Coupling the hydrazine oxidation reaction (HzOR) with the hydrogen evolution reaction (HER) offers an energy-saving strategy for hydrogen production, addressing both energy scarcity and environmental concerns. However, efficient bifunctional electrocatalysts remain a key bottleneck. Herein, by taking advantage of lysozyme with massive reserves in nature, abundant heteroatoms and low cost, the ultrafine Ru nanoparticles loaded on N,S-codoped porous carbon (Ru-PNSC) with honeycomb structures and large surface area is synthesized, which exhibits excellent HER/HzOR bifunctional activity and stability across alkaline and neutral media. Specifically, at 10 mA cm-2, Ru-PNSC achieves remarkably low HER operating potentials (vs. RHE) of -2.8 mV (alkaline) and -21.6 mV (neutral), and HzOR potentials of -59 mV and 190 mV, respectively. Notably, the assembled hydrazine-assisted system operates at voltages of 10.3 mV (alkaline) and 231.5 mV (neutral) at 10 mA cm- 2. Furthermore, this system can be efficiently driven by a direct hydrazine-H2O2 fuel cell or a commercial solar cell, delivering substantial H2 production rates of 1.05 and 1.56 mmol h- 1, respectively.
An adaptive optimal neural network algorithm with fast finite-time convergence is proposed for stochastic multiagent systems (SMASs) under deception attacks, time-varying asymmetric output constraints and dead zones. An additional attack signal corrupts the state information of nonlinear systems, resulting in the unavailability of real state information for controller development. To overcome this obstacle, a reinforcement learning (RL)-based identifier-actor-critic-disturbance architecture is used to develop a fast finite-time adaptive optimal tracking algorithm for each subsystem in SMASs, which alleviates the negative effects of cyberattacks that intentionally tamper with sensor signals. Herein, a barrier function is designed to transform the constrained system into an unconstrained equivalent. Furthermore, time-varying dead zones in SMASs pose considerable challenges for controller design, while enhancing the applicability of the system in practical scenarios. The proposed resilient adaptive optimal tracking algorithm guarantees the boundedness of all signals in the overall system in probability. Eventually, two simulation results are conducted to prove the effectiveness of the proposed method.
The conversion of lignocellulosic biomass into value-added chemicals represents a key pathway toward establishing a circular bioeconomy. 5-Hydroxymethylfurfural (HMF), readily obtainable from renewable resources such as waste paper and corncob, serves as a versatile platform molecule. However, its selective transformation to 2,5-hexanedione (HD)-a valuable building block for polymers, pharmaceuticals, and biofuels-is hindered by the incompatible requirements of hydrodeoxygenation and subsequent hydrolytic ring-opening. Herein, we present a one-pot, two-step strategy that temporally decouples these two functions by employing a mechanically mixed Ni/SiO2 and HZSM-5 catalyst in conjunction with an operational gas switch from H2 to N2. Under the optimized conditions, the system achieves an overall HMF conversion exceeding 98% and an HD selectivity of 73.8%. Comprehensive characterization (XRD, TEM, XPS, NH3-TPD, H2-TPR) reveals well-dispersed NiO species and a well balanced Br & oslash;nsted/Lewis acid distribution. DFT calculations on a conceptual model interface suggest that a dual-site anchoring of HMF can reduce the activation barrier for the key deoxygenation step, consistent with the experimentally observed synergy. This work demonstrates that functional decoupling via environmental modulation offers a generalizable approach for designing efficient tandem catalysts for biomass valorization.
The rank-partitioned multi-strategy collaborative optimization framework (RPMSCF) is derived from the Dung Beetle Optimization (DBO) algorithm, which exhibits rapid convergence and strong search capabilities. However, its performance is limited by the undue emphasis on global best and worst solutions. To address these limitations, this paper proposes an enhanced version of RPMSCF incorporating multiple strategies, referred to as ERPMSCF. Specifically, a dynamic opposition-based learning mechanism is employed to refine the initial population quality. Horizontal and vertical crossover strategies are incorporated to bolster the search capabilities. Moreover, to preserve high population diversity across the iterative process, the conventional boundary-control mechanism is replaced with regulatory rules derived from the Wave Search Algorithm. To evaluate the effectiveness of ERPMSCF, it is compared with state-of-the-art algorithms using benchmark functions from CEC 2017 and CEC2022. Experimental results demonstrate that the ERPMSCF exhibits reliable performance, characterized by robust global exploration ability, stable convergence behavior, and notable efficacy in large-scale optimization tasks. Furthermore, the ERPMSCF is assessed through three engineering benchmarks, confirming its practical viability and effectiveness in addressing complex real-world optimization scenarios.
Large-scale and long-time-span nonequilibrium molecular dynamics simulations have been performed to determine the thermal conductivity of single-walled and double-walled carbon nanotubes (CNTs) using a machine learning potential trained on atomic energies and forces from density functional theory calculations for sp(2)-hybridized carbon. The size dependence of graphene and CNTs up to 1 mu m has been studied with 200000 atoms and simulation times up to 5 ns. The simulations reveal that thermal transport, whether ballistic, quasi-ballistic, or diffusive, is determined by the relationship between the sample length and the effective mean-free path (MFP). The system size has less effect on thermal conductivity when the sample length significantly exceeds the MFP. Radial tensile strain in CNTs causes the C-C bond length to increase in smaller-diameter CNTs, resulting in a phonon softening effect that subsequently reduces thermal conductivity. An analytical function is proposed to describe the relationship between phonon relaxation time and nanotube diameter. The thermal conductivity of the double-walled CNT is lower than that of an equivalent-size single-walled CNT. Phonon-phonon scattering, interlayer van der Waals interactions, and degenerate coupling of transverse acoustic modes are considered to contribute to the reduction in thermal transport.
