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Abstract Stroke is a leading cause of adult disability worldwide, with approximately 101 million survivors globally. Over 60% of these individuals live with from long-term, often lifelong, movement impairments that significantly hinder their ability to perform essential daily activities and maintain independence. Post-stroke movement disabilities are highly associated with structural and functional changes in motor descending pathways, particularly the corticospinal tract and other indirect motor pathways via the brainstem. For decades, neuroengineers have been working to quantitively evaluate the post-stroke changes of motor descending pathways, aiming to establish a precision prognosis and tailoring treatments to post-stroke motor impairment. However, a clear and practicable technique has not yet been established as a breakthrough to change the standard of care for current clinical practice. In this review, we outline recent progress in neuroimaging, neuromodulation, and electrophysiological approaches for assessing structural and functional changes of motor descending pathways in stroke. We also discuss their limitations and challenges, arguing the need of artificial intelligence and large multi-modal data registry for a groundbreaking advance to this important topic. 

Abstract Objective.Effective characterization of neural complexity during motor execution tasks enhances understanding of maladaptive cortical reorganization in stroke and inform targeted rehabilitation. While traditional EEG analyses often do not consider nonlinear temporal dynamics, we introduce a recurrence based computational framework to quantify cortical complexity during hierarchical motor tasks. Here, we evaluate contralesional motor system engagement in stroke survivors using recurrence quantification analysis (RQA), ensuring sensitivity to nonlinear and temporally structured cortical activity.Approach. RQA was applied to EEG signals recorded during shoulder abduction (SABD) at 20% and 40% torque levels to characterize nonlinear cortical dynamics and quantify complexity distinguishing adaptive from maladaptive motor system engagement. Spatially resolved recurrence metrics were compared between stroke and control participants to elucidate compensatory cortical reorganization linked to motor impairment and hierarchical task demands.Results. Our findings show a statistically significant increase in EEG signal complexity within the contralesional hemisphere of stroke participants, particularly under higher SABD loads. Consistent with previous studies, we observed abnormal muscle coactivation patterns between proximal and distal muscles, along with distinct shifts in EMG vector direction in stroke-impaired limbs. These shifts in coactivation patterns suggest constraints in muscle coactivation patterns resulting from losses in corticofugal projections and upregulated brainstem pathways.Significance. We introduce a novel application of RQA to quantify nonlinear EEG complexity during motor execution in chronic stroke. Our results show that increased EEG complexity reflects greater recruitment of contralesional motor pathways, indicating maladaptive cortical reorganization linked to impaired motor control. Unlike traditional spectral or connectivity-based EEG signal processing methods, RQA quantifies temporally evolving, nonlinear recurrence dynamics, serving as a marker of maladaptive contralesional motor recruitment, positioning RQA as a promising, clinically meaningful, and computationally efficient tool to evaluate cortical dynamics and guide targeted neurorehabilitation strategies aimed at minimizing maladaptive plasticity. 

Tertiary phosphines are ubiquitous in inorganic chemistry. They play important roles as ligands in coordination chemistry and catalysis. Furthermore, they act as surface acidity probes for oxide surfaces. However, only volatile phosphines, such as PH3 have been applied in this function so far. Here we demonstrate for the first time that the triaryl- and trialkylphosphines PPh3 and PCy3 with high melting points self-adsorb readily onto a silica surface even in the absence of a solvent. The self-adsorption takes place within days when both solid components are mixed and then left undisturbed. The phosphines form well-defined monolayers on the surface and the transition from monolayer to left-over polycrystalline phosphine is abrupt. Therefore, the maximal surface coverage with a monolayer can be easily determined. When the phosphines are adsorbed from solutions, the same maximal surface coverage is found. Solid-state NMR spectroscopy provides a unique analytical tool for studying the structure and dynamics of phosphines in different environments. 31P and 2H solid-state NMR measurements are successfully applied for characterizing the adsorption process and the mobilities of the adsorbed phosphines across the silica surface. Furthermore, using (Ph3P)2Ni(CO)2 as a representative, it is demonstrated that the silica surface has a hitherto unrecognized impact on immobilized and surface-residing catalysts because it competes for phosphine ligands coordinated to a metal center. This competition manifests as one more factor leading to the loss of phosphine ligands and ultimately leaching of immobilized metal complexes or nanoparticle formation. Besides the increase of fundamental knowledge about adsorption processes, the presented results have implications for chromatographic separations of metal complexes and for the lifetime of immobilized and other types of surface-residing catalysts.