Molecular dynamics simulations of friction behaviours on nano-textured silicon surfaces

Molecular dynamics simulations have been applied to study the friction behaviours of nano-textured silicon surfaces. The effects of texture shape, texture pitch and indenter size on forces, temperature, stress and plastic deformation are investigated. It is found that the presence of the texture facilitates the reduction of friction due to the decrease the contact area. The texture shape significantly influences the tribological properties of the textured surface. The hemispherical texture has the optimum friction reduction effect, followed by cylindrical texture and lastly by cubic texture. The number of atoms that undergo phase transformation in the scratching is the maximal for the cubic texture while the smallest for the hemispherical texture. However, the texture pitch has little effect on the tribological properties of the textured surface. In addition, it is interesting to observe the indenter size effect that a larger indenter causes a smaller force and wear volume at the initial stage of scratching. The indenter size effect on tribological properties results from the variation of contact area in the scratching. The insights gained can shed light on the friction mechanism of nanoscale textured silicon surface and are beneficial to the design of micro/nanoscale devices such as micro/nanoelectromechanical systems with surface textures.</p

Molecular Dynamics Simulation of Nanodroplets Impacting Stripe-Textured Surfaces

The dynamic behavior of droplets impacting on textured surfaces has an important influence on many engineering applications, such as anti-icing and self-cleaning. However, the mechanism and law of the effect of textured surfaces on the impact behavior of nanodroplets has not been fully revealed yet. In this paper, the molecular dynamics (MD) method is used to model the dynamic behavior of nanodroplets after impacting the solid surface with a striped texture. The influences of texture gap and texture angle on the real contact area, spreading factor, contact time, and bounce velocity of the droplet after impact are also quantitatively analyzed. It is shown that the striped texture produces significant anisotropy in the spreading and contraction behavior of nanodroplets after impact, and the anisotropy is more pronounced on the ridged texture surface than on the grooved texture surface. In addition, we find that the texture gap has little effect on the dynamic behavior of nanodroplets impacting the textured surface. However, as the bottom angle of the texture increases, the real contact area and bounce velocity of the nanodroplet increase significantly, while the contact time and spreading factor decrease. This work further elucidates the characteristics and mechanisms of nanodroplets impacting on stripe-textured surfaces and provides a theoretical basis for the design of nanostructured surfaces in relevant applications

The heatmap of the clinical features of cases in the low- and high-DEPDC1 expression group in the TCGA cohort.

(*P P P < 0.001). (A) LUAD. (B) LUSC. (C) NSCLC.</p

The expression levels of DEPDC1 in different cancers based on TIMER2.0 database.

The expression levels of DEPDC1 in different cancers based on TIMER2.0 database.</p

Analysis of DEPDC1 signaling pathway and immune-related genes (*<i>P</i> < 0.05, **<i>P</i> < 0.01, ***<i>P</i> < 0.001).

(A) Gene enrichment plot of multiple pathways in GSEA. (B) Protein levels of DEPDC1, P53 and BAX in response to the treatment of siRNA-NC and siRNA-DEPDC1. (C) Deviation plot of the top 20 immune-related genes significantly associated with DEPDC1. (D) The expression levels of NRAS, TNFSF12, CD81 and BIRC5 mRNAs after the treatment of siRNA-NC and siRNA-DEPDC1. (E) GO analysis of DEPDC1 and immune-related genes. (F) KEGG pathways items of DEPDC1 and immune-related genes.</p

DEPDC1 inhibits NSCLC cell proliferation and enhances their apoptosis (*<i>P</i> < 0.05, **<i>P</i> < 0.01, ***<i>P</i> < 0.001).

(A) DEPDC1 mRNA expression within BEAS-2B and NCI-H1299 cells. (B) DEPDC1 protein expression within BEAS-2B and NCI-H1299 cells. (C) DEPDC1 mRNA expression within NCI-H1299 cells after siRNA-NC and siRNA-DEPDC1 transfection. (D) DEPDC1 protein expression within NCI-H1299 cells after siRNA-NC and siRNA-DEPDC1 transfection. (E) MTS assay conducted to analyze NCI-H1299 cell proliferation at 0/24/48/72h after siRNA-NC and siRNA-DEPDC1 transfection. (F) NCI-H1299 cell apoptosis after siRNA-NC and siRNA-DEPDC1 transfection.</p

pone.0294227.t001 - Comprehensive analysis and validation reveal DEPDC1 as a potential diagnostic biomarker associated with tumor immunity in non-small-cell lung cancer

pone.0294227.t001 - Comprehensive analysis and validation reveal DEPDC1 as a potential diagnostic biomarker associated with tumor immunity in non-small-cell lung cancer</p

Forest plots and the sROC curve based on GEO and TCGA data.

(A) Forest plot of sensitivity. (B) Forest plot of specificity. (C) Forest plot of positive likelihood ratio. (D) Forest plot of negative likelihood ratio. (E) Forest plot of odds ratio. (F) The sROC curve.</p

Relationship between the expression of DEPDC1 and clinicopathological features in NSCLC patients from GEO.

Relationship between the expression of DEPDC1 and clinicopathological features in NSCLC patients from GEO.</p

Continuous variable meta-analysis of GEO data.

(A) Forest plot. (B) Sensitivity analysis. (C) Subgroup analysis based on cancer type. (D) Funnel plot.</p