Sequestration of a food dye (sunset yellow) from wastewater using natural adsorbent: a kinetic, isotherm and interference study.

Cocos nucifera, commonly known as coconut is rich in coir dust (CCD) at its outer surface, which is a very significant agri waste used as biosorbent for wastewater treatment. The current work addresses use of CCD for removal of hazardous Sunset Yellow dye (SY) FCF widely used as coloring agent in food industry, from wastewater. The uptake capacity in batch and column mode is 82 mg/g and 160 mg/g respectively. Characterization study including SEM, FTIR and BET results also supported the adsorption process. The comparative analysis with other natural biosorbents showed best results of biosorption with CCD. The output was better at high pH (10) and lower concentration of dye (5 mg/L). The kinetic study suggested pseudo second order rate revealing both adsorbate-adsorbent interdependency. The presence of covalent bonding or valence forces between the interfaces, suggested chemisorption as the rate limiting mechanism with valence forces, hydrogen bonding and pi-pi stacking being the chief forces responsible in binding of the dye molecules to the surface. The isotherm supported Langmuir model with monolayer and uniform adsorption at the interfaces. The interference test confirmed slight decrease in percent adsorption with interference from chloride and sulfate as dominating ions. The techno-economic feasibility highly recommended in field application of the substitute (net profit value, 1.256 Rs/m3, input cost, 0.052 Rs/m3). The industrial sample analysis with lab to land approach justified sustainability and commercial viability of the present work.

Facile removal of a food dye (sunset yellow, FCF) using Coconut coir dust (CCD).Uptake capacity in batch and column mode is 82 mg/g and 160 mg/g respectively.Chemisorption as the rate limiting mechanism with valence forces, hydrogen bonding and pi-pi stacking being the chief forces.Better uptake efficiency is seen at higher pH (10) and lower concentration (5mg/L).

Thiosulfate impregnated spent tea leaves for the remarkable uptake of malachite green.

Herein we demonstrate an enhanced performance of acid-assisted thiosulfate-impregnated spent/waste tea leaves (TWTL) for the removal of malachite green (MG) from water by batch mode. The material was characterized by pHZPC, FTIR, powder XRD, SEM, and proximate analysis. FTIR suggests the presence of polyphenolic moieties whereas a lignocellulosic peak was observed in powder XRD. SEM image shows a grafted surface texture with intermittent blocks, which upon dye uptake becomes somewhat condensed. Under optimized conditions, the highest removal efficiency of 126.8 mg/g was achieved at pH 7. A fast adsorption process was noticed with >97% removal within the first 10 min. Adsorption follows pseudo-second-order kinetics (R2 = 0.999) and the Langmuir model (R2 = 0.999). The material can be regenerated by dilute hydrochloric acid and can be reused for up to four cycles. Treatment of industrial effluent was successful in up to 47.56%. Our results highlight the potential of thiosulfate-treated spent tea leaves as a choice for the efficient removal of malachite green from water.

Tea, being one of the most popular beverages produces huge waste which requires proper management. With this aim; the thiosulfate-impregnated spent tea leaves have been exercised for effective separation of malachite green from contaminated water. Thiosulfate impregnation under mildly acidic conditions activates the tea leaves and makes the material robust with enhanced water stability than its untreated variety. With a remarkable maximum adsorption capacity of 126.8 mg/g under ambient conditions, the present methodology enjoys the edge over related phytosorbents. The protocol is techno-economic, environment friendly, and could be extended to possible field applications.

Alkali treated water chestnut (Trapa natans L.) shells as a promising phytosorbent for malachite green removal from water.

Search for eco-friendly adsorbents for sustainable dye treatment is on the rise. The present study demonstrated the enhanced removal of malachite green (MG) with alkali-modified shells of water chestnut (AWCN) under optimized physio-chemical parameters. Alkali treatment significantly reduces the lignocellulosic components which in turn increased the water stability. The material was been characterized by pHzpc, FTIR, FESEM-EDAX, and BET surface area analysis. pH-dependent adsorption was noticed and the maximum adsorption capacity was determined as 136.46 mg/g. Adsorption followed pseudo-second-order kinetics (R2=0.99) and Langmuir isotherm model (R2=0.99). Thermodynamic parameters suggested that the adsorption process is spontaneous (ΔG°= -2.99 kJ/mol), favorable and endothermic (ΔH°=34.72 kJ/mol). Simple regeneration allows multi-cycle use with minimal loss of activity. The mechanism has been proposed to be a combination of electrostatic interaction, H-bonding, and π-π stacking between AWCN and MG. In conclusion, alkali modification of Trapa natans L. shells provides excellent removal of MG from water.

Waste shells of water chestnut (Trapa natans L.), a waterborne fruit have been modified using sodium hydroxide solution and tested for removal of malachite green by batch method. Excellent adsorption capacity (136.46 mg/g) was obtained under ambient conditions. As of now, very little work has been reported on water chestnut shells for the removal of dyes from wastewater. The present work shows an excellent adsorption capacity among all the previous work on water chestnut for dye remediation. Alkali activation significantly reduces hydrophilic/lignocellulosic components within the shells, which in turn makes the material more water stable and sustainable.

Alkali assisted hydrophobic reinforcement of coconut fiber for enhanced removal of cationic dyes: equilibrium, kinetics, and thermodynamic insight.

