Search

The second most potent forcer of climate change, soot, has severe harmful effects on both human health and the environment. Accurate numerical modeling of soot formation is extremely complex and has a high computational cost due to its dependence on many physical and chemical interactions, specifically in turbulent flames. The high computational cost of coupling chemistry, fluid dynamics, thermodynamics, and heat transfer raise the need for a novel, precise, and computationally cost-effective numerical technique for predicting soot concentrations. This study applies machine learning (ML) to predict soot formation in a turbulent flame. It has been discovered that the local soot volume fraction is correlated to the histories of gas properties strongly correlative to soot formation and oxidation. A library with the Lagrangian temporal histories of soot-containing fluid parcels is created from turbulent diffusion flame data computed using direct numerical simulation (DNS). This library is then used to train an ML algorithm to predict soot volume fraction along randomly selected trajectories (pathlines) in the domain. The prediction capability is tested over 10% of the entire dataset, and it is seen that soot volume fraction can be predicted well along the selected pathlines with low error and computational cost. To describe quantitative results, the calculated R

In this research, for the first time, the QuEChERS was combined with the dispersive liquid–liquid microextraction based on deep eutectic solvent (QuEChERS-DES-DLLME). The method was developed for efficient extraction, clean-up and preconcentration of acetamiprid (ACE) and imidacloprid (IMI), two commonly used pesticides, from pistachio samples before their determination by HPLC–UV. The pesticides were firstly extracted from pistachio samples and cleaned-up by QuEChERS, and then their supernatant was used as a dispersant in following DLLME in order to further purification and preconcentration. The extraction solvent used in DLLME was based on deep eutectic solvents (DESs). Preparation of hydrophobic DES was done by mixing amyl alcohol as hydrogen bond donor and trioctylmethylammonium chloride (TOMAC) as hydrogen bond acceptor. Different parameters that affect the efficiency of the two steps were evaluated and optimized. Under optimal conditions, the limit of detections was 1.5 and 3.0 μg kg−1 for ACE and IMI and the limit of quantification was 1.5 and 4.5 μg kg−1 for ACE and IMI, respectively. Calibration curves were linear in the range 5.0–500 μg kg−1 with correlation coefficients higher than 0.99. Preconcentration factor for ACE and IMI was 16 and 13, respectively. The intraday and interday precisions at concentration levels of 10 and 100 μg kg−1 were less than 7.0%. Finally, applicability of the suggested method was investigated through analysis of the analytes in some pistachio samples, and the obtained relative recoveries (95.6–99.4%) were acceptable. The good sensitivity and suitable purification in the analysis of the pistachio samples, as a complex food matrix, are obtained using the proposed method by combining the advantages of QuEChERS and DLLME.

One promising solution to achieve moderate or intense low-oxygen dilution (MILD) combustion under lower temperatures may be using non-equilibrium plasma discharge. Plasma assisted combustion can extend the flammability limits by reducing the auto-ignition temperature of the reactants and can be a great approach to achieve steady MILD combustion under extreme conditions through decreasing ignition delay time. In particular, the influence of the outlet flows from a co-flowing cylindrical dielectric barrier discharge (DBD) on the CH4/N2 combustion under MILD combustion has been investigated numerically through some simplifications on plasma discharge. A three streams coaxial burner are modeled in a 2D-axisymmetric computational domain by a steady state approach. On the whole, different effects of simplified non-equilibrium plasma discharging, fuel dilution, and oxygen concentration of the preheated oxidizer on MILD combustion regimes are studied. It is found that applying the plasma discharge to the flame region, leads to an intensification of the reaction zone, increment of the rate of heat release by around a factor of 3, and 30% reduction of flame lift-off. Different aspects of plasma effect through chemical kinetics, mixing, and thermal heating on MILD combustion are investigated. Apart from all the benefits of plasma such as improving the mixing, it has a momentous impact on both the reaction zone length and its diameter via improving the ignition delay time.

Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitive domain, and existing classifiers lack transparency in their predictions. This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. Our experiments show that: our model outperforms previous models of frame prediction; we can further improve performance with unlabeled training data leveraging the semi-supervised nature of our model; and the learnt event and actor embeddings intuitively corroborate the document-level predictions, providing a nuanced and interpretable article frame representation., Accepted at NAACL 2021

