Photocatalytic oxidation of crosslinked chitosan-epichlorohydrin (CS-ECH) film was successfully achieved via an immobilized TiO2/CS-ECH photocatalyst system on a glass plate. Oxidation process of CS-ECH film was carried out by irradiating the system with a 45-W fluorescent lamp for 10h in ultra-pure water. The results indicate the formation of carbonyl functional groups and partial elimination of amine groups in the molecular structure of the oxidized CS-ECH film. This oxidized CS-ECH film has different optical properties, ionic conductivity, degree of transparency, swelling index and chemical stability than the fresh CS-ECH film. In the environmental applications, the TiO2/oxidized-CS-ECH photocatalyst system can have photodegradation and faster mineralization rate of phenol than both fresh TiO2/CS-ECH and TiO2/oxidized-CS photocatalyst systems. This simple photocatalyst system, therefore can be considered as an environmental friendly method to oxidize synthetic biopolymer and to improve the photocatalytic efficiency of TiO2 to treat wastewater.
Chitosan-ethylene glycol diglycidyl ether/TiO2 nanoparticles (CS-EGDE/TNP) composite was synthesized to be biosorbent for the removal of reactive orange 16 (RO16) dye from aqueous solution. The CS-EGDE/TNP composite was characterized via BET, XRD, FTIR, and SEM-EDX techniques. Response surface methodology (RSM) with Box-Behnken design (BBD) was applied to optimize the adsorption key parameters such as adsorbent dose (A: 0.02-0.08 g/L), RO16 dye concentration (B: 20-80 mg/L), solution pH (C: 4-10), temperature (D: 30-50 °C), and contact time (E: 30-90 min). The adsorption isotherm followed Freundlich model and pseudo-second order (PSO) kinetic model. The adsorption capacity of CS-EGDE/TNP for RO16 dye was 1407.4 mg/g at 40 °C. The adsorption mechanism of RO16 dye on the surface of CS-EGDE/TNP can be attributed to various interactions such as electrostatic attraction, n-π interaction, Yoshida H-bonding, and H-bonding. Results supported the potential use of CS-EGDE/TNP as effective adsorbent for the treatment of acid reactive dye.
Fabrication of an immobilized cross-linked chitosan-epichlorohydrine thin film (CLCETF) onto glass plate for adsorption of reactive orange 16 (RO16) dye was successfully studied using the direct casting technique. Adsorption experiments were performed as a function of contact time, initial dye concentration (25mg/L to 350mg/L), and pH (3-11). The adsorption isotherm followed the Langmuir model. The adsorption capacity of CLECTF for RO16 was 356.50mg/g at 27±2°C. The kinetics closely followed the pseudo-second-order model. Results supported the potential use of an immobilized CLECTF as effective adsorbent for the treatment of reactive dye without using filtration process.
A crosslinked chitosan-glyoxal/TiO2 nanocomposite (CCG/TNC) was synthesized by loading different ratios of TiO2 nanoparticles into polymeric matrix of crosslinked chitosan-glyoxal (CCG) to be a promising biosorbent for methyl orange (MO). Box-Behnken design (BBD) in response surface methodology (RSM) was applied to optimize various process parameters, viz., loading of TiO2 nanoparticles into CCG polymeric matrix (A: 0%-50%), adsorbent dose (B: 0.04-0.14 g/50 mL), solution pH (C: 4-10), and temperature (D: 30-50 °C). The highest MO removal efficiency of 75.9% was observed by simultaneous interactions between AB, AC, and BC. The optimum TiO2 loading, adsorbent dosage, solution pH, and temperature were (50% TiO2: 50% chitosan labeled as CCG/TNC-50), 0.09 g/50 mL, 4.0, and 40 °C. The adsorption of MO from aqueous solution by using CCG/TNC-50 in batch mode was evaluated. The kinetic results were well described by the pseudo-first order kinetic, and the equilibrium data were in agreement with Langmuir isotherm model with maximum adsorption capacity of 416.1 mg/g. The adsorption mechanism included electrostatic attractions, n-π stacking interactions, dipole-dipole hydrogen bonding interactions, and Yoshida H-bonding.
