In this research work, carbon nanofibers (CNFs) were synthesized on honeycomb monolith substrates using injection chemical vapor deposition (ICVD) technique. The effect of various wash-coated materials and catalyst promoter on the growth rate of CNFs on monolith substrates were examined. The characteristics of the synthesized CNFs-coated monolith composites were examined using Raman spectroscopy, Brunauer-Emmett-Teller (BET), thermogravimetric analysis (TGA), field emission scanning electron microscopy (FE-SEM), and Transmission electron microscopy (TEM) techniques. According to the textural characterization study, the specific surface area and pore volume of CNFs-coated monolith composites were significantly improved as compared to bare monolith which might be attributed to the growth of highly pure and aligned CNFs over monolith substrate. Besides that, the synthesized CNFs-coated monolith possessed extremely well thermal stability up to the temperature of 550 °C which was corresponded to the strong attachment of highly graphitized CNFs over monolith substrates.
The mechanistic modeling of the sulfation reaction between fly ash-based sorbent and SO2 is a challenging task due to a variety reasons including the complexity of the reaction itself and the inability to measure some of the key parameters of the reaction. In this work, the possibility of modeling the sulfation reaction kinetics using a purely data-driven neural network was investigated. Experiments on SO2 removal by a sorbent prepared from coal fly ash/CaO/CaSO4 were conducted using a fixed bed reactor to generate a database to train and validate the neural network model. Extensive SO2 removal data points were obtained by varying three process variables, namely, SO2 inlet concentration (500-2000 mg/L), reaction temperature (60-80 degreesC), and relative humidity (50-70%), as a function of reaction time (0-60 min). Modeling results show that the neural network can provide excellent fits to the SO2 removal data after considerable training and can be successfully used to predict the extent of SO2 removal as a function of time even when the process variables are outside the training domain. From a modeling standpoint, the suitably trained and validated neural network with excellent interpolation and extrapolation properties could have immediate practical benefits in the absence of a theoretical model.
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m(2)g(-1) could be obtained by mixing 13.1g of coal fly ash and 5.5 g of calcium sulfate in a hydration process containing 100ml of water and 5 g of calcium oxide for a fixed hydration time of 10 h.
Polypyrrole (PPy) and polypyrrole-carboxylic functionalized multi wall carbon nanotube composites (PPy/f-MWCNT) were synthesized by in situ chemical oxidative polymerization of pyrrole on the carbon nanotubes (CNTs). The structure of the resulting complex nanotubes was characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The effects of f-MWCNT concentration on the electrical properties of the resulting composites were studied at temperatures between 100 K and 300 K. The Hall mobility and Hall coefficient of PPy and PPy/f-MWCNT composite samples with different concentrations of f-MWCNT were measured using the van der Pauw technique. The mobility decreased slightly with increasing temperature, while the conductivity was dominated by the gradually increasing carrier density.
Here is presented a systematic study of the dispersibility of multiwall carbon nanotubes (MWCNTs) in natural rubber latex (NR-latex) assisted by a series of single-, double-, and triple-sulfosuccinate anionic surfactants containing phenyl ring moieties. Optical polarising microscopy, field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and Raman spectroscopy have been performed to obtain the dispersion-level profiles of the MWCNTs in the nanocomposites. Interestingly, a triple-chain, phenyl-containing surfactant, namely sodium 1,5-dioxo-1,5-bis(3-phenylpropoxy)-3-((3-phenylpropoxy)carbonyl) pentane-2-sulfonate (TCPh), has a greater capacity the stabilisation of MWCNTs than a commercially available single-chain sodium dodecylbenzenesulfonate (SDBS) surfactant. TCPh provides significant enhancements in the electrical conductivity of nanocomposites, up to ∼10(-2) S cm(-1), as measured by a four-point probe instrument. These results have allowed compilation of a road map for the design of surfactant architectures capable of providing the homogeneous dispersion of MWCNTs required for the next generation of polymer-carbon-nanotube materials, specifically those used in aerospace technology.
Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.
The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.
In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10-5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.
The seeds (6.9±0.2% by weight of fruit) of the red-skin rambutan (Nephelium lappaceum L.) contain a considerable amount of crude fat (38.0±4.36%) and thus, the aim of the study was to determine the physico-chemical properties of this fat for potential applications. The iodine and saponification values, and unsaponifiable matter and free fatty acid contents of the seed fat were 50.27 g I2/100g fat, 182.1 mg KOH/g fat, 0.8% and 2.1%, respectively. The fat is pale yellow with a Lovibond color index of 3.1Y+1.1R. The fatty acid profile indicates an almost equal proportion of saturated (49.1%) and unsaturated (50.9%) fatty acids, where oleic (42.0%) and arachidic (34.3%) acids were the most dominant fatty acids. It also contained small amounts of stearic (8.0%), palmitic (4.6%), gadoleic (5.9%), linoleic (2.2%), behenic (2.1%) palmitoleic (0.7%) myristic (0.1%) and erucic (0.1%) acids. HPLC analysis showed that the fat comprised mainly unknown triacylglycerols (TAG) with high retention times indicating they have higher carbon numbers compared with many vegetable oils. The fat has melting and cooling points of 44.2°C and -42.5°C, respectively, making it a semi-solid at room temperature. The solid content at 0°C was 53.5% and the fat melted completely at 40°C. z-Nose analysis showed that the presence of high levels of volatile compounds in red-skin rambutan seed and seed fat.
Relatively, little is known about the relationship between biodiversity and ecosystem functioning in forests, especially in the tropics. We describe the Sabah Biodiversity Experiment: a large-scale, long-term field study on the island of Borneo. The project aims at understanding the relationship between tree species diversity and the functioning of lowland dipterocarp rainforest during restoration following selective logging. The experiment is planned to run for several decades (from seed to adult tree), so here we focus on introducing the project and its experimental design and on assessing initial conditions and the potential for restoration of the structure and functioning of the study system, the Malua Forest Reserve. We estimate residual impacts 22 years after selective logging by comparison with an appropriate neighbouring area of primary forest in Danum Valley of similar conditions. There was no difference in the alpha or beta species diversity of transect plots in the two forest types, probably owing to the selective nature of the logging and potential effects of competitive release. However, despite equal total stem density, forest structure differed as expected with a deficit of large trees and a surfeit of saplings in selectively logged areas. These impacts on structure have the potential to influence ecosystem functioning. In particular, above-ground biomass and carbon pools in selectively logged areas were only 60 per cent of those in the primary forest even after 22 years of recovery. Our results establish the initial conditions for the Sabah Biodiversity Experiment and confirm the potential to accelerate restoration by using enrichment planting of dipterocarps to overcome recruitment limitation. What role dipterocarp diversity plays in restoration only will become clear with long-term results.
Various phenolic compounds have been screened against Ganoderma boninense, the fungal pathogen causing basal stem rot in oil palms. In this study, we focused on the effects of salicylic acid (SA) on the growth of three G. boninense isolates with different levels of aggressiveness. In addition, study on untargeted metabolite profiling was conducted to investigate the metabolomic responses of G. boninense towards salicylic acid. The inhibitory effects of salicylic acid were both concentration- (P < 0.001) and isolate-dependent (P < 0.001). Also, growth-promoting effect was observed in one of the isolates at low concentrations of salicylic acid where it could have been utilized by G. boninense as a source of carbon and energy. Besides, adaptation towards salicylic acid treatment was evident in this study for all isolates, particularly at high concentrations. In other words, inhibitory effect of salicylic acid treatment on the fungal growth declined over time. In terms of metabolomics response to salicylic acid treatment, G. boninense produced several metabolites such as coumarin and azatyrosine, which suggests that salicylic acid modulates the developmental switch in G. boninense towards the defense mode for its survival. Furthermore, the liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) analysis showed that the growth of G. boninense on potato dextrose agar involved at least four metabolic pathways: amino acid metabolism, lipid pathway, tryptophan pathway and phenylalanine pathway. Overall, there were 17 metabolites that contributed to treatment separation, each with P<0.005. The release of several antimicrobial metabolites such as eudistomin I may enhance G. boninense's competitiveness against other microorganisms during colonisation. Our findings demonstrated the metabolic versatility of G. boninense towards changes in carbon sources and stress factors. G. boninense was shown to be capable of responding to salicylic acid treatment by switching its developmental stage.