The present study illustrates enhanced removal of methylene blue (MB) and malachite green (MG) from water using alkali-activated coconut fiber (ACF) as adsorbent. Alkali activation effectively reduces the lignocellulosic components present within coco-fiber which in turn reinforces the coco-fiber to become more water-stable. The material was characterized by FTIR, SEM-EDS, BET, XRD, and pHZPC. BET surface area was found to be 10.901 m2 g-1, whereas pHZPC of the material is 6.05. FESEM images reveal rod-like morphology. Batch experiments were optimized with respect to contact time (0-120 min), temperature (288-308 K), pH (3-10), dose (1-5 g) and input dye concentration (10-50 mg L-1). The maximum adsorption coefficient was found to be 133.11 and 110.74 mg g-1 for MB and MG respectively. Adsorptions are best described by pseudo-second-order kinetics (kMB = 1.712, R2 = 0.999; kMG = 1.399, R2 = 0.999) and Langmuir isotherm model (R2 = 0.999). Thermodynamic data suggests a spontaneous (ΔG, -14 kJ mol-1) and feasible process. Spent material could be regenerated by using 0.5 M HCl. Up to 50% retention of activities was seen after five cycles. It can be concluded that alkali-activated coconut fiber is an economic and sustainable choice for dye removal. Novelty statement: Spent coconut was converted into an effective biosorbent by simple alkali activation under ambient conditions to increase the hydrophobicity of the fibers by reducing the lignocellulosic components. Two cationic dyes; methylene blue and malachite green have been efficiently removed with adsorption capacities of 133.11 and 110.74 mg g-1. The operation is simple, economically viable, and partially fulfills the principles of green engineering. Comparing with contemporary adsorbents, this material offers higher adsorption capacities with multi-cycle reusability and enhanced water stability.

A breakthrough column study for removal of malachite green using coco-peat.

A continuous adsorption study in a fixed bed column using coco-peat (CP) as an adsorbent was carried out for the removal of toxic malachite green (MG) from contaminated water. Fixed bed column studies were carried out to check field application viability. Various parameters like particle size, pH, concentration, dose and interference were exercised to optimize dye removal. Data obtained from breakthrough column studies were evaluated using Thomas and BDST model. Thomas rate constants Kt (0.22 ml min-1 mg-1) and adsorption capacity qo (181.04 mg g-1) were estimated and found to favor efficiency of CP. Thomas model was tested with several parameters like flow rate, concentration, and bed depth. Upon increase in input dye concentration, flow rate and bed height, adsorption coefficients increased. According to BDST model, maximum dye uptake of 468.26 mg/l was obtained with an input dye concentration of 5 mg/l. HYBRID and MPSD error functions were tested and found that Thomas model fits best. Dilute hydrochloric acid was found best for desorption. Real wastewater from textile industry was analyzed and confirmed the prospect of large-scale industrial application. In conclusion, coco-peat can be used as a promising bio-sorbent in column bed for scavenging of MG from contaminated water.

Modelling and Evolution of Conducting Filament in TiOx Memristor Using Conducting Atomic Force Microscopy.

Conducting filament evolution in TiOx based resistive switching memory fabricated by simple oxidation of Ti film is investigated. Formation of titanium oxide is confirmed from the X-ray diffraction study. Forming is required to initiate the switching process. A bipolar analog switching is observed with a positive set and negative reset voltage. The switching properties in TiOx layer owing to the formation of conducting filament is confirmed from the conducting atomic force micrograph at different bias voltage. A significant change in surface topography as a filament formation during set and reset is presented. Conduction mechanism inside the device at various voltage and effect of tunnel width on current is studied. The effective tunnel width of conduction filament and related parameters for device using device modelling (Threshold Adaptive Memristor model) is studied. The device can be used for synaptic applications.

Dewaxed Honeycomb as an Economic and Sustainable Scavenger for Malachite Green from Water.

Dewaxed honeycomb powder (HCP) was used as a promising adsorbent for removal of malachite green (MG) from aqueous solution. Raw honeycomb was strategically dewaxed by petroleum ether, and the purified product was characterized by Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), pHzpc, and proximate analysis. A high uptake capacity (123 mg/g) was found at neutral pH. Experimental data follow pseudo-second-order kinetics (k 2 as 0.45 × 10-2 g/min/mg, R 2 = 0.986) and Langmuir isotherm with R 2 0.999. Thermodynamic parameters suggested a spontaneous (ΔG = -26.28 kJ/mol) and exothermic (ΔH = -11.61 kJ/mol) process, which suggests increased randomness (ΔS = 0.0486 kJ/mol) at the solid-liquid interface during the adsorption process. The material can be regenerated by ordinary salt solution (1 M NaCl) and efficiently reused for three cycles with a minimal loss in efficiency. Adsorption mechanism is proposed to be a combination of electrostatic interaction and π-π stacking between aromatic units of HCP and MG. Abundant availability, possibility of wax commercialization, economic sustainability, and comprehensive waste management make HCP an ideal choice for dye decolorization.

Comparison of various texture classification methods using multiresolution analysis and linear regression modelling.

Textures play an important role in image classification. This paper proposes a high performance texture classification method using a combination of multiresolution analysis tool and linear regression modelling by channel elimination. The correlation between different frequency regions has been validated as a sort of effective texture characteristic. This method is motivated by the observation that there exists a distinctive correlation between the image samples belonging to the same kind of texture, at different frequency regions obtained by a wavelet transform. Experimentally, it is observed that this correlation differs across textures. The linear regression modelling is employed to analyze this correlation and extract texture features that characterize the samples. Our method considers not only the frequency regions but also the correlation between these regions. This paper primarily focuses on applying the Dual Tree Complex Wavelet Packet Transform and the Linear Regression model for classification of the obtained texture features. Additionally the paper also presents a comparative assessment of the classification results obtained from the above method with two more types of wavelet transform methods namely the Discrete Wavelet Transform and the Discrete Wavelet Packet Transform.