Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 15

Search results for: Ionut Cristea

15 TiO2 Solar Light Photocatalysis a Promising Treatment Method of Wastewater with Trinitrotoluene Content

Authors: Ines Nitoi, Petruta Oancea, Lucian Constantin, Laurentiu Dinu, Maria Crisan, Malina Raileanu, Ionut Cristea


2,4,6-Trinitrotoluene (TNT) is the most common pollutant identified in wastewater generated from munitions plants where this explosive is synthesized or handled (munitions load, assembly and pack operations). Due to their toxic and suspected carcinogenic characteristics, nitroaromatic compounds like TNT are included on the list of prioritary pollutants and strictly regulated in EU countries. Since their presence in water bodies is risky for human health and aquatic life, development of powerful, modern treatment methods like photocatalysis are needed in order to assures environmental pollution mitigation. The photocatalytic degradation of TNT was carried out at pH=7.8, in aqueous TiO2 based catalyst suspension, under sunlight irradiation. The enhanced photo activity of catalyst in visible domain was assured by 0.5% Fe doping. TNT degradation experiments were performed using a tubular collector type solar photoreactor (26 UV permeable silica glass tubes series connected), plug in a total recycle loops. The influence of substrate concentration and catalyst dose on the pollutant degradation and mineralization by-products (NO2-, NO3-, NH4+) formation efficiencies was studied. In order to compare the experimental results obtained in various working conditions, the pollutant and mineralization by-products measured concentrations have been considered as functions of irradiation time and cumulative photonic energy Qhν incident on the reactor surface (kJ/L). In the tested experimental conditions, at tens mg/L pollutant concentration, increase of 0,5%-TiO2 dose up to 200mg/L leads to the enhancement of CB degradation efficiency. Since, doubling of TNT content has a negative effect on pollutant degradation efficiency, in similar experimental condition, prolonged irradiation time from 360 to 480 min was necessary in order to assures the compliance of treated effluent with limits imposed by EU legislation (TNT ≤ 10µg/L).

Keywords: wastewater treatment, TNT, photocatalysis, environmental engineering

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14 Tool for Determining the Similarity between Two Web Applications

Authors: Doru Anastasiu Popescu, Raducanu Dragos Ionut


In this paper the presentation of a tool which measures the similarity between two websites is made. The websites are compound only from webpages created with HTML. The tool uses three ways of calculating the similarity between two websites based on certain results already published. The first way compares all the webpages within a website, the second way compares a webpage with all the pages within the second website and the third way compares two webpages. Java programming language and technologies such as spring, Jsoup, log4j were used for the implementation of the tool.

Keywords: Java, Jsoup, HTM, spring

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13 Comparative Analysis of Classical and Parallel Inpainting Algorithms Based on Affine Combinations of Projections on Convex Sets

Authors: Irina Maria Artinescu, Costin Radu Boldea, Eduard-Ionut Matei


The paper is a comparative study of two classical variants of parallel projection methods for solving the convex feasibility problem with their equivalents that involve variable weights in the construction of the solutions. We used a graphical representation of these methods for inpainting a convex area of an image in order to investigate their effectiveness in image reconstruction applications. We also presented a numerical analysis of the convergence of these four algorithms in terms of the average number of steps and execution time in classical CPU and, alternatively, in parallel GPU implementation.

Keywords: convex feasibility problem, convergence analysis, inpainting, parallel projection methods

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12 Application of Deep Neural Networks to Assess Corporate Credit Rating

Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu


In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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11 Thermodynamic Cycle Analysis for Overall Efficiency Improvement and Temperature Reduction in Gas Turbines

Authors: Jeni A. Popescu, Ionut Porumbel, Valeriu A. Vilag, Cleopatra F. Cuciumita