Chitosan (CS) was physically modified with fly ash (FA) powder and subjected to chemical cross-linking reaction with tripolyphosphate (TPP) to produce a cross-linked CS-TPP/FA composite as adsorbent for removal of reactive orange 120 (RR120) dye. Different ratios of FA such as 25% FA particles (CS-TPP/FA-25) and 50% FA particles (CS-TPP/FA-50) were loaded into the molecular structure of CS-TPP. Box-Behnken design (BBD) was applied to optimize the input variables that affected the synthesis of the adsorbent and the adsorption of RR120 dye. These variables included FA loading (A: 0-50%), adsorbent dose (B: 0.04-0.1 g), solution pH (C: 4-10), temperature (D: 30 °C-60 °C), and time (E: 30-90 min). Results revealed that the highest removal (88.8%) of RR120 dye was achieved by CS-TPP/FA-50 at adsorbent dosage of 0.07 g, solution of pH 4, temperature of 45 °C, and time of 60 min. The adsorption equilibrium was described by the Freundlich model, with 165.8 mg/g at 45 °C as the maximum adsorption capacity of CS-TPP/FA-50 for RR120 dye. This work introduces CS-TPP/FA-50 as an ideal composite adsorbent for removal of textile dyes from the aqueous environment.
In current research work, chitosan (Chi) was subjected to subsequent physical and chemical modifications by incorporating kaolin clay (KA) into its polymeric structure, and crosslinking process with a covalent cross-linker namely epichlorohydrin (ECH) respectively. The final product of crosslinked chitosan-epichlorohydrin/kaolin (Chi-ECH/KA) composite was successfully applied for color removal and chemical oxygen demand (COD) reduction of textile dye namely reactive blue 19 dye (RB19) from aqueous environment. The influence of pertinent parameters, i.e. A: Chi-ECH/KA dose (0.02-0.1 g), B: pH (4-10), and C: time (5-30 min) on the RB19 color removal and COD reduction were statistically optimized by using response surface methodology with Box-Behnken design (RSM-BBD). The experimental data of the adsorption kinetic and the adsorption isotherm demonstrated a better fitness to pseudo-second order model and Langmuir isotherm model respectively. Excellent absorption ability of 560.9 mg/g was recorded for Chi-ECH/KA composite. The calculated thermodynamic functions clarified that the RB19 adsorption process was endothermic and spontaneous in nature. The mechanism of RB19 adsorption onto the Chi-ECH/KA may include electrostatic interactions, hydrogen bonding, Yoshida H-bonding, and n-π interactions. This study introduces Chi-ECH/KA composite as an eco-friendly, potential and multi-function composite bio adsorbent for removal of textile dye and COD reduction from aqueous environment.
In this research, an attempt to develop zwitterion composite adsorbent is conducted by modifying chitosan (CHS) with a covalent cross-linker (epichlorohydrin, ECH) and an aluminosilicate mineral (zeolite, ZL). The zwitterion composite adsorbent of chitosan-epichlorohydrin/zeolite (CHS-ECH/ZL) is performed multifunctional tasks by removing two structurally different cationic (methylene blue dye, MB), and anionic (reactive red 120 dye, RR120) dyes from aqueous solutions. The surface property, crystallinity, morphology, functionality, and charge of the CHS-ECH/ZL are analyzed using BET, XRD, SEM, FTIR, and pHpzc, analyses, respectively. The influence of pertinent parameters namely CHS-ECH/ZL dosage (0.02-0.5 g), solution pH (4-10), temperature (303-323K), initial dye concentration (30-400 mg/L), and contact time (0-600 min) on the MB and RR120 removal are tested. The research findings revealed that the adsorption isotherm at equilibrium well explained in according to the Freundlich isotherm model, and the recorded adsorption capacities of CHS-ECH/ZL are 156.1 and 284.2 mg/g for MB and RR120 respectively at 30 °C. The mechanism of MB and RR120 adsorption onto the CHS-ECH/ZL indicates various types of interactions namely, electrostatic interaction, hydrogen bonding, and Yoshida H-bonding in addition to n-π interaction. Overall, this research introduces CHS-ECH/ZL composite as an eco-friendly zwitterion adsorbent with good applicability towards the two structurally different cationic and anionic dyes from aqueous environment.
