Temperature promotes selectivity during electrochemical CO reduction on NiO:SnO nanofibers.

Electrolyzers operate over a range of temperatures; hence, it is crucial to design electrocatalysts that do not compromise the product distribution unless temperature can promote selectivity. This work reports a synthetic approach based on electrospinning to produce NiO:SnO nanofibers (NFs) for selectively reducing CO to formate above room temperature. The NFs comprise compact but disjoined NiO and SnO nanocrystals identified with STEM.

3D topographies promote macrophage M2d-Subset differentiation.

cellular models denote a crucial part of drug discovery programs as they aid in identifying successful drug candidates based on their initial efficacy and potency. While tremendous headway has been achieved in improving 2D and 3D culture techniques, there is still a need for physiologically relevant systems that can mimic or alter cellular responses without the addition of external biochemical stimuli. A way forward to alter cellular responses is using physical cues, like 3D topographical inorganic substrates, to differentiate macrophage-like cells.

Tuning the catalytic acidity in AlO nanofibers with mordenite nanocrystals for dehydration reactions.

Alumina (AlO) is one of the most used supports in the chemical industry due to its exceptional thermal stability, surface area, and acidic properties. Mesoscopic structured alumina with adequate acidic properties is important in catalysis to enhance the selectivity and conversion of certain reactions and processes. This study introduces a synthetic method based on electrospinning to produce AlO nanofibers (ANFs) with zeolite mordenite (MOR) nanocrystals (hereafter, hybrid ANFs) to tune the textural and surface acidity properties.

Filtering efficiency model that includes the statistical randomness of non-woven fiber layers in facemasks.

Facemasks have become important tools to fight virus spread during the recent COVID-19 pandemic, but their effectiveness is still under debate. We present a computational model to predict the filtering efficiency of an N95-facemask, consisting of three non-woven fiber layers with different particle capturing mechanisms. Parameters such as fiber layer thickness, diameter distribution, and packing density are used to construct two-dimensional cross-sectional geometries.

A wafer-scale fabrication method for three-dimensional plasmonic hollow nanopillars.

Access to nanofabrication strategies for crafting three-dimensional plasmonic structures is limited. In this work, a fabrication strategy to produce 3D plasmonic hollow nanopillars (HNPs) using Talbot lithography and I-line photolithography is introduced. This method is named subtractive hybrid lithography (SHL), and permits intermixed usage of nano-and-macroscale patterns.