Inclusions properties at 1673 K and room temperature with Ce addition in SS400 steel

A rapid and specific antimicrobial resistance detection of Escherichia coli via magnetic nanoclusters

Drinking water contamination, often caused by bacteria, leads to substantial numbers of diarrhea deaths each year, especially in developing regions. Human urine as a source of fertilizer, when handled improperly, can contaminate drinking water. One dominant bacterial pathogen in urine is Escherichia coli, which can trigger serious waterborne/foodborne diseases. Considering the prevalence of the multi-drug resistant extended-spectrum beta-lactamase (ESBL) producing E. coli, a rapid detection method for resistance is highly desired. In this work, we developed a method for quick identification of E. coli and, at the same time, capable of removal of general bacterial pathogens from human urine. A specific peptide GRHIFWRRGGGHKVAPR, reported to have a strong affinity to E. coli, was utilized to modify the PEGylated magnetic nanoclusters, resulting in a specific capture and enrichment of E. coli from the bacteria-spiked artificial urine. Subsequently, a novel luminescent probe was applied to rapidly identify the antimicrobial resistance of the collected E. coli within 30 min. These functionalized magnetic nanoclusters demonstrate a promising prospect to rapidly detect ESBL E. coli in urine and contribute to reducing drinking water contamination.

Ultrafast Determination of Antimicrobial Resistant Staphylococcus aureus Specifically Captured by Functionalized Magnetic Nanoclusters

Sepsis, the systemic response to infection, is a life-threatening situation for patients and leads to high mortality, especially when caused by antimicrobial resistant pathogens. Prompt diagnosis and identification of the pathogenic bacteria, including their antibiotic resistance, are highly desired to yield a timely decision for treatment. Here, we aim to develop a platform for rapid isolation and efficient identification of Staphylococcus aureus, the most frequently occurring pathogen in sepsis. A peptide (VPHNPGLISLQG, SA5-1), specifically binding to S. aureus, was conjugated to the PEGylated magnetic nanoclusters, successfully enabling the specific capture and enrichment of S. aureus from blood serum. Consequently, fast detection of the antimicrobial resistance of the collected S. aureus was achieved within 30 min using a novel luminescent probe. These magnetic nanoclusters manifest a promising diagnostic prospect to combat sepsis.

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed, e.g., ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties.

Effects of Cooling Rate during Quenching and Tempering Conditions on Microstructures and Mechanical Properties of Carbon Steel Flange

This study investigated the mechanical properties of steel in flanges, with the goal of obtaining high strength and high toughness. Quenching was applied alone or in combination with tempering at one of nine combinations of three temperatures T_TEM and durations t_TEM. Cooling rates at various flange locations during quenching were first estimated using finite element method simulation, and the three locations were selected for mechanical testing in terms of cooling rate. Microstructures of specimens were observed at each condition. Tensile test and hardness test were performed at room temperature, and a Charpy impact test was performed at −46 °C. All specimens had a multiphase microstructure composed of matrix and secondary phases, which decomposed under the various tempering conditions. Decrease in cooling rate (CR) during quenching caused reduction in hardness and strength but did not affect low-temperature toughness significantly. After tempering, hardness and strength were reduced and low-temperature toughness was increased. Microstructures and mechanical properties under the various tempering conditions and CRs during quenching were discussed. This work was based on the properties directly obtained from flanges under industrial processes and is thus expected to be useful for practical applications.

Study of Bauschinger effect of acicular ferrite and polygonal ferrite through ex-situ interrupted bending tests in API X80 linepipe steels

Linepipe steels complexly consisted of low-temperature transformation microstructures of bainitic ferrite, granular bainite, and acicular ferrite (AF) as well as polygonal ferrite (PF) which individually affect the Bauschinger effect occurring during the pipe-forming. In this study, microscopic analyses of electron back-scattered diffraction (EBSD) coupled with tension-compression and interrupted bending tests were performed for verification of the Bauschinger effect of AF and PF working as major microstructures in single-phase- and two-phase-rolled API X80 steels, respectively. With respect to microstructural effects on Bauschinger effect, the reduction in mobile dislocation density during the flattening was smaller in the AF than in the PF. However, the dislocation pile-up at low-angle substructures and high-angle grain boundaries was more frequently observed, thereby leading to the higher back stress and Bauschinger effect in the AF. Boundary kernel average misorientation (KAM) profile played a critical role in determining the Bauschinger effect because they were closely related with the back stress. Thus, the Bauschinger effect was higher in the single-phase-rolled steel than in the two-phase-rolled steel. The present ex-situ interrupted bending methods coupled with EBSD analyses are outstanding ones for the detailed explanation of Bauschinger effect and provide an important idea for the yield strength designs of linepipe steels.

Assessment of plant species diversity based on hyperspectral indices at a fine scale

Fast and nondestructive approaches of measuring plant species diversity have been a subject of excessive scientific curiosity and disquiet to environmentalists and field ecologists worldwide. In this study, we measured the hyperspectral reflectances and plant species diversity indices at a fine scale (0.8 meter) in central Hunshandak Sandland of Inner Mongolia, China. The first-order derivative value (FD) at each waveband and 37 hyperspectral indices were used to assess plant species diversity. Results demonstrated that the stepwise linear regression of FD can accurately estimate the Simpson (R² = 0.83), Pielou (R² = 0.87) and Shannon-Wiener index (R² = 0.88). Stepwise linear regression of FD (R² = 0.81, R² = 0.82) and spectral vegetation indices (R² = 0.51, R² = 0.58) significantly predicted the Margalef and Gleason index. It was proposed that the Simpson, Pielou and Shannon-Wiener indices, which are widely used as plant species diversity indicators, can be precisely estimated through hyperspectral indices at a fine scale. This research promotes the development of methods for assessment of plant diversity using hyperspectral data.

Nucleation and Ostwald Growth of Particles in Fe-O-Al-Ca Melt

Tremendous focus has been put on the control of particle size distribution which effects the grain structure and mechanical properties of resulting metallic materials, and thus nucleation and growth of particles in solution should be clarified. This study uses classical nucleation theory and Ostwald ripening theory to probe the relationship between the compositions of Fe-O-Al-Ca melts and the behavior of particles under the condition of no external stirring. Our experimental data suggest that decreasing the initial Ca addition and Al addition is conductive to the increase of nucleation rate for calcium aluminate particles, which exhibits a same change trend with that predicted from classical nucleation theory. Based on the experimental evidence for particles size distribution in three-dimensional, we demonstrate that Ostwald ripening is the predominate mechanism on the coarsening of particles in Fe-O-Al-Ca melt at early stage of deoxidation under the condition of no external stirring but not at later stage.