In this study, tunable Schiff's base-cross-linked chitosan-glutaraldehyde (CS-GLA) was modified and applied to remove reactive red 120 (RR120) dye from an aqueous solution. Different ratios of TiO2 nanoparticles, such as 25% TiO2 nanoparticles (CS-GLA/TNC-25) and 50% TiO2 nanoparticles (CS-GLA/TNC-50), were loaded into the CS-GLA's molecular structure. The adsorptive properties of CS-GLA, CS-GLA/TNC-25, and CS-GLA/TNC-50 for the RR120 dye in the aqueous solution were evaluated. CS-GLA/TNC-25 exhibited the best adsorptive property possibly because of the perfect balancing between the surface area and available amine (NH2) groups in the composite formulation. The impact of adsorption key parameters, such as adsorbent dosage (0.01-1.2 g), RR120 dye concentration (30-400 mg/L), solution pH (3-12), and contact time (0-400 min) were explored by batch adsorption mode. The adsorption was well described by the Freundlich model and pseudo-second order kinetic model. The adsorption capacity of CS-GLA/TNC-25 for RR120 dye was 103.1 mg/g at 303K. The adsorption mechanism of RR120 on the CS-GLA/TNC-25 surface can be assigned to various interactions, such as electrostatic attraction, n-π stacking, and H-bonding. Results indicate the potential application of CS-GLA/TNC-25 as environment-friendly biosorbent for removing acid and/or textile dyes, such as RR120, from aqueous environments.
In this work, chitosan (Chi) was cross-linked with glyoxal (Gly) and deposited onto glass plate to be a superior adsorbent film for two structurally different reactive orange 16 (RO-16) and methyl orange (MO) dyes by using non-conventional adsorption system without filtration process. The characterizations indicate that the cross-linked chitosan-glyoxal (Chi-Gly) film has a low swelling index, high adherence strength on glass plate, amine group (NH2) content was 32.52%, and pHpzc of ∼6.0 indicating a negative surface charge occurs above pHpzc. The adsorption isotherm data of RO-16 and MO by Chi-Gly film were in agreement with Langmuir isotherm, with maximum adsorption capacities of 1554.3 mg/g and 1451.9 mg/g, respectively. The pseudo-first-order kinetic model best described the kinetic data. The adsorption process was spontaneous and exothermic in nature at Chi-Gly film thickness of 8.55 μm, and pH ~3. The mechanism of adsorption included mainly electrostatic attractions, dipole-dipole hydrogen bonding interactions, n-π stacking attractions, and Yoshida H-bonding. This study reveals that immobilized Chi-Gly film as a good candidate for adsorption of reactive and acid dyes as it does not require any filtration process and adsorbent recovery during and post-adsorption process.
A magnetic biocomposite blend of chitosan-polyvinyl alcohol/fly ash (m-Cs-PVA/FA) was developed by adding fly ash (FA) microparticles into the polymeric matrix of magnetic chitosan-polyvinyl alcohol (m-Cs-PVA). The effectiveness of m-Cs-PVA/FA as an adsorbent to remove textile dye (reactive orange 16, RO16) from aquatic environment was evaluated. The optimum adsorption key parameters and their significant interactions were determined by Box-Behnken Design (BBD). The analysis of variance (ANOVA) indicates the significant interactions can be observed between m-Cs-PVA/FA dose with solution pH, and m-Cs-PVA/FA dose with working temperature. Considering these significant interactions, the highest removal of RO16 (%) was found 90.3% at m-Cs-PVA/FA dose (0.06 g), solution pH (4), working temperature (30 °C), and contact time (17.5 min). The results of adsorption kinetics revealed that the RO16 adsorption was better described by the pseudo-second-order model. The results of adsorption isotherm indicated a multilayer adsorption process as well described by Freundlich model with maximum adsorption capacity of 123.8 mg/g at 30 °C. An external magnetic field can be easily applied to recover the adsorbent (m-Cs-PVA/FA). The results supported that the synthesized m-Cs-PVA/FA presents itself as an effective and promising adsorbent for textile dye with preferable adsorption capacity and separation ability during and after the adsorption process.
