Optical microsensor for counting food substance particles in lab-on-a-chips

Integrated optical detection is considered to be an important operation in lab-on-a-chips. This paper presents an optical fiber-based micro-sensor that is capable of detecting food substance particles in a lab-on-a-chip. The system consists of a microcontroller and associated circuitry, a laser emitter, a laser receiver, fiber optic cables, a microfluidics chip, and the food substance samples to be tested. When the particles flow through the microfluidic channel in the chip, the receiver’s output voltage varies due to the particles blocking the passage of the laser ray. The changes in the collected signals are analyzed to count the number of particles. Experiments are conducted on several food substance samples including talcum powder, ground ginger, and soy sauce. The experimental results are presented and discussed.

Three-dimensional tissue scaffolds from interbonded poly(e-caprolactone) fibrous matrices with controlled porosity

In this article, we report on the preparation and cell culture performance of a novel fibrous matrix that has an interbonded fiber architecture, excellent pore interconnectivity, and controlled pore size and porosity. The fibrous matrices were prepared by combining melt-bonding of short synthetic fibers with a template leaching technique. The microcomputed tomography and scanning electron microscopy imaging verified that the fibers in the matrix were highly bonded, forming unique isotropic pore architectures. The average pore size and porosity of the fibrous matrices were controlled by the fiber/template ratio. The matrices having the average pore size of 120, 207, 813, and 994 mm, with the respective porosity of 73%, 88%, 96%, and 97%, were investigated. The applicability of the matrix as a three-dimensional (3D) tissue scaffold for cell culture was demonstrated with two cell lines, rat skin fibroblast and Chinese hamster ovary, and the influences of the matrix porosity and surface area on the cell culture performance were examined. Both cell lines grew successfully in the matrices, but they showed different preferences in pore size and porosity. Compared with two-dimensional tissue culture plates, the cell number on 3D fibrous matrices was increased by 97.27% for the Chinese hamster ovary cells and 49.46% for the fibroblasts after 21 days of culture. The fibroblasts in the matrices not only grew along the fiber surface but also bridged among the fibers, which was much different from those on two-dimensional scaffolds. Such an interbonded fibrous matrix may be useful for developing new fiber-based 3D tissue scaffolds for various cell culture applications.

Microstructure and mechanical properties of nitrided and chromized short steel fiber reinforced aluminum based P/M composites

The present investigation is on the microstructure evolution and hardness of powder metallurgically processed Al- 0.5 wt.%Mg base 10 wt.% short steel fiber reinforced composites. The 0.38 wt.% C short steel fibers of average diameter 50µm and 500-800µm length were nitrided and chromized in a fluid bed furnace. Nitriding was carried out at 525°C for 90, 30 and 5 min durations. Chromizing was performed at 950°C for 53 and 7 min durations, using thermal reactive deposition (TRD) and diffusion technique. The treated fibers and resulting reaction interfaces were characterized using metallographic, microhardness and XRD techniques.

Engineering of small-molecule gels based on the thermodynamics and kinetics of fiber formation

In this chapter, we will give an overview of our work on manipulating the micro/nano structure and macroscopic properties of SMGs. Firstly, it will cover the analysis of the thermodynamics of fiber formation in SMGs and the classification and characterization of the topological and micro/nano structures of fiber networks, followed by the analysis of the formation kinetics of these networks. The criteria of engineering of the SMGs will be summarized according to the latest understanding of the formation mechanisms of fiber networks. On the basis of this understanding, approaches that have been developed to engineer the micro/nanometer structures and macroscopic properties of typical SMGs will be presented.

Properties of bio-based polymer nylon 11 reinforced with short carbon fiber composites

In this work, micro-composite materials were produced by incorporating 3-mm long reclaimed short carbon fibers into bio-based nylon 11 via melt compounding. A systematic fiber length distribution analysis was performed after the masterbatching, compounding and an injection moulding processes using optical microscopy images. It was found that the large majority of the fibers were within the 200-300 μm in length range after the injection moulding process. The mechanical (flexural and tensile), thermo-mechanical, and creep properties of the injection moulded materials are reported. We found that an enhancement in flexural and Young's modulus of 25% and 14%, respectively, could be attained with 2 wt% carbon fiber loading whilst no significant drawback on the ductility and toughness of the matrix was observed. The creep resistance and recovery of the nylon 11, tested using dynamic mechanical thermal analysis at room temperature and 65°C, was significantly improved by up to 30% and 14%, respectively, after loading with carbon fiber. This work provides an insight into the property improvement of the bio-based polymer nylon 11 using a small amount of a reclaimed engineered material. © 2014 Society of Plastics Engineers.

