Tailoring adsorbents for levodopa detection: a DFT study on Pt-encapsulated fullerene systems.
Wendy MaxakatoMiracle N OgboguAdebayo P AdeleyeIsmail O AmoduInnocent BenjaminHenry O EdetPublished in: RSC advances (2024)
Despite its effectiveness in managing the motor symptoms of Parkinson's disease, levodopa therapy is often accompanied by adverse effects that can significantly reduce patients' quality of life. Hence, the need to detect levodopa has escalated among researchers and health experts. Herein, the intricacies of levodopa adsorption were studied using newly tailored fullerene-based adsorbents. All theoretical calculations were performed using the DFT/PBE1PBE/GENECP level of theory. Having modified the surface by Pt-encapsulation followed by functionalization with a functional group (COOH, HCO, NH 2 , NO 2 , and OH), new materials were engineered towards levodopa adsorption. Various theoretical and computational analyses were thoroughly explored to gain insight into the electronic properties, nature of inter- and intra-molecular interactions, strength and phenomenal of adsorption, and the mechanisms of sensing. Adsorption was found to have taken place from the region of the functional groups, where adsorption strength is influenced by the varying electron-withdrawing abilities of the groups. In all cases, the adsorption phenomenon is best described as physisorption. Changes in the dimensions are attributed to the stretching vibration of the bonds on the surface. Also, the small energy gaps within a close range of 0.295 to 0.675 eV exhibited by the materials upon adsorption are an indication of semiconductors. Hence, the functionalized systems hold promise as adsorbents for levodopa molecules, offering valuable insights for future research endeavors.
Keyphrases
- parkinson disease
- aqueous solution
- deep brain stimulation
- density functional theory
- end stage renal disease
- systematic review
- public health
- randomized controlled trial
- healthcare
- chronic kidney disease
- mental health
- ejection fraction
- newly diagnosed
- mesenchymal stem cells
- health information
- artificial intelligence
- room temperature
- current status
- machine learning
- sleep quality
- molecular dynamics simulations
- crystal structure
- mass spectrometry
- high resolution
- health promotion