ArticleEnvironmental science and pollution research international2020
Exploring of toxic Pb(II) removal by low-cost bio-adsorbent of camphor leaf forestry waste after camphor oil extraction.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Synthesis of Magnetic Biosorbent from Bamboo Powders and Their Application for Methylene Blue Removal from Aqueous Solution: Kinetics, Isotherm, and Regeneration Studies.Molecules (Basel, Switzerland) · 2025Article
- Recent Progress on the Adsorption of Heavy Metal Ions Pb(II) and Cu(II) from Wastewater.Nanomaterials (Basel, Switzerland) · 2024Review
- Aminophosphonic Acid Functionalized Cellulose Nanofibers for Efficient Extraction of Trace Metal Ions.Polymers · 2020Article
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Authors and funding
6 authors.
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Abstract
Camphor leaf (CL) was widely used to extract camphor oil and thus led to abundant forestry waste. In order to reduce pollution, the waste CL was used to prepare bio-adsorbent for Pb(II) removal after alkali treatment and functional modification. The effects of solution pH, initial Pb(II) concentration, contact time and solution temperature were investigated on adsorption process to evaluate the potential application in heavy metal ions' removal. It was found that the massive hydroxyl groups released and plenty of micro-pores formed after the alkali treatment of CL bio-adsorbent, which obviously increased the Pb(II) adsorption. And the adsorption performance promoted continually after further functional modification by ionized 1,2,3,4-butanetetracarboxylic acid (BTCA). The increase of pH was favourable for the adsorption even though the precipitation effect was deducted. Linear fitting method was more suitable to describe the adsorption process than nonlinear fitting method, including adsorption isotherms and adsorption kinetics research. The adsorption thermodynamics was better to be described by nonlinear fitting method due to its lower root mean square error (RMSE) value and higher R
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