The corrosion resistance of titanium alloy poses a crucial challenge, significantly affecting its prospect for service and application. The present study aimed to investigate the corrosion resistance of Ti-6Al-4V ELI alloys with varying surface roughness in hydrofluoric acid solution, in order to assess the influence of roughness on their corrosion resistance performance. The weight loss percentage, surface morphology evolution, and roughness variation of Ti-6Al-4V ELI alloys before and after exposure to hydrofluoric acid corrosion were characterized. While the weight loss and weight loss percentage of the Ti-6Al-4V ELI alloy increased with prolonged corrosion, the overall weight loss rate decreased. The accumulation of TiF₃ phases and depletion of the Ti-6Al-4V ELI matrix mutually led to the alterations of the surface roughness. Due to the inability to prevent fluoride ions from contacting with the Ti-6Al-4V ELI alloy, continuous corrosion occurred in hydrofluoric acid. Based on these experimental results and analysis, the corrosion mechanism of the Ti-6Al-4V ELI alloy corroded by hydrofluoric acid solution was elucidated. Furthermore, an analysis was conducted to explore the influence of corrosion time on mechanical properties by analyzing the decay in compressive properties of the Ti-6Al-4V ELI titanium alloy after hydrofluoric acid corrosion treatment. The bearing capacity of the Ti-6Al-4V ELI alloy deteriorated over the corrosion time.

We have developed a CSPR model by incorporating self-enhanced mobility, which can reproduce the vortex phenomenon and reveal the transition mechanism of different vortex patterns from a physical perspective.

Active systems exhibit fascinating self-organized structures and rich motility patterns, yet the underlying mechanisms governing their emergence and characteristics remain elusive. Here, we develop a chiral self-propelled rod (CSPR) model with mechanical contact-induced quorum sensing to investigate the spatiotemporal dynamics of dense bacteria populations. Our findings show that the CSPR model showcases spontaneous nonequilibrium oscillatory clustering of active systems. The motion characteristics of these clusters depend on colony features (microswimmers' morphology and density) and mechanical contact-induced sensing mechanisms (polarization alignment and angular velocity alignment of CSPR). Interestingly, reinforced strength of polar alignment accelerates the formation of stable oscillations, while decreased density and angular velocity alignment strength modify their emergence pattern. Significantly, our study identifies three distinct oscillation patterns: global stable oscillation, bistable oscillation, and multistable oscillation, and reveals that their phase transitions are driven by variations in the spatial correlation of CSPR. These insights provide a new perspective on understanding the intricate evolution of active matter, opening possible avenues for emerging applications.

Accurate diagnosis and treatment assessment of liver fibrosis face significant challenges, including inherent limitations in current techniques like sampling errors and inter-observer variability. Addressing this, our study introduces a novel machine learning (ML) framework, which integrates light gradient boosting machine and multivariate imputation by chained equations to enhance liver status assessment using biomechanical markers. Building upon our previously established multiscale mechanical characteristics in fibrotic and treated livers, this framework employs Gaussian Bayesian optimization for post-imputation, significantly improving classification performance. Our findings indicate a marked increase in the precision of liver fibrosis diagnosis and provide a novel, quantitative approach for assessing fibrosis treatment. This innovative combination of multiscale biomechanical markers with advanced ML algorithms represents a transformative step in liver disease diagnostics and treatment evaluation, with potential implications for other areas in medical diagnostics.

The cytoskeleton, a dynamic network of structural proteins within cells, is essential for cellular deformation and responds to external mechanical cues. Here, based on the structure of the cytoskeleton, combined with the biochemical reactions of the activator RhoA and the inhibitor F-actin, we develop a novel mechanochemical cytoskeleton model to investigate the mechanical behavior of cells. Interestingly, we find that active stress fibers exhibit diverse dynamical modes at specific inhibitor concentration thresholds. The existence of concentration differences and sustained mechanochemical feedback in activators and inhibitors trigger a global oscillation of isolated cells. In addition, under uniaxial and biaxial stretches, activators and inhibitors preferentially diffuse toward the more significantly deformed cytoskeletal elements, and their dynamic interactions regulate the cell to align with the main stretching direction. Our findings, consistent with many experimental results, provide fundamental insights into cytoskeletal remodeling and cellular mechanosensing mechanisms.