In this study, the biomass of rubber seed pericarp was first treated with sulfuric acid and then its activated carbon was formed by the pyrolysis process. As produced acid-treated activated carbon of chosen biomass was then used for the adsorption of crystal violet (CV) and methylene green (MG) from the colored aqueous solution. The adsorbent was exposed to several characterization methods to know its structural and morphological behaviors before and after CV and MG adsorption. The adsorbent was found to be mesoporous having a surface area of 59.517 m2/g. The effect of pH, time, and concentration was assessed while various isotherm and kinetics models were employed to know the adsorption insight. The optimum conditions were at pH 8, within 30 min, 50 mg/L concentration, and 0.06 gm dose. The adsorption data (the maximum adsorption capacity for CV and MG were found to be 302.7 and 567.6 mg/g, respectively) was validated by fitting in a response surface statistical methodology and the positive interactions between the studied factors were found. The adsorption was mainly belonging to the electrostatic attraction of the dye molecules. The study proves that the used adsorbent is economical and an excellent source of treating wastewater.
In this study, pineapple crown (PC) feedstock residues were utilized as a potential precursor toward producing activated carbon (PCAC) via pyrolysis induced with ZnCl2 activation. The PCAC has a surface area (457.8 m2/g) and a mesoporous structure with an average pore diameter of 3.35 nm, according to the Brunauer-Emmett-Teller estimate. The removal of cationic dye (Fuchsin basic; FB) was used for investigating the adsorption parameters of PCAC. The optimization of significant adsorption variables (A: PCAC dose (0.02-0.1 g/100 mL); B: pH (4-10); C: time (10-90); and D: initial FB concentration (10-50 mg/L) was conducted using the Box-Behnken design (BBD). The pseudo-second-order (PSO) model characterized the dye adsorption kinetic profile, whereas the Freundlich model reflected the equilibrium adsorption profile. The maximum adsorption capacity (qmax) of PCAC for FB dye was determined to be 171.5 mg/g. Numerous factors contribute to the FB dye adsorption mechanism onto the surface of PCAC, which include electrostatic attraction, H-bonding, pore diffusion, and π-π stacking. This study illustrates the utilization of PC biomass feedstock for the fabrication of PCAC and its successful application in wastewater remediation.
In this investigation, microwave irradiation assisted by ZnCl2 was used to transform pineapple crown (PN) waste into mesoporous activated carbon (PNAC). Complementary techniques were employed to examine the physicochemical characteristics of PNAC, including BET, FTIR, SEM-EDX, XRD, and pH at the point-of-zero-charge (pHpzc). PNAC is mesoporous adsorbent with a surface area of 1070 m2/g. The statistical optimization for the adsorption process of two model cationic dyes (methylene blue: MB and, crystal violet: CV) was conducted using the response surface methodology-Box-Behnken design (RSM-BBD). The parameters include solution pH (4-10), contact time (2-12) min, and PNAC dosage (0.02-0.1 g/100 mL). The Freundlich and Langmuir models adequately described the dye adsorption isotherm results for the MB and CV systems, whereas the pseudo-second order kinetic model accounted for the time dependent adsorption results. The maximum adsorption capacity (qmax) for PNAC with the two tested dyes are listed: 263.9 mg/g for CV and 274.8 mg/g for MB. The unique adsorption mechanism of MB and CV dyes by PNAC implicates multiple contributions to the adsorption process such as pore filling, electrostatic forces, H-bonding, and π-π interactions. This study illustrates the possibility of transforming PN into activated carbon (PNAC) with the potential to remove two cationic dyes from aqueous media.
Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9-5 parameters without losing their learned information over the models' training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost-SVM, XGBoost-ANN, XGBoost-Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost-Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.
In this study, activated carbon (AC) was prepared from agro-waste betel nut husks (BNH) through the chemical activation method. Different characterization techniques described the physicochemical nature of betel nut husks activated carbon (BNH-AC) through Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), scanning electron microscopy (SEM), and pH point of zero charge. Later, the produced AC was used for methylene blue (MB) adsorption via numerous batch experimental parameters: initial concentrations of MB dye (25-250 mg/L), contact time (0.5-24 hours) and initial pH (2-12). Dye adsorption isotherms were also assessed at three temperatures where the maximum adsorption capacity (381.6 mg/g) was found at 30 °C. The adsorption equilibrium data were best suited to the non-linear form of the Freundlich isotherm model. Additionally, non-linear pseudo-second-order kinetic model was better fitted with the experimental value as well. Steady motion of solute particles from the boundary layer to the BNH-AC's surface was the possible reaction dynamics concerning MB adsorption. Thermodynamic study revealed that the adsorption process was spontaneous and exothermic in nature. Saline water emerged as an efficient eluent for the desorption of adsorbed dye on AC. Therefore, the BNH-AC is a very promising and cost-effective adsorbent for MB dye treatment and has high adsorption capacity.
Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R2) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.
In this study, a new magnetic Schiff's base-chitosan-glyoxal/fly ash/Fe3O4 biocomposite (Chi-Gly/FA/Fe3O4) was successfully synthesized by direct compositing of magnetic chitosan (Chi) with fly ash (FA) powder particles, and followed by Schiff's base formation via cross-linking reaction with glyoxal (Gly). Various techniques such as BET, XRD, FTIR, and SEM-EDX were utilized to characterize of Chi-Gly/FA/Fe3O4 biocomposite. The effectiveness of Chi-Gly/FA/Fe3O4 as an adsorbent was evaluated for the removal anionic azo dye such as reactive orange 16 (RO16) from aqueous environment. The effect of adsorption process parameters namely adsorbent dose (A: 0.02-0.1 g), solution pH (B: 4-10), temperature (C: 30-50 °C), and contact time (D: 5-20 min) were optimized via Box-Behnken design (BBD) in response surface methodology (RSM). The adsorption process followed the pseudo-second order (PSO) kinetic, and Freundlich isotherm models. The maximum adsorption capacity of Chi-Gly/FA/Fe3O4 biocomposite for RO16 dye was recorded to be 112.5 mg/g at 40 °C. The RO16 dye adsorption mechanism was attributed to various interactions such as electrostatic, n-π, H-bonding, and Yoshida H-bonding. Furthermore, the Chi-Gly/FA/Fe3O4 biocomposite exhibited a high ability to separate from the aqueous solution after adsorption process by external magnetic field.
The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.
Phosphorylated chitosan (P-CS) was successfully synthesized using a facile experimental setup of hydrothermal method that was applied to the adsorption of anionic Acid Red 88 (AR88) from aqueous media. The adsorption process obeyed the pseudo-second-order (PSO) kinetic model. In contrast, the adsorption isotherm conformed to the Langmuir model, with the maximum adsorption capacity (qm = 230 mg g-1) at 303 K. Both external and intraparticle diffusion strongly influenced the rate of adsorption. The insights from this study reveal that P-CS could be easily prepared and regenerated for reusability applications. The adsorption mechanism and intermolecular interaction between P-CS and AR 88 were investigated using Fourier transform infrared (FTIR) spectroscopy and calculations via Density Functional Theory (DFT). The key modes of adsorption for the P-CS/AR 88 system are driven by electrostatic attractions, H-bonding, and n-π interactions. The findings herein reveal that P-CS is a promising adsorbent for the removal of anionic dyes such as AR88 or similar pollutants from water.