The paper presents a thermodynamic cycle analysis for three turboshaft engines. The first is the cycle is a Brayton cycle, describing the evolution of a classical turboshaft, based on the Klimov TV2 engine. The other two cycles aim at approaching an Ericsson cycle, by replacing the Brayton cycle adiabatic expansion in the turbine by quasi-isothermal expansion. The maximum quasi-Ericsson cycles temperature is set to a lower value than the maximum Brayton cycle temperature, equal to the Brayton cycle power turbine inlet temperature, in order to decrease the engine NOx emissions. Also, the power distribution over the stages of the gas generator turbine is maintained the same. In the first of the two considered quasi-Ericsson cycle, the efficiencies of the gas generator turbine stage. Also, the power distribution over the stages of the gas generator turbine is maintained the same. In the first of the two considered quasi-Ericsson cycle, the efficiencies of the gas generator turbine stages are maintained the same as for the reference case, while for the second, the efficiencies are increased in order to obtain the same shaft power as in the reference case. It is found that in the first case, both the shaft power and the thermodynamic efficiency of the engine decrease, while in the second, the power is maintained, and even a slight increase in efficiency can be noted.

Keywords: combustion, Ericsson, thermodynamic analysis, turbine

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10 Bacterial Cellulose/Silver-Doped Hydroxyapatite Composites for Tissue Engineering Application

Authors: Adrian Ionut Nicoara, Denisa Ionela Ene, Alina Maria Holban, Cristina Busuioc


At present, the development of materials with biomedical applications is a domain of interest that will produce a full series of benefits in engineering and medicine. In this sense, it is required to use a natural material, and this paper is focused on the development of a composite material based on bacterial cellulose – hydroxyapatite and silver nanoparticles with applications in hard tissue. Bacterial cellulose own features like biocompatibility, non-toxicity character and flexibility. Moreover, the bacterial cellulose can be conjugated with different forms of active silver to possess antimicrobial activity. Hydroxyapatite is well known that can mimic at a significant level the activity of the initial bone. The material was synthesized by using an ultrasound probe and finally characterized by several methods. Thereby, the morphological properties were analyzed by using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). Because the synthesized material has medical application in restore the tissue and to fight against microbial invasion, the samples were tested from the biological point of view by evaluating the biodegradability in phosphate-buffered saline (PBS) and simulated body fluid (SBF) and moreover the antimicrobial effect was performed on Gram-positive bacterium Staphylococcus aureus, Gram-negative bacterium Escherichia coli, and fungi Candida albicans. The results reveal that the obtained material has specific characteristics for bone regeneration.

Keywords: bacterial cellulose, biomaterials, hydroxyapatite, scaffolds materials

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9 Hidden Markov Model for Financial Limit Order Book and Its Application to Algorithmic Trading Strategy

Authors: Sriram Kashyap Prasad, Ionut Florescu


This study models the intraday asset prices as driven by Markov process. This work identifies the latent states of the Hidden Markov model, using limit order book data (trades and quotes) to continuously estimate the states throughout the day. This work builds a trading strategy using estimated states to generate signals. The strategy utilizes current state to recalibrate buy/ sell levels and the transition between states to trigger stop-loss when adverse price movements occur. The proposed trading strategy is tested on the Stevens High Frequency Trading (SHIFT) platform. SHIFT is a highly realistic market simulator with functionalities for creating an artificial market simulation by deploying agents, trading strategies, distributing initial wealth, etc. In the implementation several assets on the NASDAQ exchange are used for testing. In comparison to a strategy with static buy/ sell levels, this study shows that the number of limit orders that get matched and executed can be increased. Executing limit orders earns rebates on NASDAQ. The system can capture jumps in the limit order book prices, provide dynamic buy/sell levels and trigger stop loss signals to improve the PnL (Profit and Loss) performance of the strategy.

