Cephalexin Adsorption by Acidic Pretreated Jackfruit Adsorbent: A Deep Learning Prediction Model Study

Abstract

Cephalexin (CFX) residues in the environment represent a major threat to human health worldwide. Herein we investigate the use of novel approaches in deep learning in order to understand the mechanisms and optimal conditions for the sorption of cephalexin in water onto an acidic pretreated jackfruit peel adsorbent (APJPA). The interaction between the initial concentration of CFX (10–50 mg/100 mL), APJAP dosage (3–10 mg/100 mL), time (10–60 min), and the pH (4–9), was simulated using the one-factor-at-a-time method. APJPA was characterized by FESEM images showing that APJPA exhibits a smooth surface devoid of pores. FTIR spectra confirmed the presence of -C-O, C–H, C=C, and -COOH bonds within the APJPA. Maximum removal was recorded with 6.5 mg/100 mL of APJAP dosage, pH 6.5, after 35 min and with 25 mg/100 mL of CFX, at which the predicted and actual adsorption were 96.08 and 98.25%, respectively. The simulation results show that the dosage of APJAP exhibits a high degree of influence on the maximum adsorption of CFX removal (100%) between 2 and 8 mg dose/100 mL. The highest adsorption capacity of APJAP was 384.62 mg CFX/g. The simulation for the effect of pH determined that the best pH for the CFX adsorption lies between pH 5 and 8.

Single-step pyrolysis of biomass waste-derived activated carbon encapsulated zero-valent nickel NPs for the purification of antibiotic-contaminated water

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Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques

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