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Adsorption of Methyl Orange from Aqueous Solution on Hydrothermal Synthesized Mg–Al Layered Double Hydroxide

Ai, Lunhong, Zhang, Chunying, Meng, Lanying·2011·Journal of Chemical & Engineering Data

Abstract

In this study, we presented the efficiently adsorptive removal of organic dye, methyl orange (MO), from aqueous solution with the hydrothermal synthesized Mg–Al layered double hydroxide (LDH). The as-obtained product was characterized by X-ray diffraction (XRD), Fourier transformation infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The adsorption characteristics of MO onto the Mg–Al LDH were evaluated in a batch adsorption process and systematically investigated by different experimental parameters, such as initial dye concentration, contact time, and solution pH. The adsorption kinetics were analyzed by pseudofirst-order, pseudosecond-order, Elovich, intraparticle diffusion, and Boyd models, which were well-described by the pseudosecond-order model. The equilibrium adsorption data were interpreted using the Langmuir, Freundlich, and Temkin isotherm models, which fitted well to both the Langmuir and Freundlich models. The monolayer adsorption capacity of the Mg–Al LDH toward MO was found to be 0.453 mol·kg –1 . These findings suggested that the LDH could be regarded as a promising adsorbent for the removal of anionic dye in wastewaters.

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Metadata

Authors
Ai, L., Zhang, C., & Meng, L.
Venue
Journal of Chemical & Engineering Data
Published
2011-10-26
Volume
56
Issue
11
Pages
4217-4225
ISSN
  • 0021-9568 (ISSN-L)
  • 1520-5134
OpenAlex
W2329823466
Cited by
283
References
49
FWCI
9.24
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Adsorption of Methyl Orange from Aqueous Solution on Hydrothermal Synthesized Mg–Al Layered Double Hydroxide. https://app.cochat.ai/papers/10.1021/je200743u
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Ai, L., Zhang, C., & Meng, L. (2011). Adsorption of Methyl Orange from Aqueous Solution on Hydrothermal Synthesized Mg–Al Layered Double Hydroxide. Journal of Chemical & Engineering Data. https://doi.org/10.1021/je200743u

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.