Dynamic Rotational Sensor Using Polymer Optical Fiber for Robot Movement Assessment Based on Intensity Variation

A complex signal processing technique is usually required to process the data in most sensor design structures, and integration into real applications is also challenging. This work presents a dynamic rotational sensor using polymethyl methacrylate (PMMA) fiber for robot movement assessment. The sensor design structure is based on the coupling of light intensity, in which two PMMA fibers are twisted together. Both fibers are bent after twisting and attached on the linear translation stage, which is further attached to the robot. The variation in bending radius causes the bending loss, and that loss is coupled in the second fiber. The change in the macro-bend radius corresponds to the rotation of the robot. Experimental results indicate that the sensor can operate in full rotational cycle (i.e., 0°–360°) as well as for clock and anti-clockwise rotation. Moreover, different rotational speeds (2°/s, 3°/s, 5°/s, and 10°/s) were carried out. The hysteresis loss of the sensor was about 0.77% and the sensitivity was 8.69 nW/°. The presented dynamic rotational sensor is cost-effective and easily integrated into the robot structure to analyze the robot’s circular motion

Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model

The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function’s choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey–California dataset. The performance of the SVMAK model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVMAK model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution

Green Synthesis and Incorporation of Sericin Silver Nanoclusters into Electrospun Ultrafine Cellulose Acetate Fibers for Anti-Bacterial Applications

Fiber based antibacterial materials have gained an enormous attraction for the researchers in these days. In this study, a novel Sericin Encapsulated Silver Nanoclusters (sericin-AgNCs) were synthesized through single pot and green synthesis route. Subsequently these sericin-AgNCs were incorporated into ultrafine electrospun cellulose acetate (CA) fibers for assessing the antibacterial performance. The physicochemical properties of sericin-AgNCs/CA composite fibers were investigated by transmission electron microscopy (TEM), field emission electron microscopy (FE-SEM), Fourier transform infrared spectroscopy (FTIR) and wide X-ray diffraction (XRD). The antibacterial properties of sericin-AgNCs/CA composite fibers against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) were systematically evaluated. The results showed that sericin-AgNCs incorporated in ultrafine CA fibers have played a vital role for antibacterial activity. An amount of 0.17 mg/mL sericin-AgNCs to CA fibers showed more than 90% results and elevated upto >99.9% with 1.7 mg/mL of sericin-AgNCs against E. coli. The study indicated that sericin-AgNCs/CA composite confirms an enhanced antibacterial efficiency, which could be used as a promising antibacterial product

Introducing deep eutectic solvents as a water-free dyeing medium for poly (1,4-cyclohexane dimethylene isosorbide terephthalate) PICT nanofibers

Water, one of the most priceless sources of life, is becoming dangerously threatened and contaminated due to population growth, industrial development, and climatic variations. The drainage of industrial, farming, and municipal sewage into drinking water sources pollutes the water. The textile processing industry is one of the major consumers of water. Herein, the idea of water-free dyeing of electrospun poly (1, 4-cyclohexane dimethylene isosorbide terephthalate) PICT nanofibers is proposed. For this, two different deep eutectic solvents (DE solvents) were introduced as an alternative to water for the dyeing of PICT nanofibers in order to develop a water-free dyeing medium. For this, C.I. disperse red 167 was used as a model dye to improve the aesthetic properties of PICT nanofibers. PICT nanofibers were dyed by conventional batch dyeing and ultrasonic dyeing methods to investigate the effect of the dyeing technique on color buildup characteristics. Dyeing conditions such as dyeing time, temperature and, dye-concentration were optimized. Morphological and chemical characterization observations revealed a smooth morphology of dyed and undyed PICT nanofibers. The ultrasonically dyed nanofibers showed higher color strength and increased tensile strength compared to conventionally dyed nanofibers. Further, the consumption of electrical and thermal energy was also calculated for both processes. The results confirmed that the ultrasonic dyeing method can save 58% on electrical energy and 25% on thermal energy as compared to conventional dyeing

Synthesis of Highly Conductive Electrospun Recycled Polyethylene Terephthalate Nanofibers Using the Electroless Deposition Method

Plastic bottles are generally recycled by remolding them into numerous products. In this study, waste from plastic bottles was used to fabricate recycled polyethylene terephthalate (r-PET) nanofibers via the electrospinning technique, and high-performance conductive polyethylene terephthalate nanofibers (r-PET nanofibers) were prepared followed by copper deposition using the electroless deposition (ELD) method. Firstly, the electrospun r-PET nanofibers were chemically modified with silane molecules and polymerized with 2-(methacryloyloxy) ethyl trimethylammonium chloride (METAC) solution. Finally, the copper deposition was achieved on the surface of chemically modified r-PET nanofibers by simple chemical/ion attraction. The water contact angle of r-PET nanofibers, chemically modified r-PET nanofibers, and copper deposited nanofibers were 140°, 80°, and 138°, respectively. The r-PET nanofibers retained their fibrous morphology after copper deposition, and EDX results confirmed the presence of copper on the surface of r-PET nanofibers. XPS was performed to analyze chemical changes before and after copper deposition on r-PET nanofibers. The successful deposition of copper one r-PET nanofibers showed an excellent electrical resistance of 0.1 ohms/cm and good mechanical strength according to ASTM D-638

Adsorptive defluoridation from aqueous solution using a novel blend of eggshell powder and chitosan nanofibers

Groundwater mostly contains many impurities thus can not be consumed as drinking water directly. The acceptable limit of fluoride in drinking water is 0.5–1.5 mg l ^−1 recommended by World Health Organization (WHO). In this research, a novel nanofiber hybrid; based on Chitosan (CTS) and Eggshell (EGG) was prepared via electrospinning technique and investigated for deflouridation from aqueous solution. SEM images reveal bead-free, smooth morphology and the FTIR confirmed the presence of chitosan and egg within the novel nanofiber blend. The defluoridation efficiency was assessed by varying the different parameters like pH, mass of nanofibers, contact time and initial concentration for adsorption. Studies revealed that CTS/EGG nanofibers hybrid shows incredible adsorption efficiency of 86%. Furthermore, isotherm studies show that the Langmuir isotherm model was well fitted for both CTS and CTS/EGG nanofibers

Electroless deposition : a superficial route to synthesis of highly conductive electrospun nylon 6 nanofibers

In this study, we report the synthesis of conductive Nylon 6 nanofibers (N6 NFs) using the electroless deposition (ELD) method. We fabricated the N6 NFs by electrospinning technique and modified the surface with [2-(methacryloyloxy) ethyl] trimethylammonium chloride (PMETAC). After surface modification of N6 NFs, copper metal was successfully deposited using the ELD process. Parameters including time, temperature, volume, and pH concentration were optimized for copper deposition. SEM images revealed smooth morphology of neat N6 NFs and copper-coated N6 NFs. EDX spectrum was performed on neat N6 NFs and copper-coated N6 NFs to ensure the qualitative and quantitative presence of copper nanoparticles. Furthermore, advance characterization methods such as FTIR, XPS, water contact angle, and tensile strength were performed to analyze the successful copper deposition on N6 NFs