The environmental pollution caused by phenolic pollutants has attracted more and more attention, and developing simple and efficient detection methods for phenolic pollutants has aroused great interest of researchers. In this paper, a reduced graphene oxide (RGO)-modified electrode with high electrocatalytic activity was prepared by simple electrochemical reduction of graphene oxide (GO) on a glassy carbon electrode using cyclic voltammetry. The electrode-modified materials were systematically characterized by UltraViolet–Visible (UV–Vis), Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), X-ray diffraction spectroscopy (XRD) and electrochemical impedance spectroscopy (EIS). Differential pulse voltammetry (DPV) was employed to detect catechol with high sensitivity. The effects of conditions such as the amount of GO suspension, cycle number of cyclic voltammetry reduction, pH value of the buffer solution, and accumulation time on the detection of catechol were investigated. Under optimal conditions, the oxidation peak current of catechol showed a good linear relationship with its concentration when the concentration of catechol was in the range of 1 × 10−7 M 1 × 10−5 M. The linear equation was expressed as I (μA) = 2.1321 C (μM) + 4.2807 with a correlation coefficient of 0.9902, and the limit of detection (LOD) was 4.5 × 10−8 M. Meanwhile, this electrochemical sensor has been proven to be applicable for the determination of catechol in an actual water sample.
This paper investigates the hierarchical three-dimensional (3-D) output tracking problem for networked uncertain robotic systems (NURSs) endowed with Byzantine fault-tolerant (BFT) capability, where both parametric uncertainties and external disturbances are explicitly considered. To address the vulnerabilities introduced by adversarial Byzantine robots and model uncertainties, a hierarchical control framework is developed, consisting of a BFT estimation layer and an output-space tracking layer. In the BFT estimation layer, a distributed BFT mechanism is constructed over a directed interaction graph, enabling each robot to suppress falsified state broadcasts and asymptotically recover the trusted virtual leader trajectory. Once a Byzantine node is detected, a BFT-based structural isolation strategy is activated to eliminate corrupted information while maintaining the connectivity of the remaining network. In the output-space tracking layer, a nonlinear 3-D tracking controller is designed using Jacobian-based output-space transformation and robust compensation terms to counteract uncertainties and disturbances in robot dynamics. The closed-loop stability of the hierarchical architecture is rigorously established through Lyapunov analysis. Numerical simulations further verify that the proposed method achieves resilient estimation, reliable Byzantine isolation, and high-precision 3-D output tracking for NURSs operating under adversarial conditions.
This study examines how central bank institutional characteristics influence financial sector carbon emissions using a comprehensive dataset of 377 financial institutions across 45 countries from 2002 to 2020. We find that central bank size increases financial sector emissions, while independence, transparency, and solvency reduce emissions, with independence emerging as the dominant determinant followed by transparency. These effects are more pronounced in countries with higher financial development levels. Macroprudential policy development moderates these relationships by attenuating central bank size's positive effects while amplifying institutional quality characteristics' negative effects, confirming that coordinated monetary and prudential policies shape environmental outcomes. Our study also shows that imbalanced transparency-independence configurations prove ineffective, with balanced integration essential for emission reduction. These findings suggest that policymakers should implement differentiated collateral frameworks constraining balance sheet expansion's environmental externalities while prioritizing foundational financial infrastructure development in less developed economies before attempting sophisticated green monetary interventions.
This study investigates the determinants of green energy penetration in the Next Eleven (N-11) economies over the period 2000–2022, with a particular focus on the roles of foreign direct investment (FDI), green transition, governance quality, industrial growth, and urbanization. The primary objective of the study is to assess how investment flows, structural transformation, and institutional capacity jointly shape the adoption of renewable energy in fast-growing emerging economies. To achieve this goal, the study employs a second-generation panel econometric and machine-learning framework that accounts for cross-sectional dependence, slope heterogeneity, and long-run equilibrium relationships. Specifically, cross-sectional dependence and slope homogeneity tests are conducted, followed by CADF and CIPS unit root tests and the Westerlund cointegration approach. Long-run effects are then estimated using Partialing-Out LASSO and Cross-Fit machine-learning estimators, complemented by SHAP analysis to interpret nonlinear and heterogeneous effects. The results indicate that green transition, governance quality, and urbanization significantly promote green energy penetration. In contrast, FDI and industrial growth exert adverse effects, reflecting carbon-intensive investment and production structures. The findings highlight the importance of coordinated investment strategies, institutional strengthening, and urban planning in accelerating renewable energy transitions in emerging economies. These results provide policy-relevant insights for achieving sustainable energy development while supporting long-term economic growth in the N-11 countries.
This article investigates the predefined-time coordination control problem for multiagent systems (MASs) governed by diffusion partial differential equations (PDEs). The diffusion mechanism plays a critical role by modeling the spatially distributed state variations among agents, facilitating the continuous propagation of state information across the system, and enabling distributed interactions. To address the challenges posed by spatially distributed states and the requirement for achieving consensus within a predefined time, the strongly continuous semigroup theory is utilized to establish the structured solvability of the system dynamics governed by diffusion PDEs. Building on this theoretical foundation, new predefined-time control protocols are designed for both leaderless and leader-follower consensus scenarios. These protocols guarantee user-specified time convergence, irrespective of initial conditions. Furthermore, the convergence principle is applied alongside a Lyapunov functional approach to derive sufficient conditions for achieving predefined-time consensus under the proposed control mechanism. Finally, numerical examples are conducted to verify the theoretical results.
