Cancer‐Targeting Graphene Quantum Dots: Fluorescence Quantum Yields,Stability, and Cell Selectivity

Folic acid, due to its high affinity toward folate receptors (FR), is recognized as one of the most promising cancer targeting vectors. However, the inherent defects of low water solubility (1.6 µg mL^?1), high sensitivity toward photo‐bleaching, low fluorescent quantum yields (QYs, <0.5%) seriously limit its practical application. Herein, ultrastable, highly luminescent graphene quantum dots (GQDs) that selectively target diverse cancer cells are prepared and tested. The new GQDs present step changes compared to common folic acid through an ≈6250 times increase in water solubility (to ≈10 mg mL^?1), more than 150 times in QYs (up to ≈77%), while maintaining luminescence stability up to 98% when subjected to UV, visible light, and heating over 360 min. It is shown that the suppression of nonradiative transitions by amino groups pyrolyzed from pterin plays a key role in the mechanism of high QYs and excellent stability. The functional groups that are likely responsible for the selective targeting of cancer cells with different levels of folate receptor expression on the surface are identified. Collectively with these promising properties, the new functional graphene quantum dots may open a new avenue for cancer diagnosis, drug delivery, and therapies.     

Nanocrystalline CoCrFeNiMn high-entropy alloy with tunable ferromagnetic properties

A nanocrystalline CoCrFeNiMn high-entropy alloy(nc-HEA)with nano-multiphase structure was pre-pared by inert gas condensation(IGC)using a laser evaporation source.Encouragingly,the laser-IGC nc-HEA exhibits unexpected ferromagnetic behavior and the Curie temperature(Tc)increased nearly 10 times compared to any CoCrFeNiMn HEAs prepared by various other methods.In addition,the saturation magnetization(Ms)and Tc of the laser-IGC nc-HEA can be controlled via heat treatment,which is result-ing from the formation and structural evolution of magnetic nanophases during annealing.This work widens the design toolbox for high-performance nc-HEAs based upon laser-IGC technique.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

Improving of Wavelength Division Multiplexing Based on Free Space Optical Communication via Power Comparative System

Wireless Personal Communications - The names of the second and third authors in the initial online publication were not correctly typeset. The original article has been corrected.     

On the improvement of a scalable sparse direct solver for unsymmetrical linear equations

This paper focuses on the application level improvements in a sparse direct solver specifically used for large-scale unsymmetrical linear equations resulting from unstructured mesh discretization of coupled elliptic/hyperbolic PDEs. Existing sparse direct solvers are designed for distributed server systems taking advantage of both distributed memory and processing units. We conducted extensive numerical experiments with three state-of-the-art direct linear solvers that can work on distributed-memory parallel architectures; namely, MUMPS (MUMPS solver website, http://graal.ens-lyon.fr/MUMPS), WSMP (Technical Report TR RC-21886, IBM, Watson Research Center, Yorktown Heights, 2000), and SUPERLU_DIST (ACM Trans Math Softw 29(2):110–140, 2003). The performance of these solvers was analyzed in detail, using advanced analysis tools such as Tuning and Analysis Utilities (TAU) and Performance Application Programming Interface (PAPI). The performance is evaluated with respect to robustness, speed, scalability, and efficiency in CPU and memory usage. We have determined application level issues that we believe they can improve the performance of a distributed-shared memory hybrid variant of this solver, which is proposed as an alternative solver [SuperLU_MCDT (Many-Core Distributed)] in this paper. The new solver utilizing the MPI/OpenMP hybrid programming is specifically tuned to handle large unsymmetrical systems arising in reservoir simulations so that higher performance and better scalability can be achieved for a large distributed computing system with many nodes of multicore processors. Two main tasks are accomplished during this study: (i) comparisons of public domain solver algorithms; existing state-of-the-art direct sparse linear system solvers are investigated and their performance and weaknesses based on test cases are analyzed, (ii) improvement of direct sparse solver algorithm (SuperLU_MCDT) for many-core distributed systems is achieved. We provided results of numerical tests that were run on up to 16,384 cores, and used many sets of test matrices for reservoir simulations with unstructured meshes. The numerical results showed that SuperLU_MCDT can outperform SuperLU_DIST 3.3 in terms of both speed and robustness.     

Study on optimization of the circumferential and axial wavy geometrical configuration of hydrodynamic journal bearing

This paper is focused on using GA genetic algorithm to find the optimal performance with respect to shape optimization in three dimensions for the hydrodynamic journal bearing. The mathematical model for film thickness was drawn using Fourier series function and axial waviness value ( $\bar \Delta $ ) D to represent the journal bearing in circumferential and axial direction, respectively. The objective was then to determine the Fourier coefficients and axial waviness value ( $\bar \Delta $ ) D that maximized the load capacity subjected to a given set of constraint. Optimized results show that the presence of cos wave in axial direction, with a positive dimensionless amplitude (+A) and waviness number m = 0.633, improves the load capacity by (8–10) % over the cylindrical plain bearing with the same arbitrary shape and size; in general, the increasing order of Fourier series (n), an axial dimensionless amplitude and L/D ratio cause the change in load capacity to become more evident.     