Chemical structure based prediction of PAN and oxidized PAN fiber density through a non-linear mathematical model

The production of carbon fiber, particularly the oxidation/stabilization step, is a complex process. In the present study, a non-linear mathematical model has been developed for the prediction of density of polyacrylonitrile (PAN) and oxidized PAN fiber (OPF), as a key physical property for various applications, such as energy and material optimization, modeling, and design of the stabilization process. The model is based on the available functional groups in PAN and OPF. Expected functional groups, including [Formula presented], [Formula presented], –CH2, [Formula presented], and [Formula presented], were identified and quantified through the full deconvolution analysis of Fourier transform infrared attenuated total reflectance (FT-IR ATR) spectra obtained from fibers. These functional groups form the basis of three stabilization rendering parameters, representing the cyclization, dehydrogenation and oxidation reactions that occur during PAN stabilization, and are used as the independent variables of the non-linear predictive model. The k-fold cross validation approach, with k = 10, has been employed to find the coefficients of the model. This model estimates the density of PAN and OPF independent of operational parameters and can be expanded to all operational parameters. Statistical analysis revealed good agreement between the governing model and experiments. The maximum relative error was less than 1% for the present model.

Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene flourescent in situ by hybridization

Background Changes in the composition of gastrointestinal microbiota by dietary interventions using pro- and prebiotics provide opportunity for improving health and preventing disease. However, the capacity of lupin kernel fiber (LKFibre), a novel legume-derived food ingredient, to act as a prebiotic and modulate the colonic microbiota in humans needed investigation.

Aim of the study The present study aimed to determine the effect of LKFibre on human intestinal microbiota by quantitative fluorescent in situ hybridization (FISH) analysis.

Design A total of 18 free-living healthy males between the ages of 24 and 64 years consumed a control diet and a LKFibre diet (containing an additional 17–30 g/day fiber beyond that of the control—incorporated into daily food items) for 28 days with a 28-day washout period in a single-blind, randomized, crossover dietary intervention design.
Methods Fecal samples were collected for 3 days towards the end of each diet and microbial populations analyzed by FISH analysis using 16S rRNA gene-based oligonucleotide probes targeting total and predominant microbial populations.

Results Significantly higher levels of Bifidobacterium spp. (P = 0.001) and significantly lower levels of the clostridia group of C. ramosum, C. spiroforme and C. cocleatum (P = 0.039) were observed on the LKFibre diet compared with the control. No significant differences between the LKFibre and the control diet were observed for total bacteria, Lactobacillus spp., the Eubacterium spp., the C. histolyticum/C. lituseburense group and the Bacteroides–Prevotella group.
Conclusions Ingestion of LKFibre stimulated colonic bifidobacteria growth, which suggests that this dietary fiber may be considered as a prebiotic and may beneficially contribute to colon health.

Modification of wool fiber using steam explosion

Wool fiber was modified by steam explosion in this study. SEM results show that some scales on the fiber surface were cleaved and tiny grooves generated during the explosion. FTIR results suggest no evident changes in the chemical composition of the fiber after the explosion treatment. However, the crystallinity of the fiber decreased slightly as the steam pressure increased based on the X-ray results. In the thermal analysis, DSC results show that the temperature corresponding to vaporization of absorbed water and cleavage of disulfide bonds respectively decreased as the steam pressure increased. The reduction in thermal decomposition energy of the treated fiber indicates that steam explosion might have destroyed some crystals and crosslinks of macromolecular chains in the fiber. The treatment also led to some alterations of the fiber properties, including reduction in strength, moisture regain and solubility in caustic solution.

Latex-based nanocomposites

Nanoparticles have been widely used as filler in polymer because of their unique reinforcing effect. There are many compounding methods for nanocomposites. The recent development on latex nanocomposites, a group of special nanocomposites, is reviewed in this chapter. They include carbon black/latex nanocomposite, silica/latex nanocomposite, layered silicate/latex nanocomposite, ZnO/latex nanocomposite, carbon nanotubellatex nanocomposite, lignin/latex nanocomposite, starch/latex nanocomposite, nano-fiber/latex nanocomposite, and Chitin whiskers/latex nanocomposite. Advanced compounding techniques and the latest advance on these latex nanocomposites are described. The nanoreinforcing theories of latex nanocomposites are also studied.

