MARATTO

article · RSC Advances

Functionalized carbon nanotubes for selective lithium recovery from spent lithium-ion batteries: a sustainable approach to resource recycling

2026Open accessAin Shams University

Abstract

The intensive use of lithium-ion batteries (LIBs) has led to a surge in battery waste, which is a valuable secondary resource for metal recovery. Discarded LIBs, rich in critical metals like lithium and cobalt, offer a concentrated and economically attractive source, addressing supply limitations and environmental protection. The current study presents a novel, low-cost method for selectively recovering lithium ions from both synthetic solutions and LIB waste. Using a newly created adsorbent, mesoporous carbon nanotubes functionalized with aminomethylenephosphonic acid (CNT-AMP), lithium ions were selectively extracted. Extensive characterization of the structural and functional features of CNT-AMP and/or CNT-AMP/Li was carried out utilizing a variety of techniques, including FT-IR, XPS, BET, magnetization investigations, TEM, <sup>1</sup>H-NMR, <sup>13</sup>C-NMR, and GC-MS. The ideal lithium recovery (299 mg g<sup>-1</sup>) was obtained at 500 mg L<sup>-1</sup> lithium ions, 0.08 g CNT-AMP, pH 12, 25 °C, and 100 minutes of contact time. The adsorption behavior was highly consistent with the Langmuir and D-R models, indicating that chemisorption is the primary driving force behind the process. In an ideal setting, the recovery procedure produced 99.5% pure lithium carbonate (Li<sub>2</sub>CO<sub>3</sub>), which is confirmed by XRD and SEM, with a recovery efficiency of 95.76%. The results demonstrate that CNT-AMP can be utilized as a high-performance adsorbent for the sustainable recovery of lithium as a valuable recycling metal from battery waste. This helps conserve resources and promote environmental sustainability.

Research topics

  • Extraction and Separation Processes
  • Recycling and Waste Management Techniques
  • Ionic liquids properties and applications

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.1039/d5ra07566a

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.