A fundamental investigation of scaling up turbulent liquid-phase vortex reactor using experimentally validated CFD models

The production of uniform-sized nanoparticles has potential application in a wide variety of fields, but is still a challenge. One main reason that many lab-scale manufactured nanoparticles have not appeared in industry is because there is lack of control on physical properties and surface functionality of nanoparticles during massive production. Recently, a process called Flash Nanoprecipitation (FNP) has been developed to produce nanoparticles with controlled size and high drug-loading rate. In FNP, fast mixing is required to make sure that solvent and non-solvent mix homogeneously so that competitive precipitation of organics and polymer could result in functional nanoparticles with narrow size distribution. A multi-inlet vortex reactor (MIVR) has been developed to provide fast mixing for the FNP. The MIVR includes four inlets which are tangential to the mixing chamber of reactor. The MIVR has the operational advantage of providing different inlet-flow momentum and configurations compared to other reactors used in the FNP such as confined impinging jet reactor (CIJR). Former studies have already shown its ability of providing fast mixing and successfully producing functional nanoparticles in the FNP. However, until now all previous investigations about the MIVR only focused in its micro-scale (dimensions in millimetre). While the micro-scale MIVR does show great promise in the production of functional nanoparticles, the small dimensions and correspondingly small output of the micro-scale MIVR limit its usefulness to producing functional nanopraticles for applications requiring small production run such as high-value pharmaceutical agents. Some applications such as nanoparticle used in pesticides and cosmetics may require larger production run than the micro-scale MIVR can provide, making it economically unrealistic based on the relatively high capital and operating costs needed for a large number of reactors operating in parallel. For this reason, in the study we are interested in investigating the feasibility of scaling up the FNP process to a macro-scale MIVR capable of generating large quantities of functional nanoparticles, both rapidly and economically, and consequently developing experimentally verified computational fluid dynamics (CFD) models that can be used as design tools for further optimizing reactor design and operation parameters to produce customized functional nanoparticles. To accomplish this investigation, a macro-scale MIVR has been built with optical access. Non-intrusive, optical-based measurement techniques including particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF) were used to measure flow field and mixing, and related CFD models, specifically turbulence models were validated and developed for optimizing the MIVR and future model development of the FNP process

Selection Method for Kernel Function in Nonparametric Extrapolation Based on Multicriteria Decision-Making Technology

Selecting the most appropriate kernel function to extrapolate a load set is the paramount step in compiling load spectrum, as it affects the results of nonparametric extrapolation largely. Aiming at this issue, this paper provides a new approach in selecting kernel function for the nonparametric extrapolation. To solve the complexity and uncertainty of nonparametric extrapolation, characteristics of four kernel functions and their effects on the results are explained in the “from-to” diagram obtained by rainflow counting. Multicriteria decision-making (MCDM) is then applied to solve the selection problem of kernel function. To evaluate the dispersion degrees of the mean and amplitude of a load set accurately, their range, standard deviation, and interquartile range are selected as the evaluation criteria. The weight of each criterion, which represents the impact degree on the selection of the kernel function, is calculated separately using the eigenvector and entropy method. The comprehensive weights are obtained by applying the optimization theory and Jaynes’ maximum entropy principle. Finally, the importance of each criterion is discussed according to their calculated comprehensive weights, and the selection method for kernel functions is obtained, which is illustrated by extrapolating the output torque of the power split device of hybrid electrical vehicles

Effects of Chocolate Milk Supplementation on Recovery from Cycling Exercise and Subsequent Time Trial Performance

