Nanopatterned silk fibroin-based optical films exhibit both ultrahigh optical transparency and ultrahigh optical transmission haze.

We treat the dielectric decrement induced by excess ion polarization as a source of ion specificity and explore its impact on electrokinetics. We employ a modified Poisson-Nernst-Planck (PNP) equations accounting for the dielectric decrement. The dielectric decrement is determined by the excess ion polarization parameter α and when α = 0 the standard PNP model is recovered. Our model shows that ions saturate at large zeta potentials (ζ). Because of ion saturation, a condensed counterion layer forms adjacent to the charged surface, introducing a new length scale, the thickness of the condensed layer (lc). For the electro-osmotic mobility, the dielectric decrement weakens the electro-osmotic flow owing to the decrease of the dielectric permittivity. At large ζ, when α ≠ 0, the electro-osmotic mobility is found to be proportional to ζ/2, in contrast to ζ predicted by the standard PNP model. This is attributed to ion saturation at large ζ. In terms of the electrophoretic mobility Me, we carry out both an asymptotic analysis in the thin-double-layer limit and solve the full modified PNP model to compute Me. Our analysis reveals that the impact of the dielectric decrement is intriguing. At small and moderate ζ, the dielectric decrement decreases Me with an increasing α. At large ζ, it is well known that the surface conduction becomes significant and plays an important role in determining Me. It is observed that the dielectric decrement effectively reduces the surface conduction. Hence in stark contrast, Me increases as α increases. Our predictions of the contrast dependence of the mobility on α at different zeta potentials qualitatively agree with experimental results on the dependence of the mobility among ions and provide a possible explanation for such ion specificity. Finally, the comparisons between the thin-double-layer asymptotic analysis and the full simulations of the modified PNP model suggest that at large ζ the validity of the thin-double-layer approximation is determined by lc rather than the traditional Debye length.

By combining spin coating and inverse nanotransfer printing, silica-coated gold nanoparticles are patterned onto polydimethylsiloxane (PDMS) superhydrophobic surfaces to form a hierarchical structure. A layer of nanoparticles is spin-coated on a flat silicon substrate serving as the stamp, which is then transferred to the raised regions of PDMS surfaces. Our inverse nanotransfer printing is in contrast to the standard nanotransfer printing, which transfers metal from the raised regions of a stamp to a flat PDMS surface. The fabricated hierarchical surface exhibits a higher contact angle and delays the Cassie-Wenzel transition during evaporation of a sessile droplet, indicating an improvement of superhydrophobicity. Finally, we demonstrate that the fabricated nanoparticle-based superhydrophobic surfaces can enhance the Raman intensity and significantly decrease the surface-enhanced Raman scattering detection limit.

High-efficient light-management nanostructures are critical to various optical applications. However, in practical implementation, these structures have been limited by the need to resist mechanical abrasion, erosion, chemical exposure, ultraviolet radiation, and performance deterioration by dust accumulation. To address these critical technological gaps, we herein report a conceptually different approach, employing a hierarchical nanostructure embedded with multilayer LightScribe-etched graphene, capable of omnidirectional broadband light management with both high optical transparency (>90%) and high haze (∼89%), ideal for photovoltaics, which simultaneously demonstrates extraordinary robustness to various environmental challenges ranging from mechanical abrasion, UV exposure, corrosions, outdoor exposures to resistance to dust accumulation. The reported nanostructures can be readily combined to any optoelectrical device’s surface, and the practical tests on coated amorphous silicon solar cells show that it outperforms the state-of-the-art commercial coating by maintaining both 10% efficiency improvement along with the prevention of dust accumulation in contrast to 56.2% efficiency degradation with the commercial coating after the 1 month outdoor test.

Micron and sub-micron particles tend to be self-organized into well-defined arrays during conventional convective assembly, however this technique hardly works for the assembly of tiny nanoparticles smaller than 10 nm into monolayered arrays. The intrinsic difficulty is mainly related to the evaporation and convections occurring at a much larger volume than nanoparticle size. As a result, multilayered nanoparticle arrays are usually created. In this paper, we report that solvent evaporation and nanoparticle convection are artificially confined in nanoscale grooves near a pipette's periphery and the grooves can help assemble parallel strips because of the uni-directional translation of the pipette. The stacking layer of each strip is correlated with the grooves' diameter, thus the nanoparticle monolayer is fabricated in the tiny grooves. The narrowest strip width observed experimentally is geometrically limited by the effective diameter of the charged nanoparticles in gold colloid because nanoparticle convections from the inner chamber to the pipette's periphery will not occur within the grooves finer than the effective diameters.

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Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.

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