Anaerobic Biotechnology for the Treatment of Pharmaceutical Compounds and Hospital Wastewaters

Khadir, Ali; Mollahosseini, Afsaneh; Negarestani, Mehrdad; Mardy, Ali

doi:10.1007/978-3-030-48985-4_3

Cited by 12 publications

(3 citation statements)

References 87 publications

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“…anthraquinone, and azo. Textile dyes are toxic, resistant, bioaccumulative, non-biodegradable, and carcinogenic and cause damaging consequences on the surroundings, even at low concentrations [7][8][9][10][11][12] . Classifying dyes based on the remaining particle charge after dissolution in an aqueous solution is standard.…”

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confidence: 99%

Preparation of sisal fiber/polyaniline/bio-surfactant rhamnolipid-layered double hydroxide nanocomposite for water decolorization: kinetic, equilibrium, and thermodynamic studies

Negarestani,

Tavassoli,

Reisi

et al. 2023

Sci Rep

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Sisal fiber is a potent economical biomaterial for designing composites because of its low density, high specific strength, no toxic effects, and renewability. The present study utilized sisal fiber as a starting material and subjected it to modification to produce a sisal fiber/polyaniline/bio-surfactant rhamnolipid-layered double hydroxide nanocomposite material denoted as SF@PANI@LDH@RL. The composite was evaluated for its efficacy in removing reactive orange 16 (RO16) and methylene blue (MB) from aqueous solutions. The synthesized adsorbent was characterized by FTIR, XRD, and SEM–EDS techniques; these analyses indicated the successful modification of the sisal fiber. The primary factors, including contact time, adsorbent dosage, dye concentration, temperature, and pH, were optimized for achieving the most excellent adsorption efficiency. On the one hand, methylene blue removal is enhanced in the basic solution (pH = 10). On the other hand, reactive orange 16 adsorption was favored in the acidic solution (pH = 3). The highest adsorption capacities for methylene blue and reactive orange 16 were 24.813 and 23.981 mg/g at 318 K, respectively. The Temkin isotherm model, which proves the adsorption procedure of methylene blue and reactive orange 16 could be regarded as a chemisorption procedure, supplies the most suitable explanation for the adsorption of methylene blue (R2 = 0.983) and reactive orange 16 (R2 = 0.996). Furthermore, Elovich is the best-fitting kinetic model for both dyes (R2 = 0.986 for MB and R2 = 0.987 for RO16). The recommended SF@PANI@LDH@RL adsorbent was reused six consecutive times and showed stable adsorption performance. The results demonstrate that SF@PANI@LDH@RL is a perfect adsorbent for eliminating cationic and anionic organic dyes from aqueous media.

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confidence: 99%

Preparation of sisal fiber/polyaniline/bio-surfactant rhamnolipid-layered double hydroxide nanocomposite for water decolorization: kinetic, equilibrium, and thermodynamic studies

Negarestani,

Tavassoli,

Reisi

et al. 2023

Sci Rep

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“…It looks that a high RO16 concentration could significantly deteriorate the adsorption efficiency. Previous studies also declared that for a wide range of pollutants, such as dye 44 , pharmaceuticals 45 or heavy metals 46 , an increase in pollutant concentration resulted in a reduction in adsorption/removal efficiency. Such behavior could be ascribed to the fact that the number of vacant sites on lignocellulosic sawdust-Fe/Zn bio-nanocomposite is fixed during the experiments; however, the number of RO16 molecules are increasing.…”

Section: Resultsmentioning

confidence: 99%

Low-cost treated lignocellulosic biomass waste supported with FeCl3/Zn(NO3)2 for water decolorization

Kheradmand

Negarestani

Mollahosseini

et al. 2022

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Dye pollution has always been a serious concern globally, threatening the lives of humans and the ecosystem. In the current study, treated lignocellulosic biomass waste supported with FeCl3/Zn(NO3)2 was utilized as an effective composite for removing Reactive Orange 16 (RO16). SEM/EDAX, FTIR, and XRD analyses exhibited that the prepared material was successfully synthesized. The removal efficiency of 99.1% was found at an equilibrium time of 110 min and dye concentration of 5 mg L−1 Adsorbent mass of 30 mg resulted in the maximum dye elimination, and the efficiency of the process decreased by increasing the temperature from 25 to 40 °C. The effect of pH revealed that optimum pH was occurred at acidic media, having the maximum dye removal of greater than 90%. The kinetic and isotherm models revealed that RO16 elimination followed pseudo-second-order (R2 = 0.9982) and Freundlich (R2 = 0.9758) assumptions. Surprisingly, the performance of modified sawdust was 15.5 times better than the raw sawdust for the dye removal. In conclusion, lignocellulosic sawdust-Fe/Zn composite is promising for dye removal.

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“…Recently, many studies have identified antibiotics using conventional methods such as high‐performance liquid chromatography, liquid chromatography‐UV detection, liquid chromatography‐mass spectrometry, gas chromatography‐mass spectrometry, immunochromatography, and combinations of these methods in tandem. [ 2–7 ] However, these methods are highly equipment‐ and technician‐dependent, complex, and time‐consuming. Furthermore, the determination of antibiotics in food remains challenging because of the high complexity of the matrix and low concentrations of the target analytes in real‐world samples.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning‐Driven 3D Plasmonic Cavity‐in‐Cavity Surface‐Enhanced Raman Scattering Platform with Triple Synergistic Enhancement Toward Label‐Free Detection of Antibiotics in Milk

Fang

Lin

Liang

et al. 2022

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The surface‐enhanced Raman scattering (SERS) technique with ultrahigh sensitivity has gained attention to meet the increasing demands for food safety analysis. The integration of machine learning and SERS facilitates the practical applicability of sensing devices. In this study, a machine learning‐driven 3D plasmonic cavity‐in‐cavity (CIC) SERS platform is proposed for sensitive and quantitative detection of antibiotics. The platform is prepared by transferring truncated concave nanocubes (NCs) to an obconical‐shaped template surface. Owing to the triple synergistic enhancement effect, the highly ordered 3D CIC arrays improve the simulated electromagnetic field intensity and experimental SERS activity, demonstrating a 33.1‐fold enhancement compared to a typical system consisting of Au NCs deposited on a flat substrate. The integration of machine learning and Raman spectroscopy eliminates subjective judgments on the concentration of detectors using a single feature peak and achieves accurate identification. The machine learning‐driven CIC SERS platform is capable of detecting ampicillin traces in milk with a detection limit of 0.1 ppm, facilitating quantitative analysis of different concentrations of ampicillin. Therefore, the proposed platform has potential applications in food safety monitoring, health care, and environmental sampling.

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Anaerobic Biotechnology for the Treatment of Pharmaceutical Compounds and Hospital Wastewaters

Cited by 12 publications

References 87 publications

Preparation of sisal fiber/polyaniline/bio-surfactant rhamnolipid-layered double hydroxide nanocomposite for water decolorization: kinetic, equilibrium, and thermodynamic studies

Preparation of sisal fiber/polyaniline/bio-surfactant rhamnolipid-layered double hydroxide nanocomposite for water decolorization: kinetic, equilibrium, and thermodynamic studies

Low-cost treated lignocellulosic biomass waste supported with FeCl3/Zn(NO3)2 for water decolorization

Machine Learning‐Driven 3D Plasmonic Cavity‐in‐Cavity Surface‐Enhanced Raman Scattering Platform with Triple Synergistic Enhancement Toward Label‐Free Detection of Antibiotics in Milk

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