Biosorption of Multifold Toxic Heavy Metal Ions from Aqueous Water onto Food Residue Eggshell Membrane Functionalized with Ammonium Thioglycolate

Wang, Sha; Wei, Minghong; Huang, Yuming

doi:10.1021/jf4003939

Cited by 82 publications

(22 citation statements)

References 44 publications

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“…1 Hence, developing the effective, inexpensive and rapid heavy metal ion detections are highly urgent. Many methods have been developed and applied into detect the heavy metal ions including atomic absorption spectrometry, 2 inductively coupled plasma mass spectrometry, 3 and uorescence spectrometry, 4 et al However, these classical methods applied in the eld of heavy metal ion detection are limited due to their disadvantages of low sensitivity, high cost, and complicated operation. In contrast, the electrochemical methods, especially differential pulse anodic stripping voltammetry (DPASV), have been popularly used to detect the heavy metal ions because of their advantages of high sensitivity, simple operation, rapid analysis and low price.…”

Section: Introductionmentioning

confidence: 99%

Sensitive electrochemical sensor based on poly(l-glutamic acid)/graphene oxide composite material for simultaneous detection of heavy metal ions

Fei

et al. 2019

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Sensitive electrochemical sensor based on poly(l-glutamic acid)/graphene oxide composite material for simultaneous detection of heavy metal ions

Fei

et al. 2019

RSC Adv.

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“…Heavy metal pollution has placed great pressure on people since it is associated with their acute toxicity for environment and human health, generating neuromuscular paralysis, cellular carcinogenesis, and gene mutation . Therefore, it has become an increasing concern to minimize or eliminate heavy metal pollution, and various methods hitherto have been employed into its abatement including chemical precipitation, ion exchange, membrane filtration, electric resolving, and adsorption . Among these methods, adsorption is the most maneuverable, efficient, economical, and extensively used strategy for removing heavy metal ions from wastewater.…”

Section: Introductionmentioning

confidence: 99%

High Adsorption Pearl‐Necklace‐Like Composite Membrane Based on Metal–Organic Framework for Heavy Metal Ion Removal

Hou

Zhou

Jin

et al. 2018

Part & Part Syst Charact

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The zeolitic imidazolate framework‐67 (ZIF‐67)‐based “pearl‐necklace‐like” composite membranes are prepared by in situ intergrown on the surface of 2‐methylimidazole/cellulose acetate (MIM/CA) electrospun nanofibers for the first time. With the aid of MIM, the ZIF‐67 nanocrystals successfully grow throughout the composites and attach to the fibers just like the pearls in necklace. The incubation time of ZIF‐67 has a significant influence on the structures and properties of the composites. And with an approximate saturation of ZIF‐67 nanocrystals, the integrated composites achieve a much higher surface area of 463.1 m2 g−1, which is as dozens of times as that of pure MIM/CA electrospun nanofibers (6.9 m2 g−1). In addition, the composites performed a high Cu(II) and Cr(VI) adsorption of 18.9 mg g−1 and 14.5 mg g−1, respectively. The adsorption data are well fitted with the pseudo‐second‐order kinetics. Moreover, adsorption mechanism is also discussed, and the electrostatic adsorption and ions exchange contribute to the high adsorption of Cu(II) and Cr(VI), and the existence of Cr(III) indicates that the Cr(VI) ions are partially reduced to Cr(III) during the adsorption. Therefore, the fabricated metal organic framework‐composite membrane with special “pearl‐necklace‐like” is a promising environmental material for removing heavy metal ions from water.

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“…The amount of heavy metals released into the aqueous environment has been increasing as a result of anthropogenic activities such as mining, sludge disposal, and electroplating, with the effects of these metals on the ecosystem causing global concern (Shojaeimehr et al, 2014;Svecova et al, 2006;Wang et al, 2013). Adsorption offers an alternative to the remediation of industrial and municipal wastewater effluent as conventional technologies such as ion exchange, reverse osmosis, filtration, electrochemical treatment, and membrane technologies are expensive and generate large amounts of sludge waste (O'Connell et al, 2008b).…”

Section: Introductionmentioning

confidence: 99%

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

Hamid

Jenidi

Thielemans

et al. 2016

Industrial Crops and Products

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This study observed the influence of temperature, initial Cu(II) ion concentration, and sorbent dosage on the Cu(II) removal from the water matrix using surface-oxidized cellulose nanowhiskers (CNWs) bearing carboxylate functionalities. In addition, this study focused on the actual conditions in a wastewater treatment plant. Conductometric titration of CNWs suspensions showed a surface charge of 54 and 410 mmol/kg for the unmodified and modified CNWs, respectively, which indicated that the modified CNWs provide a relatively high surface area per unit mass than the unmodified CNWs. In addition, the stability of the modified CNWs was tested under different conditions and proved that the functional groups were permanent and not degraded. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in order to optimize the system and to create a predictive model to evaluate the Cu(II) removal performance of the modified CNWs. The performance of the ANN and RSM models were statistically evaluated in terms of the coefficient of determination (R 2 ), 2 absolute average deviation (AAD), and the root mean squared error (RMSE) on predicted experiment outcomes. Moreover, to confirm the model suitability, unseen experiments were conducted for 14 new trials not belonging to the training data set and located both inside and outside of the training set boundaries. Result showed that the ANN model (R 2 =0.9925, AAD = 1.15%, RMSE = 1.66) outperformed the RSM model (R 2 =0.9541, AAD = 7.07%, RMSE = 3.99) in terms of the R 2 , AAD, and RMSE when predicting the Cu (II) removal and is thus more reliable. The Langmuir and Freundlich isotherm models were applied to the equilibrium data and the results revealed that Langmuir isotherm (R 2 = 0.9998) had better correlation than the Freundlich isotherm (R 2 = 0.9461). Experimental data were also tested in terms of kinetics studies using pseudo-first order and pseudo-second order kinetic models. The results showed that the pseudo-second-order model accurately described the kinetics of adsorption.

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Biosorption of Multifold Toxic Heavy Metal Ions from Aqueous Water onto Food Residue Eggshell Membrane Functionalized with Ammonium Thioglycolate

Cited by 82 publications

References 44 publications

Sensitive electrochemical sensor based on poly(l-glutamic acid)/graphene oxide composite material for simultaneous detection of heavy metal ions

Sensitive electrochemical sensor based on poly(l-glutamic acid)/graphene oxide composite material for simultaneous detection of heavy metal ions

High Adsorption Pearl‐Necklace‐Like Composite Membrane Based on Metal–Organic Framework for Heavy Metal Ion Removal

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

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