State-of-the-art progress and prospect of metal-organic frameworks and composites for photoelectrochemical amino-drugs sensing

Sourav Bej, Eun Bum Cho

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

Unregulated discharge of antibiotics in waterbodies has posed a significant threat to the aquatic flora and fauna in post-pandemic times. This alarming situation has ascertained the need for suitable sensors to detect persistent antibiotic residues. In this context, functional hybrid materials centralized on reticular metal-organic frameworks (MOFs)/composites have been a research hot spot for photoelectrochemical host-guest recognition events over the past two decades. The unique amalgamation of the robust framework, ease of synthesis, and tunable photophysical properties complemented with in silico approaches render these materials highly promising for recognition events over other contemporaries. The present review provides a critical analysis of the state-of-the-art advancement of MOFs along with their allied composites toward the detection of targeted amino-drug residues (nitrofurazone, norfloxacin, ciprofloxacin, tetracycline, acetaminophen) within the last quinquennial period (approximately 2019–2024). Detection of the targeted drug residues by electrochemical and fluorometric pathways and their host-guest mechanistic pathways have been precisely described. Additionally, different functionalization methods and luminescence strategies with their structural viewpoint have been concisely summarized. Moreover, we delve into the futuristic possibility of integrating artificial intelligence (AI) and machine learning (ML) for better quantification of antibiotics. Finally, the unmet challenges and future research directions of the current research strategies have been outlined for automatic ML (AutoML) assisted next-generation POCT device fabrication.

Original languageEnglish
Article number120946
JournalEnvironmental Research
Volume270
DOIs
StatePublished - 1 Apr 2025

Keywords

  • AI-ML integrated POCT
  • Amino antibiotics
  • MOFs composite
  • Photoelectrochemical detection
  • Reticular engineering

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