Enrichment of the surface functional groups of activated carbon by modification method

Demiral, İlknur; Şamdan, Canan; Demiral, Hakan

doi:10.1016/j.surfin.2020.100873

Cited by 91 publications

(52 citation statements)

References 60 publications

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“…This result was as same as previous study on carbon materials. 34 However, some variations occurred in FeGAC after the modification process. The FTIR spectrum of the FeGAC exhibited a small absorption peak at 563 cm −1 , corresponding to stretching vibration of Fe–O group, and this result was in compliance with the XRD result.…”

Section: Resultsmentioning

confidence: 99%

Effects of granular activated carbon and Fe-modified granular activated carbon on anammox process start-up

Zang

et al. 2021

RSC Adv.

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Section: Resultsmentioning

confidence: 99%

Effects of granular activated carbon and Fe-modified granular activated carbon on anammox process start-up

Zang

et al. 2021

RSC Adv.

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“…The surface chemistry of activated carbons can be modified, as previously stated, through various post-synthesis treatments [ 104 , 105 ], which do not only involve the acidic-basic character of the surfaces, but also the nature of the surface heteroatoms. As mentioned, the existence of certain oxygenated groups functionalities confers acidic properties, hence why the acidic character of ACs is frequently developed through heat treatment in the gas phase with oxygen, ozone, carbon dioxide, steam, and nitrous oxide (150–700 °C) [ 74 , 90 , 92 ], or liquid phase with oxidants such as nitric acid, hypochlorite, permanganate, or hydrogen peroxide [ 106 , 107 , 108 ].…”

Section: Further Properties (Different From Porosity) Of the Acs Prep...mentioning

confidence: 99%

Chemical Activation of Lignocellulosic Precursors and Residues: What Else to Consider?

2022

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This paper provides the basis for understanding the preparation and properties of an old, but advanced material: activated carbon. The activated carbons discussed herein are obtained from “green” precursors: biomass residues. Accordingly, the present study starts analyzing the components of biomass residues, such as cellulose, hemicellulose, and lignin, and the features that make them suitable raw materials for preparing activated carbons. The physicochemical transformations of these components during their heat treatment that lead to the development of a carbonized material, a biochar, are also considered. The influence of the chemical activation experimental conditions on the yield and porosity development of the final activated carbons are revised as well, and compared with those for physical activation, highlighting the physicochemical interactions between the activating agents and the lignocellulosic components. This review incorporates a comprehensive discussion about the surface chemistry that can be developed as a result of chemical activation and compiles some results related to the mechanical properties and conformation of activated carbons, scarcely analyzed in most published papers. Finally, economic, and environmental issues involved in the large-scale preparation of activated carbons by chemical activation of lignocellulosic precursors are commented on as well.

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“…The presence of Iron (Fe) and Manganese (Mn) results in staining as well as offensive tastes and appearances, anesthetic and operational concern. In moderate doses is considered as an essential nutrient for human health (Demiral et al 2021). Other heavy metals that carry problematic effects include zinc (Zn).…”

Section: Introductionmentioning

confidence: 99%

Application of neural network techniques to predict the heavy metals in acid mine drainage from South African mines

Kabuba

Maliehe

2021

Water Science and Technology

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Acid Mine Drainage (AMD) is the formation and movement of highly acid water rich in heavy metals. Prediction of heavy metals in the AMD is important in developing any appropriate remediation strategy. This paper attempts to predict heavy metals in the AMD (Zn, Fe, Mn, Si and Ni) from South African mines using Neural Network (NN) techniques. The Backpropagation (BP) neural network model has three layers with the input layer (pH, SO42− and total dissolved solids (TDS)) and output layer (Cu, Fe, Mn and Zn). After BP training, the NN techniques were able to predict heavy metals in AMD with a tangent sigmoid transfer function (tansig) at hidden layer with five neurons and linear transfer function (purelin) at output layer. The Levenberg-Marquardt back-propagation (trainlm) algorithm was found as the best of 10 BP algorithms with mean-squared error (MSE) value of 0.00041 and coefficient of determination (R) for all (training, validation and test) value of 0.99984. The results indicate that NN can be considered as an easy and cost-effective technique to predict heavy metals in the AMD.

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Enrichment of the surface functional groups of activated carbon by modification method

Cited by 91 publications

References 60 publications

Effects of granular activated carbon and Fe-modified granular activated carbon on anammox process start-up

Effects of granular activated carbon and Fe-modified granular activated carbon on anammox process start-up

Chemical Activation of Lignocellulosic Precursors and Residues: What Else to Consider?

Application of neural network techniques to predict the heavy metals in acid mine drainage from South African mines

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