Unlocking high-efficiency decontamination by building a novel heterogeneous catalytic reduction system of thiourea dioxide/biochar.

Herein, we reported a new contaminant purification paradigm, which enabled highly efficient reductive denitration and dechlorination using a green, stable reducing agent thiourea dioxide (TDO) coupled with biochar (BC) over a wide pH range under anoxic conditions. Specifically, BC acted as both activators and electron shuttles for TDO decomposition to achieve complete anoxic degradation of p-nitrophenol (PNP), p-nitroaniline, 4-chlorophenol and 2,4-dichlorophenol within 2 h. During this process, multiple strongly reducing species (i.e., SO22-, SO2•- and e-/H•) were generated in BC/TDO systems, accounting for 13.3%, 9.7% and 75.5% of PNP removal, respectively. While electron transfer between TDO and H+ or contaminants mediated by BC led to H• generation and contaminant reduction. These processes depended on the electron-accepting capacity and electron-conducting domains of biochar. Significantly, the BC/TDO systems were highly efficient at a pH of 2.0-8.0, especially under acidic conditions, which performed robustly in common natural water constituents.

Variational Bayesian Student's-t Mixture Model With Closed-Form Missing Value Imputation for Robust Process Monitoring of Low-Quality Data.

Due to record errors, transmission interruptions, etc., low-quality process data, including outliers and missing data, commonly exist in real industrial processes, challenging the accurate modeling and reliable monitoring of the operating statuses. In this study, a novel variational Bayesian Student's-t mixture model (VBSMM) with a closed-form missing value imputation method is proposed to develop a robust process monitoring scheme for low-quality data. First, a new paradigm for the variational inference of Student's-t mixture model is proposed to develop a robust VBSMM model, which optimizes the variational posteriors in an extended feasible region. Second, conditioned on the complete and partially missing data information, a closed-form missing value imputation method is derived to address the challenges of outliers and multimodality in accurate data recovery. Then, a robust online monitoring scheme that can maintain its fault detection performance in the presence of poor data quality is developed, where a novel monitoring statistic called the expected variational distance (EVD) is first proposed to quantify the changes in operating conditions and can be easily extended to other variational mixture models. Case studies on a numerical simulation and a real-world three-phase flow facility illustrate the superiority of the proposed method in missing value imputation and fault detection of low-quality data.

Pendant Length-Dependent Electrochemical Performances for Conjugated Organic Polymers as Solid-State Polymer Electrolytes in Lithium Metal Batteries.

The development of solid-state polymer electrolytes (SPEs) has been plagued by poor ionic conductivity, low ionic transference number, and limited electrochemical potential window. The exploitation of ionized SPEs is a feasible avenue to solve this problem. Herein, conjugated organic polymers (COPs) with excellent designability and rich pore structures have been selected as platforms for exploration. Three cationic COPs with different chain lengths of quaternary ammonium salts (CbzT@Cx, x = 4, 6, 9) are designed and applied to SPEs for the first time. Meanwhile, the effects of chain lengths on their electrochemical performances are compared. Especially, CbzT@C9 shows the most attractive electrochemical performance due to its high specific surface area of 212.3 m2 g-1. The larger specific surface area allows more exposure of the long-chain quaternary ammonium cation groups, which is more favorable for the dissociation of lithium salts. Moreover, the flexible long-chain structure increases the compatibility with poly(ethylene oxide) (PEO) and reduces the crystallinity of PEO to some extent. The richer pore structure can accommodate more PEO, further disrupting the crystallinity of PEO and creating more channels for the ether-oxygen chain to transport lithium ions. At 60 °C, the SPE (CbzTM@C9) presents an excellent ionic conductivity (σ) of 8.00 × 10-4 S cm-1. CbzTM@C9 has a lithium-ion transference number (tLi+) of 0.48. Thus, the assembled Li/CbzTM@C9/LiFePO4 battery provides a good discharge capacity of 158.8 mAh g-1 at 0.1C. After 70 cycles, the capacity retention rate is 93.8% with a Coulombic efficiency of 98%. The excellent flexibility brings stable power supply capability under various bending angles to the assembled Li/CbzTM@C9/LiFePO4 soft-packed battery. The project uses conjugated organic polymers in SPEs and creates an avenue to develop flexible energy storage equipment.

Incremental Variational Bayesian Gaussian Mixture Model With Decremental Optimization for Distribution Accommodation and Fine-Scale Adaptive Process Monitoring.