The pyrolysis of karanj fruit hulls (KFH) and karanj fruit hull hydrothermal carbonization (KFH-HTC) hydrochar was thermogravimetrically investigated under a nitrogen environment at 5 °C/min, 10 °C/min, and 20 °C/min. The pyrolysis decomposition of KFH biomass was faster than that of KFH-HTC hydrochar because of the high volatility and fixed carbon of KFH biomass. Weight loss percentage was also affected by the heating rates. The kinetic data were evaluated with the Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa methods. The activation energy values obtained with these two methods were 61.06 and 68.53 kJ/mol for KFH biomass and 130.49 and 135.87 kJ/mol for KFH-HTC hydrochar, respectively. The analysis of kinetic process mechanisms was verified with the Coats-Redfern method. KFH-HTC hydrochar may play a potential role in transforming biomass to energy-rich feedstock for thermochemical applications because of its high heating value, high fixed carbon, and low ash and sulfur contents.
Wood sawdust was converted into a high-quality activated carbon (WSAC) via microwave-induced K(2)CO(3) activation. The operational variables including chemical impregnation ratio, microwave power and irradiation time on the carbon yield and adsorption capability were identified. The surface physical characteristics of WSAC were examined by pore structural analysis, scanning electron microscopy and nitrogen adsorption isotherms. The adsorptive behavior of WSAC was quantified using methylene blue as model dye compound. The best conditions resulted in activated carbon with a monolayer adsorption capacity of 423.17 mg/g and carbon yield of 80.75%. The BET surface area, Langmuir surface area and total pore volume were corresponded to 1496.05 m(2)/g, 2245.53 m(2)/g and 0.864 cm(3)/g, respectively. The findings support the potential to prepare high surface area and mesoporous activated carbon from wood sawdust by microwave assisted chemical activation.
Sunflower seed oil residue, a by-product of sunflower seed oil refining, was utilized as a feedstock for preparation of activated carbon (SSHAC) via microwave induced K(2)CO(3) chemical activation. SSHAC was characterized by Fourier transform infrared spectroscopy, nitrogen adsorption-desorption and elemental analysis. Surface acidity/basicity was examined with acid-base titration, while the adsorptive properties of SSHAC were quantified using methylene blue (MB) and acid blue 15 (AB). The monolayer adsorption capacities of MB and AB were 473.44 and 430.37 mg/g, while the Brunauer-Emmett-Teller surface area, Langmuir surface area and total pore volume were 1411.55 m(2)/g, 2137.72 m(2)/g and 0.836 cm(3)/g, respectively. The findings revealed the potential to prepare high surface area activated carbon from sunflower seed oil residue by microwave irradiation.
Water scarcity and pollution rank equal to climate change as the most urgent environmental turmoil for the 21st century. To date, the percolation of textile effluents into the waterways and aquifer systems, remain an intricate conundrum abroad the nations. With the renaissance of activated carbon, there has been a steadily growing interest in the research field. Recently, the adoption of titanium dioxide, a prestigious advanced photo-catalyst which formulates the new growing branch of activated carbon composites for enhancement of adsorption rate and discoloration capacity, has attracted stern consideration and supports worldwide. Confirming the assertion, this paper presents a state of art review of titanium dioxide/activated carbon composites technology, its fundamental background studies, and environmental implications. Moreover, its major challenges together with the future expectation are summarized and discussed. Conclusively, the expanding of activated carbons composites material represents a potentially viable and powerful tool, leading to the plausible improvement of environmental conservation.
