Advanced Theory and Simulations最新文献

To address the time-varying parameters, nonlinear friction, leakage, and load disturbances inherent to electro-hydrostatic actuator (EHA), and the exoskeleton robot's demands for handling strong coupling and achieving high trajectory accuracy, we design an adaptive variable-damping sliding-mode controller (ADV-SMC) that suppresses chattering while improving the dynamic response of the joint EHA. Because most existing studies overlook the higher-order characteristics of composite disturbances, this work incorporates an improved nonlinear extended disturbance observer (INEDOB) into the controller design. By constructing a novel surrogate function to circumvent the differentiability constraint of the saturation function, the INEDOB enables accurate estimation of time-varying external disturbances and their higher-order derivatives. On the basis of a rigorous stability proof, the effectiveness of the newly designed controller is further analyzed and validated. This integrated control scheme markedly enhances system robustness and trajectory-tracking accuracy, providing a new avenue for high-dynamic-precision control of exoskeleton robots.

Ammonia (NH₃) formation from nitric oxide (NO) and carbon monoxide (CO) in the presence of water is a promising pathway for low-temperature NO_x reduction. In this work, density functional theory (DFT) calculations were used to investigate the mechanism of NH₃ formation on a Rh(111)/γ-Al₂O₃(110), in which the Rh(111) and γ-Al₂O₃(110) were considered separately. NO readily dissociates on the Rh site, forming N*, which reacts with CO to produce the key intermediate of NCO*. Meanwhile, water dissociates more easily on γ-Al₂O₃(110), providing H* species. The hydrolysis of NCO* can proceed via two pathways: on Rh(111), it forms HNCO, while on γ-Al₂O₃(110), it forms HNCOH*. Both intermediates subsequently decompose into NH* species, which are then hydrogenated to produce NH₃. The Rh pathway is more favorable in both kinetics and thermodynamics, while the support mainly supplies hydrogen through water activation. These results suggest a dual-site mechanism in which Rh drives the main transformation, and γ-Al₂O₃(110) assists via hydrogen transfer. This work presents the individual roles of Rh and Al₂O₃ surfaces in NO reduction with CO, producing NH₃, which is relevant to catalytic behavior under humid conditions.

This study designs a novel 2D van der Waals magnetic heterojunction, VS₂/FePSe₃@Ni, achieving remote spin control over the oxygen reduction reaction (ORR) catalytic performance by manipulating the spin orientation of the underlying VS₂ layer. Theoretical calculations reveal that switching the ferromagnetic VS₂ layer's spin orientation from spin-up to spin-down induces significant shifts in the heterojunction's band edge alignment and built-in electric field. This restructuring shifts the optimal ORR pathway: the spin-down state favors the efficient 4e⁻ pathway with H₂O desorption as the rate-determining step (energy barrier: −0.86 eV), while the spin-up state promotes 2e⁻ reduction where OOH hydrogenation limits the rate (energy barrier: −0.54 eV). Further analysis reveals that spin flipping significantly enhances the material's electrical conductivity and Seebeck coefficient, leading to a notable improvement in the thermoelectric figure of merit ZT, demonstrating excellent thermoelectric conversion potential. This research not only uncovers the remote-control mechanism of spin polarization effects on the catalytic performance of heterojunctions but also provides a new paradigm for developing highly active and selective spintronic catalysts and multifunctional thermoelectric materials.

We propose here a zinc selenide (ZnSe)-graphene-reliant sensitive surface plasmon resonance (SPR) sensor for detecting chemicals such as D₂O, alcohol, and acetone. The sensor's performance is evaluated using the transfer matrix method (TMM) with respect to reflectivity, sensitivity, detection accuracy, and figure of merit. The results are then further analyzed by adjusting the sensor's structural parameters to design an optimal structure with optimal performance. Nine ZnSe layers and a monolayer of graphene were used to optimize sensor performance. With a Figure of Merit (FoM) of 46.704 RIU⁻¹ and a maximum sensitivity of 279.666 deg/RIU, this hybrid structure is positioned atop a 45 nm thick layer of silver. The performance of the sensor structure was also evaluated over a wide detection range of 1.33–1.39, as many biological solutions fall within this range.

