New biomass derived carbon catalysts for biomass valorization

Due to diminishing petroleum reserves, unsteady market situation and the environmental concerns associated with utilization of fossil resources, the utilization of renewables for production of energy and chemicals (biorefining) has gained considerable attention. Biomass is the only sustainable source of organic compounds that has been proposed as petroleum equivalent for the production of fuels, chemicals and materials. In fact, it would not be wrong to say that the only viable answer to sustainably convene our future energy and material requirements remain with a bio-based economy with biomass based industries and products. This has prompted biomass valorization (biorefining) to become an important area of industrial research. While many disciplines of science are involved in the realization of this effort, catalysis and knowledge of chemical technology are considered to be particularly important to eventually render this dream to come true. Traditionally, the catalyst research for biomass conversion has been focused primarily on commercially available catalysts like zeolites, silica and various metals (Pt, Pd, Au, Ni) supported on zeolites, silica etc. Nevertheless, the main drawbacks of these catalysts are coupled with high material cost, low activity, limited reusability etc. – all facts that render them less attractive in industrial scale applications (poor activity for the price). Thus, there is a particular need to develop active, robust and cost efficient catalytic systems capable of converting complex biomass molecules. Saccharification, esterification, transesterification and acetylation are important chemical processes in the valorization chain of biomasses (and several biomass components) for production of platform chemicals, transportation fuels, food additives and materials. In the current work, various novel acidic carbons were synthesized from wastes generated from biodiesel and allied industries, and employed as catalysts in the aforementioned reactions. The structure and surface properties of the novel materials were investigated by XRD, XPS, elemental analysis, SEM, TEM, TPD and N2-physisorption techniques. The agro-industrial waste derived sulfonic acid functionalized novel carbons exhibit excellent catalytic activity in the aforementioned reactions and easily outperformed liquid H2SO4 and conventional solid acids (zeolites, ion-exchange resins etc). The experimental results indicated strong influence of catalyst pore-structure (pore size, pore-volume), concentration of –SO3H groups and surface properties in terms of the activity and selectivity of these catalysts. Here, a large pore catalyst with high –SO3H density exhibited the highest esterification and transesterification activity, and was successfully employed in biodiesel production from fatty acids and low grade acidic oils. Also, a catalyst decay model was proposed upon biodiesel production and could explain that the catalyst loses its activity mainly due to active site blocking by adsorption of impurities and by-products. The large pore sulfonated catalyst also exhibited good catalytic performance in the selective synthesis of triacetin via acetylation of glycerol with acetic anhydride and out-performed the best zeolite H-Y with respect to reusability. It also demonstrated equally good activity in acetylation of cellulose to soluble cellulose acetates, with the possibility to control cellulose acetate yield and quality (degree of substitution, DS) by a simple adjustment of reaction time and acetic anhydride concentration. In contrast, the small pore and highly functionalized catalysts obtained by hydrothermal method and from protein rich waste (Jatropha de-oiled waste cake, DOWC), were active and selective in the esterification of glycerol with fatty acids to monoglycerides and saccharification of cellulosic materials, respectively. The operational stability and reusability of the catalyst was found to depend on the stability of –SO3H function (leaching) as well as active site blocking due to adsorption of impurities during the reaction. Thus, our results corroborate the potential of DOWC derived sulfated mesoporous active carbons as efficient integrated solid acid catalysts for valorization of biomass to platform chemicals, biofuel, bio-additive, surfactants and celluloseesters.

Ultrasound-assisted electrochemical treatment of wastewaters containing organic pollutants by using novel Ti/Ta2O5-SnO2 electrodes

Advanced oxidation processes (AOPs) are modern methods using reactive hydroxyl radicals for the mineralization of organic pollutants into simple inorganic compounds, such as CO2 and H2O. Among AOPs electrochemical oxidation (EO) is a method suitable for coloured and turbid wastewaters. The degradation of pollutants occurs on electrocatalytic electrodes. The majority of electrodes contain in their structure either expensive materials (diamond and Pt-group metals) or are toxic for the environment compounds (Sb or Pb). One of the main disadvantages of electrochemical method is the polarization and contamination of electrodes due to the deposition of reaction products on their surface, which results in diminishing of the process efficiency. Ultrasound combined with the electrochemical degradation process eliminates electrode contamination because of the continuous mechanical cleaning effect produced by the formation and collapse of acoustic cavitation bubbles near to the electrode surface. Moreover, high frequency ultrasound generates hydroxyl radicals at water sonolysis. Ultrasound-assisted EO is a non-selective method for oxidation of different organic compounds with high degradation efficiencies. The aim of this research was to develop novel sustainable and cost-effective electrodes working as electrocatalysts and test their activity in electrocatalytic oxidation of organic compounds such as dyes and organic acids. Moreover, the goal of the research was to enhance the efficiency of electrocatalytic degradation processes by assisting it with ultrasound in order to eliminate the main drawbacks of a single electrochemical oxidation such as electrodes polarization and passivation. Novel Ti/Ta2O5-SnO2 electrodes were developed and found to be electrocatalytically active towards water (with 5% Ta content, 10 oxide film layers) and organic compounds oxidation (with 7.5% Ta content, 8 oxide film layers) and therefore these electrodes can be applicable in both environmental and energy fields. The synergetic effect of combined electrolysis and sonication was shown while conducting sonoelectrochemical (EO/US) degradation of methylene blue (MB) and formic acid (FA). Complete degradation of MB and FA was achieved after 45 and 120 min of EO/US process respectively in neutral media. Mineralization efficiency of FA over 95% was obtained after 2 h of degradation using high frequency ultrasound (381, 863, 1176 kHz) combined with 9.1 mA/cm2 current density. EO/US degradation of MB provided over 75% mineralization in 8 h. High degradation kinetic rates and mineralization efficiencies of model pollutants obtained in EO/US experiments provide the preconditions for further extrapolation of this treatment method to pilot scale studies with industrial wastewaters.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.