ABSTRACT
Rechargeable aqueous zinc-ion batteries (RAZIBs) are regarded as competitive alternatives for large-scale energy storage on account of cost-effectiveness and inherent safety. In particular, rechargeable Zn-MnO2 batteries have drawn increasing attention due to high manufacturing readiness level. However, obtaining MnO2 with high electrochemical activity and high cyclic stability toward Zn2+/H+ storage still remains challenging. Herein, we reveal that incorporating yttrium ions (Y3+) into layered MnO2 can regulate the electronic structure of the MnO2 cathode by narrowing its band gap (from 3.25 to 2.50 eV), thus boosting the electrochemical performance in RAZIBs. Taking advantage of this feature, the optimized Y-MnO2 (YMO) sample exhibits greater capacity (212 vs. 152 mA h g-1 at 0.5 A g-1), better rate capability (94 vs. 61 mA h g-1 at 8 A g-1), reduced charge-transfer resistance (79 vs. 148 Ω), and promoted mass transfer kinetics (3.13 × 10-11vs. 2.37 × 10-11 cm2 s-1) in comparison with Y-free MnO2 (MO). More importantly, compared to MO, YMO-0.1 exhibits enhanced energy storage capability by nearly 40% (309 vs. 222 W h kg-1) and stable cycle performance (94 vs. 52 mA h g-1 after 3000 cycles). In situ Raman microscopy further reveals that the presence of Y3+ endows MnO2 with remarkable electrochemical reversibility during charge/discharge processes. This work highlights the importance of the Y3+ preintercalation strategy, which can be further developed to obtain better cathode materials for aqueous batteries.
ABSTRACT
Rechargeable aqueous zinc-ion batteries (RAZIBs) show great potential as a competitive candidate for reliable energy storage by virtue of cost-effectiveness, high safety, and environmental friendliness. However, unsatisfactory cycle stability of cathode material impedes the development of high-performance RAZIBs. This study reveals a strategic polyol-mediated process by using glycerol as the solvent for solvothermal reaction. After heat treatment in air, Mn-deficient Mn3O4 spinel (D-Mn3O4) can be obtained with rich Mn valence states (Mn2+/Mn3+/Mn4+), expanded crystal structure, high surface area, and good electrolyte compatability. Compared to well-crystallized Mn3O4, the presence of manganese vacancies in D-Mn3O4 enables lower charge-transfer resistance (86.0 vs 196.5 Ω), reduced activation energy for ion insertion (30.9 vs 50.4 kJ mol-1), and boosted solid-state ion diffusivity (9.45 × 10-12 vs 4.61 × 10-14 cm2 s-1). Therefore, D-Mn3O4 exhibits promising electrochemical performance with high capacity (284 mAh g-1), high specific energy (388.5 Wh kg-1) and stable cycle retention (87% after 200 cyclesat 0.3 A g-1). On the contrary, the well-crystallized Mn3O4 sample suffers from severe capacity fading with only 48% capacity retention. Moreover, the specific energies obtained after 200 cycles are 336.1 and 166.0 Wh kg-1 for D-Mn3O4 and Mn3O4, respectively. The drastic differences between the electrochemical performance of D-Mn3O4 and Mn3O4 manifest that the existing manganese vacancies in Mn3O4 spinel structure enhance energy storage capability.
ABSTRACT
OBJECTIVES: Several population pharmacokinetics (popPK) models for polymyxin B have been constructed to optimize therapeutic regimens. However, their predictive performance remains unclear when extrapolated to different clinical centers. Therefore, this study aimed to evaluate the predictive ability of polymyxin B popPK models. METHODS: A literature search was conducted, and the predictive performance was determined for each selected model using an independent dataset of 20 patients (92 concentrations) from the Third Xiangya Hospital. Prediction- and simulation-based diagnostics were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. RESULTS: Eight published studies were evaluated. In prediction-based diagnostics, the prediction error within ± 30% was over 50% in two models. In simulation-based diagnostics, the prediction- and variability-corrected visual predictive check (pvcVPC) showed satisfactory predictivity in three models, while the normalized prediction distribution error (NPDE) tests indicated model misspecification in all models. Bayesian forecasting demonstrated a substantially improvement in the model predictability even with one prior observation. CONCLUSION: Not all published models were satisfactory in prediction- and simulation-based diagnostics; however, Bayesian forecasting improved the predictability considerably with priors, which can be applied to guide polymyxin B dosing recommendations and adjustments for clinicians.