Water scarcity and pollution rank equal to climate change as the most urgent environmental issue for the 21st century. To date, the percolation landfill leachate into the groundwater tables and aquifer systems which poses a potential risk and potential hazards towards the public health and ecosystems, remains an aesthetic concern and consideration abroad the nations. Arising from the steep enrichment of globalization and metropolitan growth, numerous mitigating approaches and imperative technologies have currently drastically been addressed and confronted. Confirming the assertion, this paper presents a state of art review of leachate treatment technologies, its fundamental background studies, and environmental implications. Moreover, the key advance of activated carbons adsorption, its major challenges together with the future expectation are summarized and discussed. Conclusively, the expanding of activated carbons adsorption represents a potentially viable and powerful tool, leading to the superior improvement of environmental conservation.
In this work, the adsorption potential of bamboo waste based granular activated carbon (BGAC) to remove C.I. Reactive Black (RB5) from aqueous solution was investigated using fixed-bed adsorption column. The effects of inlet RB5 concentration (50-200mg/L), feed flow rate (10-30 mL/min) and activated carbon bed height (40-80 mm) on the breakthrough characteristics of the adsorption system were determined. The highest bed capacity of 39.02 mg/g was obtained using 100mg/L inlet dye concentration, 80 mm bed height and 10 mL/min flow rate. The adsorption data were fitted to three well-established fixed-bed adsorption models namely, Adam's-Bohart, Thomas and Yoon-Nelson models. The results fitted well to the Thomas and Yoon-Nelson models with coefficients of correlation R(2)>or=0.93 at different conditions. The BGAC was shown to be suitable adsorbent for adsorption of RB5 using fixed-bed adsorption column.
Concern about environmental protection has increased over the years from a global viewpoint. To date, the percolation of pesticide waste into the groundwater tables and aquifer systems remains an aesthetic issue towards the public health and food chain interference. With the renaissance of activated carbon, there has been a consistent growing interest in this research field. Confirming the assertion, this paper presents a state of art review of pesticide agrochemical practice, its fundamental characteristics, background studies and environmental implications. Moreover, the key advance of activated carbon adsorption, its major challenges together with the future expectation are summarized and discussed. Conclusively, the expanding of activated carbon adsorption represents a plausible and powerful circumstance, leading to the superior improvement of environmental preservation.
Oil palm fronds (OPF) were used to prepare activated carbon (PFAC) using physiochemical activation method, which consisted of potassium hydroxide (KOH) treatment and carbon dioxide gasification. The effects of the preparation variables, which were activation temperature, activation time and chemical impregnation ratios (KOH: char by weight), on the carbon yield and bentazon removal were investigated. Based on the central composite design (CCD), two factor interaction (2FI) and quadratic models were, respectively, employed to correlate the PFAC preparation variables to the bentazon removal and carbon yield. From the analysis of variance (ANOVA), the most influential factor on each experimental design response was identified. The optimum conditions for preparing activated carbon from OPF were found as follows: activation temperature of 850 degrees C, activation time of 1h and KOH:char ratio of 3.75:1. The predicted and experimental results for removal of bentazon and yield of PFAC were 99.85%, 20.5 and 98.1%, 21.6%, respectively.
This study investigated the adsorption potential of oil palm shell-based activated carbon to remove 2,4,6-trichlorophenol from aqueous solution using fixed-bed adsorption column. The effects of 2,4,6-trichlorophenol inlet concentration, feed flow rate and activated carbon bed height on the breakthrough characteristics of the adsorption system were determined. The regeneration efficiency of the oil palm shell-based activated carbon was evaluated using ethanol desorption technique. Through ethanol desorption, 96.25% of the adsorption sites could be recovered from the regenerated activated carbon.