Keywords: algorithmic trading, Hidden Markov model, high frequency trading, limit order book learning

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8 Author Profiling: Prediction of Learners’ Gender on a MOOC Platform Based on Learners’ Comments

Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea


The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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7 Lower Limb Oedema in Beckwith-Wiedemann Syndrome

Authors: Mihai-Ionut Firescu, Mark A. P. Carson


We present a case of inferior vena cava agenesis (IVCA) associated with bilateral deep venous thrombosis (DVT) in a patient with Beckwith-Wiedemann syndrome (BWS). In adult patients with BWS presenting with bilateral lower limb oedema, specific aetiological factors should be considered. These include cardiomyopathy and intraabdominal tumours. Congenital malformations of the IVC, through causing relative venous stasis, can lead to lower limb oedema either directly or indirectly by favouring lower limb venous thromboembolism; however, they are yet to be reported as an associated feature of BWS. Given its life-threatening potential, the prompt initiation of treatment for bilateral DVT is paramount. In BWS patients, however, this can prove more complicated. Due to overgrowth, the above-average birth weight can continue throughout childhood. In this case, the patient’s weight reached 170 kg, impacting on anticoagulation choice, as direct oral anticoagulants have a limited evidence base in patients with a body mass above 120 kg. Furthermore, the presence of IVCA leads to a long-term increased venous thrombosis risk. Therefore, patients with IVCA and bilateral DVT warrant specialist consideration and may benefit from multidisciplinary team management, with hematology and vascular surgery input. Conclusion: Here, we showcased a rare cause for bilateral lower limb oedema, respectively bilateral deep venous thrombosis complicating IVCA in a patient with Beckwith-Wiedemann syndrome. The importance of this case lies in its novelty, as the association between IVC agenesis and BWS has not yet been described. Furthermore, the treatment of DVT in such situations requires special consideration, taking into account the patient’s weight and the presence of a significant, predisposing vascular abnormality.

Keywords: Beckwith-Wiedemann syndrome, bilateral deep venous thrombosis, inferior vena cava agenesis, venous thromboembolism

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6 The Relationship Between Cyberbullying Victimization, Parent and Peer Attachment and Unconditional Self-Acceptance

Authors: Florina Magdalena Anichitoae, Anca Dobrean, Ionut Stelian Florean


Due to the fact that cyberbullying victimization is an increasing problem nowadays, affecting more and more children and adolescents around the world, we wanted to take a step forward analyzing this phenomenon. So, we took a look at some variables which haven't been studied together before, trying to develop another way to view cyberbullying victimization. We wanted to test the effects of the mother, father, and peer attachment on adolescent involvement in cyberbullying as victims through unconditional self acceptance. Furthermore, we analyzed each subscale of the IPPA-R, the instrument we have used for parents and peer attachment measurement, in regards to cyberbullying victimization through unconditional self acceptance. We have also analyzed if gender and age could be taken into consideration as moderators in this model. The analysis has been performed on 653 adolescents aged 11-17 years old from Romania. We used structural equation modeling, working in R program. For the fidelity analysis of the IPPA-R subscales, USAQ, and Cyberbullying Test, we have calculated the internal consistency index, which varies between .68-.91. We have created 2 models: the first model including peer alienation, peer trust, peer communication, self acceptance and cyberbullying victimization, having CFI=0.97, RMSEA=0.02, 90%CI [0.02, 0.03] and SRMR=0.07, and the second model including parental alienation, parental trust, parental communication, self acceptance and cyberbullying victimization and had CFI=0.97, RMSEA=0.02, 90%CI [0.02, 0.03] and SRMR=0.07. Our results were interesting: on one hand, cyberbullying victimization is predicted by peer alienation and peer communication through unconditional self acceptance. Peer trust directly, significantly, and negatively predicted the implication in cyberbullying. In this regard, considering gender and age as moderators, we found that the relationship between unconditional self acceptance and cyberbullying victimization is stronger in girls, but age does not moderate the relationship between unconditional self acceptance and cyberbullying victimization. On the other hand, regarding the degree of cyberbullying victimization as being predicted through unconditional self acceptance by parental alienation, parental communication, and parental trust, this hypothesis was not supported. Still, we could identify a direct path to positively predict victimization through parental alienation and negatively through parental trust. There are also some limitations to this study, which we've discussed in the end.

