scholarly journals Surfactant location and internal phase volume fraction dictate emulsion electrospun fiber morphology and modulate drug release and cell response

2021 ◽  
Author(s):  
Pamela M. Johnson ◽  
Kelsey E. Knewtson ◽  
Jacob G. Hodge ◽  
Justin M. Lehtinen ◽  
Anna S. Trofimoff ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Tissue Engineering ◽  
Drug Release ◽  
Volume Fraction ◽  
Electrospun Fiber ◽  
Phase Volume ◽  
Fiber Morphology ◽  
Phase Volume Fraction ◽  
Internal Phase ◽  
Versatile Technique

Emulsion electrospinning is a versatile technique used to create tunable fibrous meshes for applications in drug delivery and tissue engineering.

A two-step approach for copper and nickel extracting and recovering by emulsion liquid membrane

2016 ◽  
Vol 74 (10) ◽  
pp. 2454-2461
Author(s):  
Qiang Bi ◽  
Juanqin Xue ◽  
Yingjuan Guo ◽  
Guoping Li ◽  
Haibin Cui
Keyword(s):  
Liquid Membrane ◽  
Volume Fraction ◽  
Process Variable ◽  
Phase Volume ◽  
Optimum Conditions ◽  
Application Technology ◽  
Emulsion Liquid Membrane ◽  
Phase Volume Fraction ◽  
Internal Phase ◽  
Valuable Metals

The recycling of copper and nickel from metallurgical wastewater using emulsion liquid membrane (ELM) was studied. P507 (2-ethylhexyl phosphonic acid-2-ethylhexyl ester) and TBP (tributyl phosphate) were used as carriers for the extraction of copper and nickel by ELMs, respectively. The influence of four emulsion composition variables, namely, the internal phase volume fraction (ϕ), surfactant concentration (Wsurf), internal phase stripping acid concentration (Cio) and the carrier concentration (Cc), and the process variable treat ratio on the extraction efficiencies of copper or nickel were studied. Under the optimum conditions, 98% copper and nickel were recycled by using ELM. The results indicated that ELM extraction is a promising industrial application technology to retrieve valuable metals in low concentration metallurgical wastewater.

scholarly journals Vinyl Ester Oligomer Crosslinked Porous Polymers Prepared via Surfactant-Free High Internal Phase Emulsions

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Yun Zhu ◽  
Ye Hua ◽  
Shengmiao Zhang ◽  
Jianding Chen ◽  
Chun Pu Hu
Keyword(s):  
Elastic Modulus ◽  
Vinyl Ester ◽  
Volume Fraction ◽  
Porous Polymers ◽  
Diameter Distribution ◽  
Phase Volume ◽  
Phase Volume Fraction ◽  
Internal Phase ◽  
External Phase ◽  
Hierarchical Pore

Using vinyl ester resin (VER) containing styrene (or methyl methacrylate) and vinyl ester oligomer (VEO) as external phase, Pickering high internal phase emulsions (Pickering HIPEs) having internal phase volume fraction of up to 95 vol% were prepared with copolymer particles as sole stabilizer. Polymerizing the external phase of these Pickering HIPEs led to porous polymers (poly-Pickering-HIPEs). Compared to the polystyrene- (PS-) based poly-Pickering-HIPEs which were prepared with mixture of styrene and divinylbenzene (DVB) as crosslinker, the poly-Pickering-HIPEs herein showed much higher elastic modulus and toughness. The elastic modulus of these poly-Pickering-HIPEs increased with increasing the VEO concentration in the external phase, while it decreased with increasing internal phase volume fraction. Increasing VEO concentration in the external phase also resulted in a decrease in the average void diameter as well as a narrow void diameter distribution of the resulting poly-Pickering-HIPEs. In addition, there were many small pores in the voids surface caused by the volume contraction of VER during the polymerization, which suggests a new method to fabricate porous polymers having a well-defined hierarchical pore structure.

scholarly journals Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Volume Fraction ◽  
Training Data ◽  
Phase Fraction ◽  
Phase Volume ◽  
Generative Adversarial Network ◽  
Phase Volume Fraction ◽  
Original Dataset ◽  
Multi Phase

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.

Drug-loaded emulsion electrospun nanofibers: characterization, drug release and in vitro biocompatibility

RSC Advances ◽  
2015 ◽  
Vol 5 (121) ◽  
pp. 100256-100267 ◽  
Author(s):  
Jue Hu ◽  
Molamma P. Prabhakaran ◽  
Lingling Tian ◽  
Xin Ding ◽  
Seeram Ramakrishna
Keyword(s):  
Drug Delivery ◽  
Tissue Engineering ◽  
Drug Release ◽  
Electrospun Nanofibers ◽  
Tissue Engineering Scaffold ◽  
In Vitro Biocompatibility

Emulsion electrospun drug–PCL nanofibrous mats were demonstrated as better drug delivery substrates and tissue engineering scaffold compared to PHBV nanofibers.

The Influence of the Ferrite and Pearlite Grain Size on the S-N Fatigue Characteristics of Steel

2014 ◽  
Vol 224 ◽  
pp. 3-8 ◽  
Author(s):  
Sebastian Kamiński ◽  
Marcel Szymaniec ◽  
Tadeusz Łagoda
Keyword(s):  
Mechanical Properties ◽  
Grain Size ◽  
Volume Fraction ◽  
Fatigue Properties ◽  
Phase Volume ◽  
Bending Tests ◽  
Phase Volume Fraction ◽  
Similar Chemical ◽  
Fatigue Characteristics ◽  
Ferrite Grain Size

In this work an investigation of internal structure influence on mechanical and fatigue properties of ferritic-pearlitic steels is shown. Ferrite grain size and phase volume fraction of three grades of structural steel with similar chemical composition, but different mechanical properties, were examined. Afterwards, samples of the materials were subjected to cyclic bending tests. The results and conclusions are presented in this paper

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