels in a multi-step look ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients. For the prediction horizon (PH) of 30 mins, the average values forroot mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE), andnormalized mean squared error(NRMSE) are$$23.22 \pm 6.39$$ $23.22\pm 6.39$mg/dL, 16.77 ± 4.87 mg/dL,$$12.84 \pm 3.68$$ $12.84\pm 3.68$and$$0.08 \pm 0.01$$ $0.08\pm 0.01$respectively. When Clarke and Parkes error grid analyses were performed comparing predicted BG with actual BG, the results showed average percentage of points in Zone A of$$80.17 \pm 9.20$$ $80.17\pm 9.20$and$$84.81 \pm 6.11,$$ $84.81\pm 6.11,$respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.