Due to the frequent changes in operating conditions, time-varying behaviors, including slow-varying dynamics and switching modes, commonly exist in industrial processes, resulting in different degrees of shifting in the process data distribution. When the data distribution shifts in a relatively wide range, conventional adaptive methods become ineffective since they are unable to distinguish normal shifts from real faults, leading to false alarms. In this study, an incremental variational Bayesian Gaussian mixture model (IncVBGMM) is proposed for developing a fine-scale adaptive monitoring scheme to efficiently accommodate the shifting data distribution caused by different degrees of time-varying behaviors. First, IncVBGMM with decremental optimization is proposed to adapt to the changing data distribution via the automatic complement of local models while reducing redundancy to optimize the mixture model. Then, a fine-scale adaptive monitoring scheme is built with physical interpretations to discern between normal shifts and real faults by joint analysis of the static and dynamic information. In addition, a novel monitoring statistic called the expectation of variational Bayesian inference distance (EVBID) is proposed, which can quantify the distance from samples to the variational monitoring model and indicate the fault effects. Case studies involving a real-world three-phase flow facility reveal that the proposed method can accurately differentiate various types of faults from normal shifts and effectively adapt to the time-varying dynamics.

Analytical Modeling of a Doubly Clamped Flexible Piezoelectric Energy Harvester with Axial Excitation and Its Experimental Characterization.

With the rapid development of wearable electronics, novel power solutions are required to adapt to flexible surfaces for widespread applications, thus flexible energy harvesters have been extensively studied for their flexibility and stretchability. However, poor power output and insufficient sensitivity to environmental changes limit its widespread application in engineering practice. A doubly clamped flexible piezoelectric energy harvester (FPEH) with axial excitation is therefore proposed for higher power output in a low-frequency vibration environment. Combining the Euler-Bernoulli beam theory and the D'Alembert principle, the differential dynamic equation of the doubly clamped energy harvester is derived, in which the excitation mode of axial load with pre-deformation is considered. A numerical solution of voltage amplitude and average power is obtained using the Rayleigh-Ritz method. Output power of 22.5 µW at 27.1 Hz, with the optimal load resistance being 1 MΩ, is determined by the frequency sweeping analysis. In order to power electronic devices, the converted alternating electric energy should be rectified into direct current energy. By connecting to the MDA2500 standard rectified electric bridge, a rectified DC output voltage across the 1 MΩ load resistor is characterized to be 2.39 V. For further validation of the mechanical-electrical dynamical model of the doubly clamped flexible piezoelectric energy harvester, its output performances, including both its frequency response and resistance load matching performances, are experimentally characterized. From the experimental results, the maximum output power is 1.38 µW, with a load resistance of 5.7 MΩ at 27 Hz, and the rectified DC output voltage reaches 1.84 V, which shows coincidence with simulation results and is proved to be sufficient for powering LED electronics.

Distribution of persistent free radicals in different molecular weight fractions from peat humic acids and their impact in reducing goethite.

Humic substances, the most abundant component of soil organic matter, play vital roles in the biogeochemical cycles and pollutant redox reactions. However, the knowledge regarding the distribution of persistent free radicals (PFRs) and redox capacity in different molecular weight fractions (MWF) of humic acid (HA) and their impact on the transformation of iron oxide minerals remains unknown. In this study, we separated bulk HA into various MWF using dialysis methods and systematically investigated their PFRs properties, redox capacity, and the impact in reducing goethite. The results showed that the PFRs in the low MWF (<3500, <7000, and <14,000 Da) can be assigned to oxygen-centered organic radicals while those in the bulk and high MWF (14000-retentate) were assigned to carbon-centered organic radicals. The PFRs concentrations of low MWF were 0.20-0.45 × 1016 spins/g, far less than those of bulk HA (3.04 × 1016 spins/g) and 14000-retentate (1.30 × 1016 spins/g). The total concentrations of reactive oxygen species (ROS) induced by PFRs in HA fractions ranged from 8.04 × 1016 (in 14000-retentate) to 32.35 × 1016 spins/g (in bulk HA). Compared with the low MWF, the 14000-retentate fractions had the higher reducing capacity, which was positively related to the content of PFRs and phenolic hydroxyl in HA. The results obtained provide valuable insight into the geochemistry processes of Fe-containing minerals during their interaction with HA in the natural environment. Overall, the results obtained provide valuable insight into the geochemical behaviors of HA-associated PFRs under natural conditions.