Keywords: adolescent, attachment, cyberbullying victimization, parents, peers, unconditional self-acceptance

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5 Biopolymers: A Solution for Replacing Polyethylene in Food Packaging

Authors: Sonia Amariei, Ionut Avramia, Florin Ursachi, Ancuta Chetrariu, Ancuta Petraru


The food industry is one of the major generators of plastic waste derived from conventional synthetic petroleum-based polymers, which are non-biodegradable, used especially for packaging. These packaging materials, after the food is consumed, accumulate serious environmental concerns due to the materials but also to the organic residues that adhere to them. It is the concern of specialists, researchers to eliminate problems related to conventional materials that are not biodegradable or unnecessary plastic and replace them with biodegradable and edible materials, supporting the common effort to protect the environment. Even though environmental and health concerns will cause more consumers to switch to a plant-based diet, most people will continue to add more meat to their diet. The paper presents the possibility of replacing the polyethylene packaging from the surface of the trays for meat preparations with biodegradable packaging obtained from biopolymers. During the storage of meat products may occur deterioration by lipids oxidation and microbial spoilage, as well as the modification of the organoleptic characteristics. For this reason, different compositions of polymer mixtures and film conditions for obtaining must be studied to choose the best packaging material to achieve food safety. The compositions proposed for packaging are obtained from alginate, agar, starch, and glycerol as plasticizers. The tensile strength, elasticity, modulus of elasticity, thickness, density, microscopic images of the samples, roughness, opacity, humidity, water activity, the amount of water transferred as well as the speed of water transfer through these packaging materials were analyzed. A number of 28 samples with various compositions were analyzed, and the results showed that the sample with the highest values for hardness, density, and opacity, as well as the smallest water vapor permeability, of 1.2903E-4 ± 4.79E-6, has the ratio of components as alginate: agar: glycerol (3:1.25:0.75). The water activity of the analyzed films varied between 0.2886 and 0.3428 (aw< 0.6), demonstrating that all the compositions ensure the preservation of the products in the absence of microorganisms. All the determined parameters allow the appreciation of the quality of the packaging films in terms of mechanical resistance, its protection against the influence of light, the transfer of water through the packaging. Acknowledgments: This work was supported by a grant of the Ministry of Research, Innovation, and Digitization, CNCS/CCCDI – UEFISCDI, project number PN-III-P2-2.1-PED-2019-3863, within PNCDI III.

Keywords: meat products, alginate, agar, starch, glycerol

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4 Relationship between Structure of Some Nitroaromatic Pollutants and Their Degradation Kinetic Parameters in UV-VIS/TIO2 System

Authors: I. Nitoi, P. Oancea, M. Raileanu, M. Crisan, L. Constantin, I. Cristea


Hazardous organic compounds like nitroaromatics are frequently found in chemical and petroleum industries discharged effluents. Due to their bio-refractory character and high chemical stability cannot be efficiently removed by classical biological or physical-chemical treatment processes. In the past decades, semiconductor photocatalysis has been frequently applied for the advanced degradation of toxic pollutants. Among various semiconductors titania was a widely studied photocatalyst, due to its chemical inertness, low cost, photostability and nontoxicity. In order to improve optical absorption and photocatalytic activity of TiO2 many attempts have been made, one feasible approach consists of doping oxide semiconductor with metal. The degradation of dinitrobenzene (DNB) and dinitrotoluene (DNT) from aqueous solution under UVA-VIS irradiation using heavy metal (0.5% Fe, 1%Co, 1%Ni ) doped titania was investigated. The photodegradation experiments were carried out using a Heraeus laboratory scale UV-VIS reactor equipped with a medium-pressure mercury lamp which emits in the range: 320-500 nm. Solutions with (0.34-3.14) x 10-4 M pollutant content were photo-oxidized in the following working conditions: pH = 5-9; photocatalyst dose = 200 mg/L; irradiation time = 30 – 240 minutes. Prior to irradiation, the photocatalyst powder was added to the samples, and solutions were bubbled with air (50 L/hour), in the dark, for 30 min. Dopant type, pH, structure and initial pollutant concentration influence on the degradation efficiency were evaluated in order to set up the optimal working conditions which assure substrate advanced degradation. The kinetics of nitroaromatics degradation and organic nitrogen mineralization was assessed and pseudo-first order rate constants were calculated. Fe doped photocatalyst with lowest metal content (0.5 wt.%) showed a considerable better behaviour in respect to pollutant degradation than Co and Ni (1wt.%) doped titania catalysts. For the same working conditions, degradation efficiency was higher for DNT than DNB in accordance with their calculated adsobance constants (Kad), taking into account that degradation process occurs on catalyst surface following a Langmuir-Hinshalwood model. The presence of methyl group in the structure of DNT allows its degradation by oxidative and reductive pathways, while DNB is converted only by reductive route, which also explain the highest DNT degradation efficiency. For highest pollutant concentration tested (3 x 10-4 M), optimum working conditions (0.5 wt.% Fe doped –TiO2 loading of 200 mg/L, pH=7 and 240 min. irradiation time) assures advanced nitroaromatics degradation (ηDNB=89%, ηDNT=94%) and organic nitrogen mineralization (ηDNB=44%, ηDNT=47%).