Harmful emissions from combustion, such as soot, have severe adverse effects on the environment and human health. One way to mitigate these emissions and their dangerous influence is through fuel dilution. In the present study, the effects of adding different percentages of hydrogen and nitrogen to the fuel in a set of laminar coflow ethylene/air diffusion flames are investigated numerically by employing an in-house algorithm, the CoFlame code. The various influences of hydrogen, including dilution, density, transport, thermal, and chemical effects on soot formation and flame characteristics, are evaluated by introducing different fictitious species into the chemical mechanism. The numerical calculations of temperature, soot volume fraction, and average primary particle diameter of soot are in agreement with experimental measurements from the literature. Results show that blending hydrogen and nitrogen into the fuel decreases soot volume fraction and radiation. The hydrogen abstraction C2H2 addition (HACA) surface growth and polycyclic aromatic hydrocarbon (PAH) condensation rate reductions are responsible for the decrease in soot volume fraction. It is found that the soot formation/oxidation reaction rates are decreased under the dilution effect while increased under the transport and chemical effects. Among the various effects of hydrogen addition, the dilution effect has the most significant influence on soot formation/oxidation reaction rates. It is seen that the density and thermal effects have a negligible effect on soot volume fraction.

In addition to the intrinsic toxicity associated with the chemical composition of nanoparticles (NP) and their ligands, biofunctionalized NP can perturb specific cellular processes through NP-cell interactions and induce programmed cell death (apoptosis). In the case of the epidermal growth factor (EGF), nanoconjugation has been shown to enhance the apoptotic efficacy of the ligand, but the critical aspects of the underlying mechanism and its dependence on the NP morphology remain unclear. In this manuscript we characterize the apoptotic efficacy of nanoconjugated EGF as a function of NP size (with sphere diameters in the range 20-80 nm), aspect ratio (A.R., in the range of 4.5 to 8.6), and EGF surface loading in EGFR overexpressing MDA-MB-468 cells. We demonstrate a significant size and morphology dependence in this relatively narrow parameter space with spherical NP with a diameter of approx. 80 nm being much more efficient in inducing apoptosis than smaller spherical NP or rod-shaped NP with comparable EGF loading. The nanoconjugated EGF is found to trigger an EGFR-dependent increase in cytoplasmic reactive oxygen species (ROS) levels but no indications of increased mitochondrial ROS levels or mitochondrial membrane damage are detected at early time points of the apoptosis induction. The increase in cytoplasmic ROS is accompanied by a perturbation of the intracellular glutathione homeostasis, which represents an important check-point for NP-EGF mediated apoptosis. Abrogation of the oxidative stress through the inhibition of EGFR signaling by the EGFR inhibitor AG1478 or addition of antioxidants N-acetyl cysteine (NAC) or tempol, but not trolox, successfully suppressed the apoptotic effect of nanoconjugated EGF. A model to account for the observed morphology dependence of EGF nanoconjugation enhanced apoptosis and the underlying NP-cell interactions is discussed.

Here poly(3,4-ethylenedioxy)thiophene–poly(styrenesulfonate) has been used as a base to produce a carbon monoxide chemiresistive detector. Poly(3,4-ethylenedioxy)thiophene/poly(styrenesulfonate) (PEDOT/PSS) thin films were prepared by spin coating method. The polymer was tested by some common gases in the atmosphere to determine its discrimination ability to detect CO. Particular compositions of Fe, Al and morpholine were also added to the polymer solution to improve thin films sensitivity (percent of resistance variations with respect to its initial value) to recognize carbon monoxide from other gases like water vapor. Room air contains a partial pressure of water vapor which has the main effect on pure polymer. As doping agents were added to the polymer, prepared thin films showed a satisfying sensitivity to CO which was now comparable with the effects of room air. The sensor behavior in approach to room air and carbon monoxide and also the effect of dopants on the polymer has been described.

Noble metal nanoparticles have large cross-sections in both optical and electron microscopy and plasmon coupling between noble metal nanoparticles facilitate the characterization of subdiffraction limit separations through spectral analysis of the scattered light in Plasmon Coupling Microscopy (PCM). The size compatibility of noble metal nanoparticles together with the ability to encode specific functi onality in a rational fashion by control of the nanoparticle surface makes noble metal nanoparticles uni que probes for a broad range of biologi cal processes. Recent applications of the technology include i.) characterization of cellular heterogeneity in nanomaterial uptake and processing through macrophages, ii.) testing the role of viral membrane lipids in mediating viral binding and trafficking, and iii.) characterizing the spatial organization of cancer biomarkers in plasma membranes. This paper reviews some of these applications and introduces the physical and material science principles underlying them. We will also introduce the use of membrane wrapped noble metal nanoparticles, which combine the superb photophysical properties of a nanoparticle core with the biological functionality of a membrane, as probes in PCM. Keywords: Plasmon Coupling Microscopy, Plasmon Ruler, Gold Nanoparticle, Silver Nanoparticle, Smart Nanoparticle, Artificial Virus Nanoparticle, Subdiffraction Limit Imaging, Bioplasmonics