An artificial neural network-based hairiness prediction model for worsted wool yarns

This study evaluated the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns from various top, yarn and processing parameters. The results indicated that the MLP model predicted yarn hairiness more accurately than the MLR model, and should have wide mill specific applications. On the basis of sensitivity analysis, the factors that affected yarn hairiness significantly included yarn twist, ring size, average fiber length (hauteur), fiber diameter and yarn count, with twist having the greatest impact on yarn hairiness.

New effective catalysts based on mesoporous nanofibrous carbon for selective oxidation of hydrogen sulfide

The systems based on granular mesoporous nanofibrous carbonaceous (NFC) materials synthesized by decomposition of hydrocarbons over nickel- containing catalysts are promising catalysts for selective oxidation of hydrogen sulfide. Sample series of nanofibrous carbon with three main types of their fiber structures and different contents of metal catalysts inherited from the catalysts for their synthesis were studied in this reaction. The correlation between NFC structure and its activity and selectivity in hydrogen sulfide oxidation was determined. The metal inherited from the initial catalysts for the synthesis of NFC influences the activity and selectivity of the resulting carbon catalysts. A particular influence is observed in the case of the catalyst withdrawn from the synthesis reactor at the stage of stationary operation of the metal catalyst (low specific carbon yields per unit weight of the catalyst). The presence of the metal phase results in an increase in the carbon catalyst activity and in a decrease in the selectivity to sulfur. NFC samples with the highest activity and selectivity are nanotubes and those with graphite planes perpendicular to the axis of the fibers. Carbon nanotubes have high selectivity, while samples obtained on copper–nickel catalysts also possess high activity. The promising NFC catalysts provide high conversion and selectivity (almost independent of the molar oxygen/hydrogen sulfide ratio) when a large excess of oxygen is contained in the reaction mixture.

Plastic deformation in a partially crystallized Zr-based BMG under Vickers indenter

Vickers indentations were carried out on an anneal-introduced partially crystallized Zr₄₁Ti₁₄Cu_12.5 Ni₁₀Be_22.5 bulk metallic glass (BMG), and the evolution of the shear bands in this samplewas investigated and compared to the as-cast, aswell as the structurally relaxed counterparts. The results indicate that the plastic deformation in the partially crystallized BMG was accommodated by the semi-circular (primary) and radial (secondary) shear bands. A full crack or flake that was produced due to the spring back during the load removal was observed. The shear band density in the annealed alloy which was dispersed with crystalliteswas significantly lower than that of the as-cast alloy. The difference of the shear band features among the three kinds of alloy status, i.e., partially crystallized, structurally relaxed and as-cast alloys was discussed in terms of the free volume in the BMGs and the characteristics of nano-composites. It has been demonstrated that the plasticity for the three statuses of alloys queues in the descending order as the as-cast, annealed with partial crystallization, and annealed without crystallization.

Feature extraction for animal fiber identification

Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.

Random forest based lung nodule classification aided by clustering

An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) A_z of 0.9786 has been achieved.

Robust ODF smoothing for accurate estimation of fiber orientation

Q-ball imaging was presented as a model free, linear and multimodal diffusion sensitive approach to reconstruct diffusion orientation distribution function (ODF) using diffusion weighted MRI data. The ODFs are widely used to estimate the fiber orientations. However, the smoothness constraint was proposed to achieve a balance between the angular resolution and noise stability for ODF constructs. Different regularization methods were proposed for this purpose. However, these methods are not robust and quite sensitive to the global regularization parameter. Although, numerical methods such as L-curve test are used to define a globally appropriate regularization parameter, it cannot serve as a universal value suitable for all regions of interest. This may result in over smoothing and potentially end up in neglecting an existing fiber population. In this paper, we propose to include an interpolation step prior to the spherical harmonic decomposition. This interpolation based approach is based on Delaunay triangulation provides a reliable, robust and accurate smoothing approach. This method is easy to implement and does not require other numerical methods to define the required parameters. Also, the fiber orientations estimated using this approach are more accurate compared to other common approaches.