Keywords: hazardous organic compounds, irradiation, nitroaromatics, photocatalysis

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3 Statistical Comparison of Ensemble Based Storm Surge Forecasting Models

Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg


Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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2 Row Detection and Graph-Based Localization in Tree Nurseries Using a 3D LiDAR

Authors: Ionut Vintu, Stefan Laible, Ruth Schulz


Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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1 Synthesis of Smart Materials Based on Polyaniline Coated Fibers

Authors: Mihaela Beregoi, Horia Iovu, Cristina Busuioc, Alexandru Evanghelidis, Elena Matei, Monica Enculescu, Ionut Enculescu


Nanomaterials field is very attractive for all researchers who are attempting to develop new devices with the same or improved properties than the micro-sized ones, while reducing the reagents and power consumptions. In this way, a wide range of nanomaterials were fabricated and integrated in applications for electronics, optoelectronics, solar cells, tissue reconstruction and drug delivery. Obviously, the most appealing ones are those dedicated to the medical domain. Different types of nano-sized materials, such as particles, fibers, films etc., can be synthesized by using physical, chemical or electrochemical methods. One of these techniques is electrospinning, which enable the production of fibers with nanometric dimensions by pumping a polymeric solution in a high electric field; due to the electrostatic charging and solvent evaporation, the precursor mixture is converted into nonwoven meshes with different fiber densities and mechanical properties. Moreover, polyaniline is a conducting polymer with interesting optical properties, suitable for displays and electrochromic windows. Otherwise, polyaniline is an electroactive polymer that can contract/expand by applying electric stimuli, due to the oxidation/reduction reactions which take place in the polymer chains. These two main properties can be exploited in order to synthesize smart materials that change their dimensions, exhibiting in the same time good electrochromic properties. In the context aforesaid, a poly(methyl metacrylate) solution was spun to get webs composed of fibers with diameter values between 500 nm and 1 µm. Further, the polymer meshes were covered with a gold layer in order to make them conductive and also appropriate as working electrode in an electrochemical cell. The gold shell was deposited by DC sputtering. Such metalized fibers can be transformed into smart materials by covering them with a thin layer of conductive polymer. Thus, the webs were coated with a polyaniline film by the electrochemical route, starting from and aqueous solution of aniline and sulfuric acid, where sulfuric acid acts as oxidant agent. For the polymerization of aniline, a saturated calomel electrode was employed as reference, a platinum plate as counter electrode and the gold covered webs as working electrode. Chronoamperometry was selected as deposition method for polyaniline, by modifying the deposition time. Metalized meshes with different fiber densities were used, the transmission ranging between 70 and 80 %. The morphological investigation showed that polyaniline layer has a granular structure for all deposition experiments. As well, some preliminary optical tests were done by using sulfuric acid as electrolyte, which revealed the modification of polyaniline colour from green to dark blue when applying a voltage. In conclusion, new multilayered materials were obtained by a simple approach: the merge of the electrospinning method benefits with polyaniline chemistry. This synthesis method allows the fabrication of structures with reproducible characteristics, suitable for display or tissue substituents.

Keywords: electrospinning, fibers, smart materials, polyaniline

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