PURPOSE: Supplementing with carbohydrate plus protein following strenuous endurance exercise has been found to improve both recovery and subsequent aerobic endurance performance beyond that of a carbohydrate supplement alone. The purpose of the present study was to compare the effects of chocolate milk (CM), an isocaloric carbohydrate only supplement (CHO), and placebo (PLA) on markers of endurance exercise recovery and subsequent time trial performance in trained cyclists. METHODS: Ten trained male and female cyclists (5 males, 5 females) performed 3 trials in which they first cycled for 1.5 h at 70% of VO2max, followed by 10 min of intervals that alternated 45% and 90% VO2max. They then recovered in the laboratory for 4 h, and performed a 40 km time trial (TT). The supplements were provided immediately after the first bout and 2 h into the recovery period. Treatments were administered using a double-blind randomized design. RESULTS: TT time was significantly shorter in CM than CHO and PLA (79.43±2.11 vs. 85.74±3.44 and 86.92±3.28 min, respectively, p=\u3c.05). Significant treatment differences were found for plasma insulin, glucose, free fatty acids (FFA) and glycerol. Plasma insulin levels were significantly lower in CM than CHO at recovery time points R45 (47.30±10.54 vs. 58.71±6.01 &#;U/ml, p\u3c.05), R120 (14.32±1.34 vs. 22.53±3.37 &#;U/ml, p\u3c.05) and REnd (15.57±1.53 vs. 34.35±4.55 &#;U/ml, p\u3c.05). Plasma glucose was significantly lower in CM than CHO at recovery time points R45 (76.61±3.08 vs. 101.65±3.47 mg/dL, p\u3c.05) and R120 (74.72±2.22 vs. 81.46±4.87 mg/dL, p\u3c.05). While FFA and glycerol were both higher in PLA than in CM and CHO overall (p\u3c.05 for both), FFA and glycerol were higher in CM than in CHO (p\u3c.05 for both) during recovery and at TTEnd. Blood lactate was significantly higher at R45 and TTEnd in both CM and CHO than in PLA, but no differences were found between CM and CHO. No significant treatment differences were found for myoglobin, CPK, cortisol, and 5 pro- and anti-inflammatory cytokines (TNF-&#;, IL-6, IL-10, IL-8, and IL-1Ra). CONCLUSIONS: Chocolate milk provided during recovery can improve subsequent time trial performance in trained cyclists more effectively than an isocaloric CHO supplement. This may be due to a faster rate of muscle glycogen resynthesis

Aerobic Exercise Training Adaptations Are Increased by Postexercise Carbohydrate-Protein Supplementation

Carbohydrate-protein supplementation has been found to increase the rate of training adaptation when provided postresistance exercise. The present study compared the effects of a carbohydrate and protein supplement in the form of chocolate milk (CM), isocaloric carbohydrate (CHO), and placebo on training adaptations occurring over 4.5 weeks of aerobic exercise training. Thirty-two untrained subjects cycled 60 min/d, 5 d/wk for 4.5 wks at 75–80% of maximal oxygen consumption (VO2 max). Supplements were ingested immediately and 1 h after each exercise session. VO2 max and body composition were assessed before the start and end of training. VO2 max improvements were significantly greater in CM than CHO and placebo. Greater improvements in body composition, represented by a calculated lean and fat mass differential for whole body and trunk, were found in the CM group compared to CHO. We conclude supplementing with CM postexercise improves aerobic power and body composition more effectively than CHO alone

Solitary pancreatic tuberculous abscess mimicking pancreatic cystadenocarcinoma: a case report

BACKGROUND: The incidence of pancreatic tuberculosis is extremely rare, and it frequently misdiagnosed as pancreatic neoplasms. The nonsurgical diagnosis of this entity continues to be a challenge. CASE PRESENTATION: A 33 year old male with six-month history of intermittent right epigastric vague pain and weight lost had found a solitary pancreatic cystic mass and diagnosed as pancreatic cystadenocarcinoma. The chest X-ray film and physical examination revealed no abnormalities. Abdominal ultrasound (US) examination showed an irregular hypoechoic lesion of 6.6 cm × 4.4 cm in the head of pancreas, and color Doppler flow imaging did not demonstrate blood stream in the mass. The attempts to obtain pathological evidence of the lesion by US-guided percutaneous fine needle aspiration failed, an exploratory laparotomy and incisional biopsy revealed a caseous abscess of the head of pancreas without typical changes of tuberculous granuloma, but acid-fast stain was positive. CONCLUSIONS: Pancreatic tuberculosis should be considered in the differential diagnosis of focal pancreatic lesions, especially for young people in developing countries

Automatic detection of low surface brightness galaxies from SDSS images

Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the Low Surface Brightness Galaxies Auto Detect model (LSBG-AD), which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 mag arcsec $^ {- 2}$ to 24 mag arcsec $^ {- 2}$, quite consistent with the surface brightness distribution of the standard sample. 96.46\% of LSB galaxy candidates have an axis ratio ($b/a$) greater than 0.3, and 92.04\% of them have $fracDev\_r$\textless 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next generation observatories.Comment: 11 pages, 9 figures,accepted to be published on MNRA