Water-repellent hydrophilic nanogrooves.

The wetting behavior of a nanodrop atop a nanogroove on a smooth or a rough surface is explored by many-body dissipative particle dynamics and Surface Evolver. The nanogroove possesses the same contact angle (θY) as that of the surface. Depending on whether the groove is initially wetted or not, two critical contact angles beyond which the groove cannot be wetted are determined. Three regimes are identified: (i) as θY ≤ θ, the groove is always wetted; (ii) as , both impregnated and unwetted states can be observed; (iii) as , the groove cannot be impregnated. As the drop volume is increased, both θ and decrease but become insensitive to the volume eventually. Surface roughness tends to hamper the impregnation of grooves by liquid. Compared to a smooth surface, both critical contact angles of a rough surface with regular shallow pits are smaller. As a result, a large drop is unable to wet the groove with a rough surface even when the surface becomes slightly hydrophilic. When the surface structure within the groove is modified from shallow pits to straight trenches, the critical contact angle is further reduced. Our simulation outcomes show that the surface structure within the groove is crucial for liquid imbibition and it is possible to fabricate hydrophilic cavities that can prevent impregnation, without resorting to chemical modification processes.

Giant surfactants provide a versatile platform for sub-10-nm nanostructure engineering.

The engineering of structures across different length scales is central to the design of novel materials with controlled macroscopic properties. Herein, we introduce a unique class of self-assembling materials, which are built upon shape- and volume-persistent molecular nanoparticles and other structural motifs, such as polymers, and can be viewed as a size-amplified version of the corresponding small-molecule counterparts. Among them, "giant surfactants" with precise molecular structures have been synthesized by "clicking" compact and polar molecular nanoparticles to flexible polymer tails of various composition and architecture at specific sites. Capturing the structural features of small-molecule surfactants but possessing much larger sizes, giant surfactants bridge the gap between small-molecule surfactants and block copolymers and demonstrate a duality of both materials in terms of their self-assembly behaviors. The controlled structural variations of these giant surfactants through precision synthesis further reveal that their self-assemblies are remarkably sensitive to primary chemical structures, leading to highly diverse, thermodynamically stable nanostructures with feature sizes around 10 nm or smaller in the bulk, thin-film, and solution states, as dictated by the collective physical interactions and geometric constraints. The results suggest that this class of materials provides a versatile platform for engineering nanostructures with sub-10-nm feature sizes. These findings are not only scientifically intriguing in understanding the chemical and physical principles of the self-assembly, but also technologically relevant, such as in nanopatterning technology and microelectronics.

The effect of Dynamic tape's directional support on shoulder fatigue and pitching performance in amateur baseball players: a randomized crossover trial.

BACKGROUND: To evaluate whether the application of Dynamic tape to the pitching shoulder could result in reduced shoulder fatigue, reduced delayed onset muscle soreness, or improved performance. METHODS: This is a randomized crossover study, in which participants and investigators were blinded, included 20 amateur adult baseball players without shoulder pain. Sham taping and Dynamic taping were randomized, using an internal rotation support taping method in both groups. Bilateral shoulder strength and range of motion were measured with a handheld dynamometer and clinical goniometer before and after each test. The percentage of strength decrease, range of motion, pitch velocity, spin rate, and shoulder pain were recorded. The post-pitching decrease in strength and percentage of strength decrease were calculated by paired t-test and the pitching speed and spin rates in the innings for both the sham and Dynamic taping groups were analyzed using two-way ANOVA. RESULTS: Compared with the sham group, the Dynamic tape group showed a significant loss in the percentage of strength decrease in internal rotation compared to the sham group (-1.4% vs. 7.0%, p = 0.03). However, no significant differences were observed in other strength declines, shoulder range of motion, pain, pitching velocity, or spin rate. CONCLUSIONS: Dynamic tape reduced direction-specific shoulder fatigue but did not significantly enhance pitching performance or prevent delayed onset muscle soreness. TRIAL REGISTRATION: ClinicalTrials: N201912094.

Stoichiometric self-assembly of shape-persistent 2D complexes: a facile route to a symmetric supramacromolecular spoked wheel.

An approach to multicomponent coordination-driven self-assembly of the first terpyridine-based, shape-persistent, giant two-dimensional D(6h) supramacromolecular spoked wheel is reported. Mixing core T6, rim T3, and Zn(II) or Cd(II) ions in a stoichiometric ratio (1:6:12) permitted the selective generation of a highly symmetric spoked wheel in 94% isolated yield via geometric and thermodynamic control. The products were characterized by a combination of traveling-wave ion mobility mass spectrometry and NMR techniques together with TEM imaging, which agreed with computational simulations.

Breaking symmetry toward nonspherical Janus particles based on polyhedral oligomeric silsesquioxanes: molecular design, "click" synthesis, and hierarchical structure.

The design, synthesis, and self-assembly of a series of precisely defined, nonspherical, polyhedral oligomeric silsesquioxane (POSS)-based molecular Janus particles are reported. The synthesis aims to fulfill the "click" philosophy by using thiol-ene chemistry to efficiently install versatile functionalities on one of the POSS cages. In such a way, both the geometrical and chemical symmetries were broken to create the Janus feature. These particles self-organize into hierarchically ordered supramolecular structures in the bulk. For example, the Janus particle with isobutyl groups on one POSS and carboxylic groups on the other self-assembles into a bilayered structure with head-to-head, tail-to-tail arrangements of each particle, which further organize into a three-dimensional orthorhombic lattice. While the ordered structure in the layers was lost upon heating via a first-order transition, the bilayered structure persisted throughout. This study provides a model system of well-defined molecular Janus particles for the general understanding of their self-assembly and hierarchical structure formation in the condensed state.

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU.

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.