Mannose based polymeric nanoparticles for lectin separation

The aim of this work is to synthesize the original, new polymeric nanoparticles for concanavalin A (Con A) purification. Nanoparticles were synthesized by surfactant free emulsion polymerization. In the polymerization prosedure, 1-O-(2′-hydroxy-3′-acryloyloxypropyl)-2,3:5,6-di-O-isopropylidene-α-D-mannofuranose (Man-OPA) was used as co-monomer and 2-hydroxyethylmethacrylate (HEMA) was used as a monomer. Man-OPA was characterized by Fourier Transform Infrared Spectroscopy (FTIR), nuclear magnetic resonance and elemental analysis techniques. Poly(HEMA-Man-OPA) nanoparticles were characterized by scanning electron microscopy, FTIR and Zeta Sizer. In adsorption?desorption experiments, maximum Con A adsorption capacity of poly(HEMA-Man-OPA) nanoparticles was found 630.6 mg/g nanoparticle (pH 7.5, 1.0 mg/mL). Adsorption?desorption experiments were repeated in four times. According to results, these nanoparticles could be used several times without significant decrease in Con A adsorption capacity.     

Binary Cellulose Nanocrystal Blends for Bioinspired Damage Tolerant Photonic Films

Most attempts to emulate the mechanical properties of strong and tough natural composites using helicoidal films of wood‐derived cellulose nanocrystals (w‐CNCs) fall short in mechanical performance due to the limited shear transfer ability between the w‐CNCs. This shortcoming is ascribed to the small w‐CNC‐w‐CNC overlap lengths that lower the shear transfer efficiency. Herein, we present a simple strategy to fabricate superior helicoidal CNC films with mechanical properties that rival those of the best natural materials and are some of the best reported for photonic CNC materials thus far. Assembling the short w‐CNCs with a minority fraction of high aspect ratio CNCs derived from tunicates (t‐CNCs), we report remarkable simultaneous enhancement of all in‐plane mechanical properties and out‐of‐plane flexibility. The important role of t‐CNCs is revealed by coarse grained molecular dynamics simulations where the property enhancement are due to increased interaction lengths and the activation of additional toughening mechanisms. At t‐CNC contents greater than 5% by mass the mixed films also display UV reflecting behaviour. These damage tolerant optically active materials hold great promise for application as protective coatings. More broadly, we expect the strategy of using length‐bidispersity to be adaptable to mechanically enhancing other matrix‐free nanoparticle ensembles.     

Interfacial delamination of thin-film PTFE (polytetrafluoroethylene) coatings

Interfacial adhesion is a major concern with respect to successful performance of thin polymer films in developing new thin-film processes. Micro-indentation was used to induce interfacial delamination of polytetrafluoroethylene (PTFE) films deposited on glass substrates using hot filament chemical vapour deposition (HFCVD). Film thickness (1, 2, 3, 5, 10 µm) and indentation load (0.5, 0.75, 1, 2, 3 N) effects on the delamination diameter were investigated. A three-dimensional finite element model using shear material failure criterion and cohesive zone model (CZM) was developed to simulate the delamination. A normalized load–delamination radius relationship was obtained to evaluate the interfacial fracture toughness. The experimental observations showed that the delamination diameter depends on film thickness and indentation load. The numerical simulation indicates the delamination diameter depends on film thickness, material properties, and indentation force. The predictions of interfacial fracture toughness for 5- and 10-µm PTFE films are much smaller than those values using Rosenfeld et al.’s equation, which excludes the energy spent during the penetration.     

Source apportionment of PM(10) and PM(2.5) using positive matrix factorization and chemical mass balance in Izmir, Turkey

Atmospheric particulate matter (PM) fractions (PM(10) and PM(2.5)) were sampled concurrently between June 2004 and May 2005 at two sites (urban and suburban) in Izmir, Turkey. The elemental composition of PM (Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Sr, V, and Zn) was determined using inductively coupled plasma-optical emission spectrometer. Elemental compositions of several PM sources were also characterized. Positive matrix factorization (PMF) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions to air concentrations. The major contributors to PM were fossil fuel burning, traffic emissions, mineral industries and marine salt according to the PMF results. However, undetermined parts were more than 40%. On the other hand, the contributions to PM could be determined completely by CMB, and the dominant contributor was traffic with >70% at the two sites. Fossil fuel burning, mineral industries, marine salt and natural gas-fired power plant were the minor contributors.