Silver nanoparticle films by flowing gas atmospheric pulsed laser deposition and application to surface-enhanced Raman spectroscopy

For the fast uptake into industrial applications, the further development of robust methods of nanomaterials, which are inexpensive and simultaneously technologically feasible, is one of the major key factors. A newly introduced atmospheric pulsed laser deposition method, based on a flowing gas approach, was used for plasmonic metal nanoparticle (NP) film of silver. Contrary to vacuum, in this method, the ambient air restricts expansion of the ablation plume within 1 to 3 mm above the target surface. These sets constrain on the formation of NP film close to the ablation spot. For deposition on a widely spaced surface, ablation material was entrained in a flow of argon, supplied at ~32 ms⁻¹, and effectively delivered to the substrate at ~20 ms⁻¹. The films produced were crystalline and particulate in nature, showing spectral plasmonic feature of surface plasmon resonance in the visible region. The film was directly tested in surface-enhanced Raman spectroscopy for chemical detection of crystal violet; the film with large particulates and aggregated crystallites was well-performed, showing enhanced Raman signals and detection sensitivity. Certainly, flowing gas atmospheric pulsed laser deposition seems a fast alternative to vacuum-pulsed laser deposition but needs further investigations to bring it in the industry for applications in sensor, catalysis, solar cell, and coating technology.     

Enhancing liquid micromixing using low‐frequency rotating nanoparticles

Magnetic nanofluid actuation by rotating magnetic fields was proposed as a high‐performance tool for liquid mixing with enhanced micromixing features. A comparative study was conducted to evaluate the mixing index in T‐type mixers of magnetic and nonmagnetic fluids subject to static (SMF), oscillating (OMF), and rotating (RMF) magnetic fields. RMF excitation unveiled superior mixing indices with strong dependences to magnetic field frequency and content of magnetic nanoparticles. The impact of magnetic field types on micromixing was further examined at low and moderate Re numbers using the Villermaux–Dushman reaction and IEM micromixing model. The IEM‐inferred micromixing times were remarkably shorter by nearly four orders of magnitude in comparison with OMF and SMF excitations, and without magnetic field. The proposed mixing strategy is foreseen to complement innovative microfluidic devices with valuable mixing tools and methods for the diagnosis of the coupling between transport and intrinsic kinetics. © 2016 American Institute of Chemical Engineers AIChE J, 63: 337–346, 2017     

Optima localization by vehicle formations imitating the Nelder-Mead simplex algorithm

In this paper, we address the problem of localizing extrema points and iso-contours of ambient environmental fields (specifically, ocean bottom landscape and underwater plumes) using a networked formation of autonomous underwater vehicles. We propose the use of the Nelder-Mead extension to the basic simplex nonlinear optimization algorithm. In these robust gradient-free strategies, decisions are solely made based on field values measured by the individual vehicles, while measurements are fused and actions decided according to the algorithm. A main goal of this paper is to trigger interest in direct search methods as pertains to this type of robotic problem.     

Finite element analysis of a center crack specimen warm pre-stressed under different modes of loading

Load bearing capacity of cracked specimens can be improved following pre-loading procedures like warm pre-stressing (WPS). In this paper, the effects of modes I and II pre-loading on fracture load of a central angled crack specimen are studied by finite element analysis. The specimen is reloaded either in mode I or in mode II. To predict the fracture load of cracked specimen after pre-loading, the maximum tensile stress (MTS) and maximum shear stress (MSS) criteria are used. It is shown that mode II fracture load is independent of pre-loading but the mode I fracture load is highly improved after warm pre-stressing. The mode mixity of the specimen is also changed when the pre-loading and reloading modes are not identical. An increase in the fracture load of the specimen following pre-loading may be due to change of failure mode as predicted by finite element analysis in this work.     

Storage Properties of Low Fat Fish and Rice Flour Coextrudates

In the present study, response surface method (RSM) and genetic algorithm (GA) were used to study the effects of process variables like screw speed, rpm (x ₁), L/D ratio (x ₂), barrel temperature (°C; x ₃), and feed mix moisture content (%; x ₄), on flow rate of biomass during single-screw extrusion cooking. A second-order regression equation was developed for flow rate in terms of the process variables. The significance of the process variables based on Pareto chart indicated that screw speed and feed mix moisture content had the most influence followed by L/D ratio and barrel temperature on the flow rate. RSM analysis indicated that a screw speed > 80 rpm, L/D ratio > 12, barrel temperature > 80 °C, and feed mix moisture content > 20% resulted in maximum flow rate. Increase in screw speed and L/D ratio increased the drag flow and also the path of traverse of the feed mix inside the extruder resulting in more shear. The presence of lipids of about 35% in the biomass feed mix might have induced a lubrication effect and has significantly influenced the flow rate. The second-order regression equations were further used as the objective function for optimization using genetic algorithm. A population of 100 and iterations of 100 have successfully led to convergence the optimum. The maximum and minimum flow rates obtained using GA were 13.19 × 10⁻⁷ m³/s (x ₁ = 139.08 rpm, x ₂ = 15.90, x ₃ = 99.56 °C, and x ₄ = 59.72%) and 0.53 × 10⁻⁷ m³/s (x ₁ = 59.65 rpm, x ₂ = 11.93, x ₃ = 68.98 °C, and x ₄ = 20.04%).     

