arXiv subjects

Zhenxiang Wang

Publications and source records attributed to Zhenxiang Wang.

5 recordsLinked to original sources

arXiv2025

A Continuous sEMG-Based Prosthetic Hand Control System Without Motion or Force Sensors

Regressively-based surface electromyography (sEMG) prosthetics are widely used for their ability to continuously convert muscle activity into finger force and motion. However, they typically require additional kinematic or dynamic sensors, which increases complexity and limits practical application. To address this, this paper proposes a method based on the simplified near-linear relationship between sEMG and finger force, using the near-linear model ResDD proposed in this work. By applying the principle that a line can be determined by two points, we eliminate the need for complex sensor calibration. Specifically, by recording the sEMG during maximum finger flexion and extension, and assigning corresponding forces of 1 and -1, the ResDD model can fit the simplified relationship between sEMG signals and force, enabling continuous prediction and control of finger force and gestures. Offline experiments were conducted to evaluate the model's classification accuracy and its ability to learn sufficient information. It uses interpolation analysis to open up the internal structure of the trained model and checks whether the fitted curve of the model conforms to the nearly linear relationship between sEMG and force. Finally, online control and sine wave tracking experiments were carried out to further verify the practicality of the proposed method. The results show that the method effectively extracts meaningful information from sEMG and accurately decodes them. The near-linear model sufficiently reflects the expected relationship between sEMG and finger force. Fitting this simplified near-linear relationship is adequate to achieve continuous and smooth control of finger force and gestures, confirming the feasibility and effectiveness of the proposed approach.

cs.RO↗

arXiv2025

Machine Learning Relationships between Nanoporous Structures and Electrochemical Performance in MOF Supercapacitors

The development of supercapacitors is impeded by the unclear relationships between nanoporous electrode structures and electrochemical performance, primarily due to challenges in decoupling the complex interdependencies of various structural descriptors. While machine learning (ML) techniques offer a promising solution, their application is hindered by the lack of large, unified databases. Herein, we use constant-potential molecular simulation to construct a unified supercapacitor database with hundreds of metal-organic framework (MOF) electrodes. Leveraging this database, well-trained decision-tree-based ML models achieve fast, accurate, and interpretable predictions of capacitance and charging rate, experimentally validated by a representative case. SHAP analyses reveal that specific surface area (SSA) governs gravimetric capacitance while pore size effects are minimal, attributed to the strong dependence of electrode-ion coordination on SSA rather than pore size. SSA and porosity, respectively, dominate volumetric capacitance in 1D-pore and 3D-pore MOFs, pinnacling the indispensable effects of pore dimensionality. Meanwhile, porosity is found to be the most decisive factor in the charging rate for both 1D-pore and 3D-pore MOFs. Especially for 3D-pore MOFs, an exponential increase with porosity is observed in both ionic conductance and in-pore ion diffusion coefficient, ascribed to loosened ion packing. These findings provide profound insights for the design of high-performance supercapacitor electrodes.

cond-mat.mtrl-sci↗

arXiv2024

Organic solvent boosts charge storage and charging dynamics of conductive MOF supercapacitors

Conductive metal-organic frameworks (c-MOFs) and ionic liquids (ILs) have emerged as auspicious combinations for high-performance supercapacitors. However, the nanoconfinement from c-MOFs and high viscosity of ILs slow down the charging process. This hindrance can, however, be resolved by adding solvent. Here, we performed constant-potential molecular simulations to scrutinize the solvent impact on charge storage and charging dynamics of MOF-IL-based supercapacitors. We find conditions for >100% enhancement in capacity and ~6 times increase in charging speed. These improvements were confirmed by synthesizing near-ideal c-MOFs and developing multiscale models linking molecular simulations to electrochemical measurements. Fundamentally, our findings elucidate that the solvent acts as an ionophobic agent to induce a substantial enhancement in charge storage, and as an ion traffic police to eliminate convoluted counterion and co-ion motion paths and create two distinct ion transport highways to accelerate charging dynamics. This work paves the way for the optimal design of MOF supercapacitors.

physics.app-ph↗

arXiv2024

Moment-Tensor-Based Constant-Potential Modeling of Electrical Double Layers

Constant-potential molecular dynamics (MD) simulations are indispensable for understanding the capacitance, structure, and dynamics of electrical double layers (EDLs) at the atomistic level. However, the classical constant-potential method, relying on the so-called 'floating charges' to keep electrode equipotential, overlooks quantum effects on the electrode and always underestimates EDL capacitance for typical electrochemical systems featuring metal electrodes in aqueous electrolytes. Here, we propose a universal theoretical framework as moment-tensor-based constant potential method (mCPM) to capture electronic structure variations with electric moments. For EDLs at Au(111) electrodes, mCPM-based MD reveals bell-shaped capacitance curves in magnitude and shape both quantitatively consistent with experiments. It further unveils the potential-dependent local electric fields, agreeing with experimental observations of redshift vibration of interfacial water under negative polarization and predicting a blueshift under positive polarization, and identifies geometry dependence of two time scales during EDL formation.

physics.chem-ph↗

arXiv2021

Symmetrizing cathode-anode response to speed up charging of nanoporous supercapacitors

Asymmetric behaviors of capacitance and charging dynamics in the cathode and anode are general for nanoporous supercapacitors. Understanding this behavior is essential for the optimal design of supercapacitors. Herein, we perform constant-potential molecular dynamics simulations to reveal asymmetric features of porous supercapacitors and their effects on capacitance and charging dynamics. Our simulations show that, counterintuitively, charging dynamics can be fast in pores providing slow ion diffusion and vice versa. Unlike electrodes with single-size pores, multi-pore electrodes show overcharging and accelerated co-ion desorption, which can be attributed to the subtle interplay between the dynamics and charging mechanisms. We find that capacitance and charging dynamics correlate with how the ions respond to an applied cell voltage in the cathode and anode. We demonstrate that symmetrizing this response can help boost power density, which may find practical applications in supercapacitor optimization.

physics.chem-ph↗