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Interpretable machine learning and greenness assessment for sustai 2026 Chem

Sujesh Sudarsan, Gagana Basavaraja

2026entetracyclineactivated carbonalmond peeladsorptionphytotoxicitymachine learning

Abstract

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This study presents the conversion of almond peel waste into activated carbon (APAC) and its use for removing tetracycline (TC) antibiotic from water. The prepared APAC possesses a rough, highly porous, and mesoporous carbon structure with a high surface area, as revealed by FESEM and BET analysis. FTIR, XRD, and XPS results showed the presence of oxygenated and phosphorus-containing surface functionalities and a predominantly amorphous carbon framework, which favored effective TC adsorption. Adsorption experiments indicated that TC removal was best described by the Elovich kinetic model (R² = 0.9999), while equilibrium data fit the Freundlich isotherm model (R² = 0.9961), suggesting adsorption on a heterogeneous surface. The maximum adsorption capacity obtained from the Langmuir model was 87.91 mg/g. Thermodynamic results confirmed that the process was spontaneous and exothermic, with a ΔH° value of -14.76 kJ/mol. After 3 reuse cycles, APAC retained approximately 76.65% of its initial removal efficiency and showed reliable TC removal in real water samples. Phytotoxicity tests using Brassica juncea demonstrated that APAC treatment significantly reduced the toxic effects.

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Cite This Work

@article{7e394621-e631-4b21-9e6e-2ad3ce15a55b,
  title={Interpretable machine learning and greenness assessment for sustai 2026 Chem},
  author={Sujesh Sudarsan and Gagana Basavaraja},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - Interpretable machine learning and greenness assessment for sustai 2026 Chem
AU  - Sujesh Sudarsan
AU  - Gagana Basavaraja
PY  - 2026
LA  - en
ER  -

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