2,4-Dichlorophenoxyacetic acid (2,4-D) adsorptive removal by algal magnetic activated carbon nanocomposite.

In the present study, ferric oxide nanoparticles impregnated with activated carbon from Ulva prolifera biomass (UPAC-Fe2O3) were prepared and employed to remove 2,4-Dichlorophenoxyacetic acid (2,4-D) by adsorption. The UPAC-Fe2O3 nanocomposite was characterized for its structural and functional properties by a variety of techniques. The nanocomposite had a jagged, irregular surface with pores due to uneven scattering of Fe2O3 nanoparticles, whereas elemental analysis portrayed the incidence of carbon, oxygen, and iron. XRD analysis established the crystalline and amorphous planes corresponding to the iron oxide and carbon phase respectively. FT-IR analyzed the functional groups that confirmed the integration of Fe2O3 nanoparticles onto nanocomposite surfaces. VSM and XPS studies uncovered the superparamagnetic nature and presence of carbon and Fe2O3, respectively, in the UPAC-Fe2O3 nanocomposite. While the surface area was 292.51 m2/g, the size and volume of the pores were at 2.61 nm and 0.1906 cm3/g, respectively, indicating the mesoporous nature and suitability of the nanocomposites that could be used as adsorbents. Adsorptive removal of 2,4-D by nanocomposite for variations in process parameters like pH, dosage, agitation speed, adsorption time, and 2,4-D concentration was studied. The adsorption of 2,4-D by UPAC-Fe2O3 nanocomposite was monolayer chemisorption owing to Langmuir isotherm behavior along with a pseudo-second-order kinetic model. The maximum adsorption capacity and second order rate constant values were 60.61 mg/g and 0.0405 g/mg min respectively. Thermodynamic analysis revealed the spontaneous and feasible endothermic adsorption process. These findings confirm the suitability of the synthesized UPAC-Fe2O3 nanocomposite to be used as an adsorbent for toxic herbicide waste streams.

As (III) removal using superparamagnetic magnetite nanoparticles synthesized using Ulva prolifera - optimization, isotherm, kinetic and equilibrium studies.

In this study, magnetite nanoparticles (MNPs) were synthesized using the seaweed - Ulva prolifera, an amply found marine source in the Western coastal regions of India. The surface and other properties of MNPs were characterized by many sophisticated methods. Spherical nanoclusters were observed in the FESEM image and iron and oxygen elements were seen in EDS results. XRD peaks were consistent with magnetite standards and MNPs had good crystallinity. FTIR portrayed the specific signals for MNPs and TGA profile ascertained the thermal stability. Magnetic saturation of 41.84 emu/g with negligible hysteresis loop substantiated the superparamagnetism. XPS pointed out the presence of Fe and O with oxidation states specific for MNPs, and the results were consistent with EDS. BET revealed a high specific surface area (144.98 m2/g) of MNPs with mesopores. The synthesized MNPs were used as nanoadsorbent for the removal of As (III) from aqueous solution. The central composite design was used for optimizing As (III) adsorption on MNPs. The optimum conditions were found out as 97.5% at pH: 9, rotation speed: 150 rpm, time: 90 min, and MNPs dosage: 1.15 g/L. The adsorption process fitted in a better way with the Langmuir isotherm and pseudo-second-order model. The highest adsorption capacity was 12.45 mg/g, which is substantially larger than the documenter reports. The spontaneous and endothermic nature of adsorption were ascertained from thermodynamic studies. The results suggested that the synthesized MNPs using the extract of U. prolifera could be alternative nanoadsorbents for eliminating toxic heavy metals from waste streams.

Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass.

This study focused on the sustainable removal of chromium in its hexavalent form by adsorption using sugar-extracted spent marine macroalgal biomass - Ulva prolifera. The adsorption of Cr (VI) from aqueous solutions utilizing macroalgal biomass was studied under varying conditions of pH, adsorbent amount, agitation speed, and time to assess and optimize the process variables by using a statistical method - response surface methodology (RSM) to enhance the adsorption efficiency. The maximum adsorption efficiency of 99.11 ± 0.23% was obtained using U. prolifera under the optimal conditions: pH: 5.4, adsorbent dosage: 200 mg, agitation speed: 160 rpm, and time: 75 min. Also, a prediction tool - artificial neural network (ANN) model was developed using the RSM experimental data. Eight neurons in the hidden layer yielded the best network topology (4-8-1) with a high correlation coefficient (RANN: 0.99219) and low mean squared error (MSEANN: 0.99219). Various performance parameters were compared between RSM and ANN models, which confirmed that the ANN model was better in predicting the response with a high coefficient of determination value (R2ANN: 0.9844, R2RSM: 0.9721) and low MSE value (MSEANN: 3.7002, MSERSM: 6.2179). The adsorption data were analyzed by fitting to various equilibrium isotherms. The maximum adsorption capacity was estimated as 6.41 mg/g. Adsorption data was in line with Freundlich isotherm (R2 = 0.97) that confirmed the multilayer adsorption process. Therefore, the spent U. prolifera biomass can credibly be applied as a low-cost adsorbent for Cr (VI) removal, and the adsorption process can be modelled and predicted efficiently using ANN.

Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach.

Third generation biomass (marine macroalgae) has been projected as a promising alternative energy resource for bioethanol production due to its high carbon and no lignin composition. However, the major challenge in the technologies of production lies in the fermentative bioconversion process. Therefore, in the present study the predictive ability of an integrated artificial neural network with genetic algorithm (ANN-GA) in the modelling of bioethanol production was investigated for an indigenous marine macroalgal biomass (Ulva prolifera) by a novel yeast strain, Saccharomyces cerevisiae NFCCI1248 using six fermentative parameters, viz., substrate concentration, fermentation time, inoculum size, temperature, agitation speed and pH. The experimental model was developed using one-variable-at-a-time (OVAT) method to analyze the effects of the fermentative parameters on bioethanol production and the obtained regression equation was used as a fitness function for the ANN-GA modelling. The ANN-GA model predicted a maximum bioethanol production at 30 g/L substrate, 48 h fermentation time, 10% (v/v) inoculum, 30 °C temperature, 50 rpm agitation speed and pH 6. The maximum experimental bioethanol yield obtained after applying ANN-GA was 0.242 ± 0.002 g/g RS, which was in close proximity with the predicted value (0.239 g/g RS). Hence, the developed ANN-GA model can be applied as an efficient approach for predicting the fermentative bioethanol production from macroalgal biomass.

Evaluation of seasonal variation and the optimization of reducing sugar extraction from Ulva prolifera biomass using thermochemical method.

Green macroalgae comprise significant amount of structural carbohydrates for their conversion to liquid biofuels. However, it generally relies on species characteristics and the variability in seasonal profile to determine its route for bioprocessing. Hence, this study was conducted to analyze the indigenous marine macroalgal strain (Ulva prolifera) with respect to periodic trend and reducing sugar extraction. Consequently, in our investigation, the monthly variation in sugar profile and bioethanol yield was assessed between the monsoon and post-monsoon seasons, of which relatively high reducing sugar and fermentative bioethanol yield of about 0.152 ± 0.009 g/gdw and 6.275 ± 0.161 g/L was obtained for the October-month isolate (MITM10). Thereafter, the biochemical profile of this collected biomass (MITM10) revealed carbohydrate 34.98 ± 3.30%, protein 12.45 ± 0.49%, and lipid 1.93 ± 0.07%, respectively, on dry weight basis. Of these, the total carbohydrate fraction yielded the maximum reducing sugar of 0.156 ± 0.005 g/gdw under optimal conditions (11.07% (w/v) dosage, 0.9 M H2SO4, 121°C for 50 min) for thermal-acid hydrolysis. Furthermore, the elimination of polysaccharides was confirmed using the characterization techniques scanning electron microscopy (SEM) and Fourier transform infrared (FT-IR) spectroscopy. Therefore, the present thermochemical treatment method provides a species-specific novel strategy to breakdown the macroalgal cell wall polysaccharides that enhances sugar extraction for its utilization as an efficient bioenergy resource.

Inclusion of non-target antigen in vaccination favors generation of OVA specific CD4 memory T cells.

Inducing long-lived memory T cells by sub-unit vaccines has been a challenge. Subunit vaccines containing single immunogenic target antigen from a given pathogen have been designed with the presumption of mimicking the condition associated with natural infection, but fail to induce quality memory responses. In this study, we have included non-target antigens with vaccine candidate, OVA, in the inoculum containing TLR ligands to suffice the minimal condition of pathogen to provoke immune response. We found that inclusion of immunogenic HEL (hen egg lysozyme) or poorly immunogenic MBP (Myelin Basic protein) non-target antigen enhances the OVA specific CD4 T cell responses. Interestingly, poorly immunogenic MBP was found to strongly favor the generation of OVA specific memory CD4 T cells. MBP not only improves magnitude of T cell response but also promotes the T cells to undergo higher cycles of division, one of the characteristic of central memory T cells. Inclusion of MBP with vaccine targets was also found to promote multiple cytokine producing CD4 T cells. We also found that challenge of host with non-target antigen MBP favors generation of central Memory T cells.