Key Assumptions Used in the Calculation of Sustainability Parameters for Metals Processing Operations

JOM - No matter how sophisticated a model for prediction of important sustainability parameters, a number of assumptions must be made to yield the desired information. Some of these assumptions are...     

Molybdenum Dioxide in Carbon Nanoreactors as a Catalytic Nanosponge for the Efficient Desulfurization of Liquid Fuels

The principle of a “catalytic nanosponge” that combines the catalysis of organosulfur oxidation and sequestration of the products from reaction mixtures is demonstrated. Group VI metal oxide nanoparticles (CrO_x, MoO_x, WO_x) are embedded within hollow graphitized carbon nanofibers (GNFs), which act as nanoscale reaction vessels for oxidation reactions used in the decontamination of fuel. When immersed in a model liquid alkane fuel contaminated with organosulfur compounds (benzothiophene, dibenzothiophene, dimethyldibenzothiophene), it is found that MoO₂@GNF nanoreactors, comprising 30 nm molybdenum dioxide nanoparticles grown within the channel of GNFs, show superior abilities toward oxidative desulfurization (ODS), affording over 98% sulfur removal at only 5.9 mol% catalyst loading. The role of the carbon nanoreactor in MoO₂@GNF is to enhance the activity and stability of catalytic centers over at least 5 cycles. Surprisingly, the nanotube cavity can selectively absorb and remove the ODS products (sulfoxides and sulfones) from several model fuel systems. This effect is related to an adsorptive desulfurization (ADS) mechanism, which in combination with ODS within the same material, yields a “catalytic nanosponge” MoO₂@GNF. This innovative ODS and ADS synergistic functionality negates the need for a solvent extraction step in fuel desulfurization and produces ultralow sulfur fuel.     

Special Issue: Technological Advancements in Wireless and Optical Communication Systems

Wireless Personal Communications -     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.     

Correction to: Comparative Analysis of Processing Techniques’ Effect on the Strength of Carbon Black (N220)-Filled Poly(Lactic Acid) Composites

Strength of Materials - We apologize to the authors and readers for these technical lapses made by the journal staff.     

NanoPaint: A Tool for Rapid and Dynamic Imaging of Membrane Structural Plasticity at the Nanoscale

Single‐particle tracking with quantum dots (QDs) constitutes a powerful tool to track the nanoscopic dynamics of individual cell membrane components unveiling their membrane diffusion characteristics. Here, the nano‐resolved population dynamics of QDs is exploited to reconstruct the topography and structural changes of the cell membrane surface with high temporal and spatial resolution. For this proof‐of‐concept study, bright, small, and stable biofunctional QD nanoconstructs are utilized recognizing the endogenous neuronal cannabinoid receptor 1, a highly expressed and fast‐diffusing membrane protein, together with a commercial point‐localization microscope. Rapid QD diffusion on the axonal plasma membrane of cultured hippocampal neurons allows precise reconstruction of the membrane surface in less than 1 min with a spatial resolution of tens of nanometers. Access of the QD nanoconstructs to the synaptic cleft enables rapid 3D topological reconstruction of the entire presynaptic component. Successful reconstruction of membrane nano‐topology and deformation at the second time‐scale is also demonstrated for HEK293 cell filopodia and axons. Named “nanoPaint,” this super‐resolution imaging technique amenable to any endogenous transmembrane target represents a versatile platform to rapidly and accurately reconstruct the cell membrane nano‐topography, thereby enabling the study of the rapid dynamic phenomena involved in neuronal membrane plasticity.     

Ultrathin Dual‐Scale Nano‐ and Microporous Membranes for Vascular Transmigration Models

Selective cellular transmigration across the microvascular endothelium regulates innate and adaptive immune responses, stem cell localization, and cancer cell metastasis. Integration of traditional microporous membranes into microfluidic vascular models permits the rapid assay of transmigration events but suffers from poor reproduction of the cell permeable basement membrane. Current microporous membranes in these systems have large nonporous regions between micropores that inhibit cell communication and nutrient exchange on the basolateral surface reducing their physiological relevance. Here, the use of 100 nm thick continuously nanoporous silicon nitride membranes as a base substrate for lithographic fabrication of 3 µm pores is presented, resulting in a highly porous (≈30%), dual‐scale nano‐ and microporous membrane for use in an improved vascular transmigration model. Ultrathin membranes are patterned using a precision laser writer for cost‐effective, rapid micropore design iterations. The optically transparent dual‐scale membranes enable complete observation of leukocyte egress across a variety of pore densities. A maximal density of ≈14 micropores per cell is discovered beyond which cell–substrate interactions are compromised giving rise to endothelial cell losses under flow. Addition of a subluminal extracellular matrix rescues cell adhesion, allowing for the creation of shear‐primed endothelial barrier models on nearly 30% continuously porous substrates.