The growing need for reliable, maintenance-free monitoring of power line infrastructure has intensified interest in sustainable power sources for wireless sensors. Piezoelectric energy harvesting offers a promising alternative to batteries. However, most existing power line harvesters deliberately ignore the combined effects of air damping, strain rate damping, and optimal positioning relative to AC power lines, which limits their power output and real-world applicability. This study addresses the gap and explicitly integrates them within a unified analytical, numerical, and experimental framework validating an optimally positioned piezoelectric energy harvester that extracts mechanical energy from the alternating magnetic field generated by current-carrying conductors. A coupled electromechanical model was formulated, incorporating magnetic excitation, air damping, strain damping to support and verify the results from the experimental setup. Results show that a 6.35 mm³ magnet placed beneath the power line, generates a peak force of 5.386 mN and an output voltage of 563.68 mV, with the harvested power increasing linearly with airflow velocity under a 9 A current flow at 4 mm from the AC power Line. The LTC3588-1 interface subsequently rectified and boosted the harvested voltage to a stable 3.3 V DC supply, demonstrating suitability for powering low-power Internet of Things (IoT) sensors in smart grid applications.

Amorphous silica-based materials are often used as support for catalysis experiments. The insertion of heteroelements (i.e., neither a silicon nor an oxygen atom) leads to the formation of Brønsted or Lewis acid sites. Despite their widespread use, the characterization of their acidic properties and the correlation with structural features remain challenging. In this context, computing chemical properties with quantum chemical methods can bring insights into such materials, while the large size of these systems implies the use of models to control the computational effort. This work aims to present a general procedure for calculating chemical properties of amorphous silica-based catalysts. The amorphous network is generated using classical molecular dynamics via a thermal treatment called melt-and-quench. Particular attention is devoted to the catalyst shape and the silanol coverage. The quality of the final amorphous network is assessed through the characterization of the geometrical structures involving primitive ring analysis. DFT calculations are then performed on hemispheres extracted from the amorphous silica frameworks to model aluminosilicate Brønsted acid sites. The impact of cluster size on the prediction of chemical properties is illustrated by calculations of deprotonation energies, chemical shifts, and vibrational frequencies. The influence of the exchange-correlation (XC) functional on those quantities is also discussed.

A theoretical investigation on the Zn₃MoN₄ compound was carried out using WIEN2k, DFT-based code, to calculate the electronic, optoelectronic, and thermal properties of the wurtzite-based diamond-like Zn₃MoN₄ structure. This compound exhibits an orthorhombic structure with the space group Pmn2₁. The structural, electronic, optoelectronic and thermoelectric properties were calculated within the GGA approximation. As GGA tends to underestimate the band gap, both GGA and the hybrid functional (YS-PBE0) methods were implemented, yielding indirect band gaps of 2.062 and 3.106 eV, respectively. Band gaps predicted using Extra Trees (2.081 eV) and K-Nearest Neighbors (2.087 eV) closely matched the DFT value of 2.06 eV for Zn₃MoN₄, outperforming Random Forest (1.700 eV), Gradient Boost (1.404 eV) and XGBoost (1.455 eV). From the density of states, it was observed that Mo-d and N-p orbitals have the major electronic contribution. The calculated optoelectronic properties, including dielectric constants, absorption coefficient, refractive index, reflectivity, and energy loss, suggest promising potential for optoelectronic applications. Additionally, effective mass, exciton binding energy, and exciton Bohr radius were calculated, with the effective mass of holes being greater than that of electrons, signifying holes as the major carriers. The thermoelectric properties were also investigated, including Seebeck coefficient, electrical conductivity, thermal conductivity, power factor, and figure of merit, across the temperature range 0–1000 K. The ZT values were modelled with consistent R² and RMSE performance for the aforementioned models.