Agglomeration and restacking can reduce graphene oxide (GO) activity in a wide range of applications. Herein, GO was synthesized by a modified Hummer's method. To minimize restacking and agglomeration, in situ chemical oxidation polymerization was carried out to embed polyaniline (PANI) chains at the edges of GO sheets, to obtain GO-PANI nanocomposite. The GO-PANI was tested for the adsorptive removal of brilliant green (BG) from an aqueous solution through batch mode studies. Infrared (FT-IR) analysis revealed the dominance of hydroxyl and carboxylic functionalities over the GO-PANI surface. Solution pH-dependent BG uptake was observed, with maximum adsorption at pH 7, and attaining equilibrium in 30 min. The adsorption of BG onto GO-PANI was fit to the Langmuir isotherm, and pseudo-second-order kinetic models, with a maximum monolayer adsorption capacity (qm) of 142.8 mg/g. An endothermic adsorption process was observed. Mechanistically, π-π stacking interaction and electrostatic interaction played a critical role during BG adsorption on GO-PANI.
Lower dye concentrations and the presence of several dyes along with other matrices in environmental samples restrict their determination. Herein, a highly sensitive and rapid ultra-performance tandem mass spectrometric method was developed for simultaneous determination of cationic dyes, namely methylene blue (MB), rhodamine B (RB) and crystal violet (CV), in environmental samples. To preconcentrate environmental samples, solid-phase extraction cartridges were developed by using hydrogen peroxide modified pistachio shell biomass (MPSB). The surface morphological and chemical functionalities of MPSB were well characterized. The developed method was validated considering different validation parameters. In terms of accuracy and precision, the %RSD for all three dyes at all four concentration points was found to be between 1.26 and 2.76, while the accuracy reported in terms of the recovery was found to be 98.02%-101.70%. The recovery was found to be in the range of 98.11% to 99.55%. The real sample analysis shows that MB, RB, and CV were found in the ranges of 0.39-5.56, 0.32-1.92 and 0.27-4.36 μg/mL, respectively.
The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.
Four strains of bioflocculant-producing bacteria were isolated from a palm oil mill effluent (POME). The four bacterial strains were identified as Pseudomonas alcaliphila (B1), Pseudomonas oleovorans (B2), Pseudomonas chengduensis (B3), and Bacillus nitratireducens (B4) by molecular identification. Among the four bacterial strains, Bacillus nitratireducens (B4) achieved the highest flocculating activity (49.15%) towards kaolin clay suspension after eight hours of cultivation time and was selected for further studies. The optimum conditions for Eriochrome Black T (EBT) flocculation regarding initial pH, type of cation, and B4 dosage were determined to be pH 2, Ca2⁺ cations, and a dosage of 250 mL/L of nutrient broth containing B4. Under these conditions, above 90% of EBT dye removal was attained. Fourier transform infrared spectroscopic (FT-IR) analysis of the bioflocculant revealed the presence of hydroxyl, alkyl, carboxyl, and amino groups. This bioflocculant was demonstrated to possess a good flocculating activity, being a promissory, low-cost, harmless, and environmentally friendly alternative for the treatment of effluents contaminated with dyes.
A noticeable interest and steady rise in research studies reporting the design and assessment of smart adsorbents for sequestering aqueous metal ions and xenobiotics has occurred in the last decade. This motivates compiling and reviewing the characteristics, potentials, and performances of this new adsorbent generation's metal ion and xenobiotics sequestration. Herein, stimuli-responsive adsorbents that respond to its media (as internal triggers; e.g., pH and temperature) or external triggers (e.g., magnetic field and light) are highlighted. Readers are then introduced to selective adsorbents that selectively capture materials of interest. This is followed by a discussion of self-healing and self-cleaning adsorbents. Finally, the review ends with research gaps in material designs.
Polypyrrole (PPy) and polypyrrole/cerium oxide nanocomposite (PPy/CeO2) were prepared by the chemical oxidative method in an aqueous medium using anhydrous ferric chloride (FeCl3) as an oxidant. The successful formulation of materials was confirmed by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and transmittance electron microscopy (TEM). A four-in-line probe device was used for studying DC electrical conductivity and ammonia vapor sensing properties of PPy and PPy/CeO2. The significant improvement in both the conductivity and sensing parameters of PPy/CeO2 compared to pristine PPy reveals some synergistic/electronic interaction between PPy and cerium oxide nanoparticles (CeO2 NPs) working at molecular levels. The initial conductivity (i.e., conductivity at room temperature) was found to be 0.152 Scm-1 and 1.295 Scm-1 for PPy and PPy/CeO2, respectively. Also, PPy/CeO2 showed much better conductivity retention than pristine PPy under both the isothermal and cyclic ageing conditions. Ammonia vapor sensing was carried out at different concentration (0.01, 0.03, 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5 vol %). The sensing response of PPy/CeO2 varied with varying concentrations. At 0.5 vol % ammonia concentration, the % sensing response of PPy and PPy/CeO2 sensor was found to be 39.1% and 93.4%, respectively. The sensing efficiency of the PPy/CeO2 sensor was also evaluated at 0.4. 0.3, 0.2, 0.1, 0.05, 0.03, and 0.01 vol % ammonia concentration in terms of % sensing response, response/recovery time, reversibility, selectivity as well as stability at room temperature.