Characterizing a novel method for blending regenerated cellulose structures with polyester filaments

The combination method of various manmade fibers with other fibers was developed to create special properties many years ago. Fibers can be mixed in a generation process, such as in solution or melt spinning, or in a fiber spinning process in the form of filaments, staple, sliver, yarn, and so on. Hybrid blended yarns are a common class of yarns in the textile industry, and the most are produced through cotton–polyester or viscose–polyester blending in the spinning process. The cellulose fibers blended in spun yarns should be at least 1 in. in length with appropriate surface properties. This causes limitations in the use of cheap materials. Cheap products owe their manufacturing to waste papers, wood trash, poor linters of cotton, spinning trash, and so on. In this study, a new method for combining regenerated cellulose structures with polyester filaments was examined; it is different from conventional hybrid polyester–viscose fiber production. In the first step, the viscose pulp was prepared and then coated on the polyester filaments in various forms. The properties, including the tensile strength, density, yarn count, moisture regain, static electricity, and dyeing, of the resulted hybrid fibers were evaluated with a variety of methods. The results show that almost all of the properties of the coated samples were improved, especially the moisture absorbance, static electricity, and dyeing properties. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Solid phase extraction of lead and cadmium using solid sulfur as a new metal extractor prior to determination by flame atomic absorption spectrometry

A new method using a mini-column packed with sulfur as a new solid phase extractor has been developed for simultaneous preconcentration of lead and cadmium in water samples prior to flame atomic absorption spectrometric determinations. The effects of various parameters such as pH, flow rate of sample and eluent, type and concentration of eluent, sample volume, amount of adsorbent and interfering ions have been studied. The calibration graph was linear in the range of 10-300 and 1-20 ng mL(-1) for lead and cadmium, respectively. The limit of detection based on three times the standard deviation of the blank (3S(b)) was 3.2 and 0.2 ng mL(-1) (n=10) for lead and cadmium, respectively. A preconcentration factor of 250 was achieved in this method. The proposed procedure was applied to the determination of metal ions in tap, river and wastewater samples.     

An advanced security scheme based on clustering and key distribution in vehicular ad-hoc networks

Preservation of security is an essential requirement in vehicular ad hoc networks (VANETs) as vehicular communication is vulnerable to attacks. Attackers may exploit VANETs to send bogus information to deceive other vehicles which leads to serious issues. In this paper, we describe an advanced Secure scheme based on Clustering and Key Distribution (SCKD) among members and cluster-heads in VANET. The SCKD is a coordination based algorithm in which nodes are located within different clusters and their cluster heads are chosen from trusty nodes. For a secure end-to-end communication, our scheme deploys the proxy signature, blind proxy signature, hashed message authentication code, and symmetric cryptography. Results show that our scheme preserves security requirements including authentication, confidentiality, data-integrity, non-repudiation, and unforgeability. Since the cost and time computation of key generation and distribution decreases by SCKD compared with other algorithms, our algorithm will be applicable for VANETs.     

Improvement of intelligent methods for evaluating the apparent quality of knitted fabrics

In the previous studies, the standard boards of yarn (ASTM) were analyzed using the image analysis method and artificial neural networks; the appearance of different knitted fabrics samples was tested for appearance. There was a strong influence of yarn type and fabrics structure on fabrics apparent quality. In the present research, the artificial neural network (ANN) has been applied to predict the fabric apparent parameters. The optimum structure of ANN has been designed using the genetic algorithm method. The results show that the ANN can be optimized very well by the GA and the designed ANN is very accurate and applicable to predict the apparent parameters.     

Reconstructing the pipeline by introducing multiplexed multiple reaction monitoring mass spectrometry for cancer biomarker verification: an NCI-CPTC initiative perspective

Proteomics holds great promise in personalized medicine for cancer in the post-genomic era. In the past decade, clinical proteomics has significantly evolved in terms of technology development, optimization and standardization, as well as in advanced bioinformatics data integration and analysis. Great strides have been made for characterizing a large number of proteins qualitatively and quantitatively in a proteome, including the use of sample fractionation, protein microarrays and MS. It is believed that differential proteomic analysis of high-quality clinical biospecimen (tissue and biofluids) can potentially reveal protein/peptide biomarkers responsible for cancer by means of their altered levels of expression and/or PTMs. Multiple reaction monitoring, a multiplexed platform using stable isotope dilution-MS with sensitivity and reproducibility approaching that of traditional ELISAs commonly used in the clinical setting, has emerged as a potentially promising technique for next-generation high-throughput protein biomarker measurements for diagnostics and therapeutics.     

Fabrication of electrospun nanofibre yarn based on nylon 6/microencapsulated phase change materials

Twisted nylon 6 nanofibre yarns containing microencapsulated phase change materials (MPCMs) were fabricated via electrospinning to prepare thermal regulating nanofibre yarns. Electrospun nanofibre yarns with different contents of MPCMs including 0.375, 0.75, 1.5 and 3 wt% were prepared. The surface morphology, crystallisation and thermal properties of the yarn samples were characterised by scanning electron microscopy (SEM) and differential scanning calorimetry, respectively. SEM results showed that electrospun composite fibres and yarns had good morphology with smooth surface and the MPCMs were randomly distributed on the composite yarn surface, inside the nanofibres and between the fibres in the yarn structure. Additionally, the SEM results suggested that the average diameters of nylon 6/MPCM nanofibres decreased from 0.23 ± 0.03 µm for neat nylon 6 to a minimum of 0.10 ± 0.02 µm for composite yarn containing 3 wt% MPCM. However, nylon 6/MPCM nanofibre yarn diameter displayed a complex behaviour; the average diameters of electrospun composite yarns increased upon addition of MPCM and reached a maximum value of 165.1 ± 5.11 µm for composite yarn containing 0.75 wt% MPCM, then decreased markedly. These changes in nylon 6/MPCM nanofibres and yarn diameters have been discussed in terms of electrospinning solution properties and twist parameter. The melting enthalpy values of MPCM in the composite nanofibre yarns increased as the content of MPCMs increased up to the highest content and higher than 80% of the heat storage capacity of MPCMs was retained after electrospinning.