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Autohesion is a unique class of adhesion that enables the bonding of two identical surfaces by establishing intimate contact at interfaces. Creating intimacy between two identical surfaces poses a challenging task, often constrained by the presence of surface roughness and chemical heterogeneity. To surmount this challenge, we document a variety of autohesive traits in polypropylene-based meltblown nonwovens, accomplished through a facile, scalable, energy-efficient, and cost-effective ultrasonic bonding process. The mean work of autohesion for a single polypropylene bond, serving as a figure of merit, has been computed by extending the classical Johnson−Kendall−Roberts (JKR) theory by factoring in peel strength along with key fiber and structural parameters of nonwoven materials. Achieving a high figure of merit in ultrasonically bonded nonwovens hinges on the synergistic interplay of key process parameters, including static force, power, and welding speed, with the fiber and structural properties acting in concert. In this regard, peel-off force analysis has also been conducted on a series of twenty-seven ultrasonically bonded meltblown nonwovens prepared using a 33 full factorial design by systematically varying process parameters (static force, power, and welding speed) across three levels and extension rate. X-ray microcomputed tomography (microCT) analysis has been performed on select ultrasonically bonded nonwoven samples to discern their bulk characteristics. A broad spectrum of mean work of autohesion for a single polypropylene bond, ranging from 1.88 to 9.93 J/m², has been ascertained by modulating key process parameters.

Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel compensation-peel off (ICKC-P) framework is proposed, which consists of three steps: decomposition of the motor units (MUs) with relatively large energy by using the iterative convolution kernel compensation (ICKC) method and extraction of low-energy MUs with a Post-Processor and novel ‘peel-off’ strategy. Results- The performance of the proposed framework was evaluated by both simulated and experimental HD-sEMG signals. Our simulation results demonstrated that, with 120 simulated MUs, the proposed framework extracts more MUs compared to K-means convolutional kernel compensation (KmCKC) approach across six noise levels. And the proposed ‘peel-off’ strategy estimates more accurate MUAP waveforms at six noise levels than the ‘peel-off’ strategy proposed in the progressive FastICA peel-off (PFP) framework. For the experimental sEMG signals recorded from biceps brachii, an average of 16.1 ±3.4 MUs were identified from each contraction, while only 10.0 ± 2.8 MUs were acquired by the KmCKC method. Conclusion- The high yield and accuracy of MUs decomposed from simulated and experimental HD-sEMG signals demonstrate the superiority of the proposed framework in decomposing low-energy MUs compared to existing methods for HD-sEMG signal decomposition. Significance- The proposed framework enables us to construct a more representative motor unit pool, consequently enhancing our understanding pertaining to various neuropathological conditions and providing invaluable information for the diagnosis and treatment of neuromuscular disorders and motor neuron diseases.

Competing Interests: All the authors declare no conflict of interest.