THERMODYNAMICS OF MOISTURE SORPTION IN ALFALFA PELLETS

ABSTRACT

Experimental data on sorption isotherms of alfalfa pellets were used to determine the thermodynamic functions (differential heat of sorption, spreading pressure, net integral enthalpy and entropy) of the pellets. The thermodynamic functions were then utilized in modeling the specific heats of pellets as a function of moisture content. The estimated values of the thermodynamic functions at different moisture contents confirmed the results of an earlier study on the influence of moisture sorption on the physical integrity of the pellets. The changes in the thermodynamic properties and physical integrity were prominent when the moisture of the pellets were between 0.10 and 0.12 (mass fraction basis). Using the differential heat of sorption, it was determined that alfalfa pellets have higher affinity for moisture uptake when compared to wheat and lentils. The specific heat of the solids of alfalfa pellets had a parabolic relationship with moisture whereas the specific heat of water fraction increased linearly with moisture content of the pellets.     

Comparison of essential oils compositions of Nepeta persica obtained by supercritical carbon dioxide extraction and steam distillation methods

Essential oil of Nepeta persica cultivated in Iran was obtained by steam distillation and supercritical (carbon dioxide) extraction methods. The oils were analysed by capillary gas chromatography using flame ionization and mass spectrometric detections. The compounds were identified according to their retention indices and mass spectra (EI, 70 eV). The effects of different parameters such as pressure, temperature, modifier volume and extraction times (dynamic and static) on the supercritical fluid extraction (SFE) of N. persica oil were investigated. The results showed that under the pressure of 20.3 MPa, temperature of 45 °C, methanol of 1.5% v/v), dynamic extraction time of 50 min and static extraction time of 25 min extraction was more selective for the 4αβ,7α,7aα-nepetalactone. Twelve compounds were identified in the steam-distilled oil. The major components of N. persica were 4αβ,7α,7aα-nepetalactone (26.5%), cis-β-farnesene (4.4%) and 3,4α-dihydro-4aα,7α,7aα-nepetalactone (3.5%). However, by using supercritical carbon dioxide under optimum conditions, only two components have more than 90.0% of the oil. The extraction yield based on steam distillation was 0.08% (v/w). On the other hand, using SFE extraction yield in the range of 0.22–8.90% (w/w) were obtained at different conditions. The results show that, in Iranian N. persica oil, 4αβ,7α,7aα-nepetalactone is a major component.     

Application of a Model to Simulate the Wetting and Drying Processes of Woody Biomass in the Field

The variation of moisture content in the biomass materials would affect the quality during the utilization of these materials as solid biofuel. The ability to predict the time-dependent moisture contents of the biomass via modeling can help to devise a better way to store and manage these biomass materials. In this study, pieces of aspen stems were subject to cycles of wetting and drying in lab-scale tests. A lumped mathematical model for simulating the moisture changes during storage was developed and calibrated using the experimental data. With the available weather data (air temperature, relative humidity, solar radiation, wind speed, and precipitation) as inputs, the model was then applied to estimate the moisture content of aspen (Populus tremuloides) during one year of storage in the field. Results showed that, for both uncovered bales and covered bales, the predicted moisture contents and the profiles were in good agreement with the measured in-field results. This lumped model may be used as a first approximation, and applied to estimate the moisture content of aspen or similar woody biomass materials during relatively long-term field storage.     

Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one. This process can take up a large amount of resource real estate, and as we have laid out in this paper, by using a Model consisting of various Neural Networks trained on a custom dataset. It can be automated into assisting designers, allowing them to focus on the other more complicated parts of the system they are designing by quickly generating what would rather be straightforward busywork. Over the past 20 years, the boom in how much the internet is used and the sheer volume of pages on it demands a high level of work and time to create them. For the efficiency of the process, we proposed a multi-model-based architecture on image captioning, consisting of Convolutional neural network (CNN) and Long short-term memory (LSTM) models. Our proposed approach trained on our custom-made database can be automated into assisting designers, allowing them to focus on the other more complicated part of the system. We trained our model in several batches over a custom-made dataset consisting of over 6300 files and were finally able to achieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50 hand-drawn images at 87.86%