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.     

Insights into Protein Stability in Cell Lysate by 19F NMR Spectroscopy

In living organisms, protein folding and function take place in an inhomogeneous, highly crowded environment possessing a concentration of diverse macromolecules of up to 400 g/L. It has been shown that the intracellular environment has a pronounced effect on the stability, dynamics and function of the protein under study, and has for this reason to be considered. However, most protein studies neglect the presence of these macromolecules. Consequently, we probe here the overall thermodynamic stability of cold shock protein B from Bacillus subtilis (BsCspB) in cell lysate. We found that an increase in cell lysate concentration causes a monotonic increase in the thermodynamic stability of BsCspB. This result strongly underlines the importance of considering the biological environment when inherent protein parameters are quantitatively determined. Moreover, we demonstrate that targeted application of ¹⁹F NMR spectroscopy operates as an ideal tool for protein studies performed in complex cellular surroundings.     

Self‐Assembly of Atomically Thin Chiral Copper Heterostructures Templated by Black Phosphorus

The fabrication of 2D systems for electronic devices is not straightforward, with top‐down low‐yield methods often employed leading to irregular nanostructures and lower quality devices. Here, a simple and reproducible method to trigger self‐assembly of arrays of high aspect‐ratio chiral copper heterostructures templated by the structural anisotropy in black phosphorus (BP) nanosheets is presented. Using quantitative atomic resolution aberration‐corrected scanning transmission electron microscopy imaging, in situ heating transmission electron microscopy and electron energy‐loss spectroscopy arrays of heterostructures forming at speeds exceeding 100 nm s^?1 and displaying long‐range order over micrometers are observed. The controlled instigation of the self‐assembly of the Cu heterostructures embedded in BP is achieved using conventional electron beam lithography combined with site specific placement of Cu nanoparticles. Density functional theory calculations are used to investigate the atomic structure and suggest a metallic nature of the Cu heterostructures grown in BP. The findings of this new hybrid material with unique dimensionality, chirality, and metallic nature and its triggered self‐assembly open new and exciting opportunities for next generation, self‐assembling devices.     

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states'' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.     

Stabilising and Trimming 3D Woven Fabrics for Composite Preforming Applications

The work presented here focusses on the developments in the stabilising and trimming of 3D woven preforms. Dry fibre preforms are notoriously difficult to trim; once a fabric is cut, it loses its edge stability and consequently the fabric frays. The result is an unstable fabric which can easily be displaced/ distorted prior to composite manufacturing. In this work, three stabilisation and three trimming techniques were investigated. Of the stabilisation techniques these included powder binder, thermoplastic binder yarn (activated to give fabric stabilization); and polyester stitching. The stabilised fabrics were trimmed to near-net-shape using different trimming techniques. The trimming techniques investigated were laser, clicker press and ultrasonic knife. Each stabilisation method was trialled with each trimming method to assess the most suitable combination. The assessment of quality and suitability was made by observing the level of stabilisation, amount of fraying fibres, quality of the cut, ease of application and repeatability of the process. This paper details the assessments made for each combination alongside practical application conclusions. The key findings were; cutting by means of a laser is capable of sealing the fabric edges, producing high edge quality. Stitching as a method of stabilising is not sufficient in preventing fibres from moving during the cutting process, hence producing an unclean cut.     

Random Alloyed versus Intermetallic Nanoparticles: A Comparison of Electrocatalytic Performance

As synthetic methods advance for metal nanoparticles, more rigorous studies of structure–function relationships can be made. Many electrocatalytic processes depend on the size, shape, and composition of the nanocatalysts. Here, the properties and electrocatalytic behavior of random alloyed and intermetallic nanoparticles are compared. Beginning with an introduction of metallic nanoparticles for catalysis and the unique features of bimetallic compositions, the discussion transitions to case studies of nanoscale electrocatalysts where direct comparisons of alloy and intermetallic compositions are undertaken for methanol electrooxidation, formic acid electrooxidation, the oxygen reduction reaction, and the electroreduction of carbon dioxide (CO₂). Design and synthesis strategies for random alloyed and intermetallic nanoparticles are discussed, with an emphasis on Pt–M and Cu–M compositions as model systems. The differences in catalytic performance between alloys and intermetallic nanoparticles are highlighted in order to provide an outlook for future electrocatalyst design.     

Cancer-Targeting Nanoparticles for Combinatorial Nucleic Acid Delivery

Nucleic acids are a promising type of therapeutic for the treatment of a wide range of conditions, including cancer, but they also pose many delivery challenges. For efficient and safe delivery to cancer cells, nucleic acids must generally be packaged into a vehicle, such as a nanoparticle, that will allow them to be taken up by the target cells and then released in the appropriate cellular compartment to function. As with other types of therapeutics, delivery vehicles for nucleic acids must also be designed to avoid unwanted side effects; thus, the ability of such carriers to target their cargo to cancer cells is crucial. Classes of nucleic acids, hurdles that must be overcome for effective intracellular delivery, types of nonviral nanomaterials used as delivery vehicles, and the different strategies that can be employed to target nucleic acid delivery specifically to tumor cells are discussed. Additonally, nanoparticle designs that facilitate multiplexed delivery of combinations of nucleic acids are reviewed.     

RETRACTED ARTICLE: Tolerance rough set firefly-based quick reduct

In medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.