Lead (Pb 2+ ) contamination poses a serious threat to human health and aquatic ecosystems, demanding rapid, sensitive, and reliable detection strategies. Herein, we report a novel, low-cost, and robust Tetramethoxyresorcin4arene-based supramolecular fluorescent chemosensor series TMT-PBAP(2a–2d) for selective Pb 2+ recognition. Unlike previously reported sensors, TMT-PBAP achieves ultra-sensitive nanomolar detection 1.16 nM, demonstrating exceptional analytical performance. Structural conversion from TMT-NAP(1a-1d) to TMT-PBAP(2a-2d) produced an optimized binding cavity, enabling strong host–guest supported with ICT and CHEF mechanism. The sensors exhibit high binding constants K = 6.04 × 10 7 M −1 with fluorescence recovery rates of 95–99% with R 2 = 0.99, and real-sample validation in tap and river water consistent with ICP-AES measurements, confirming both sensitivity and practical reliability. Detailed studies, including UV–Vis, fluorescence titration, stoichiometry, anti-interference, pH stability, photobleachability, and DFT calculations , confirmed the selective Pb 2+ binding mechanism. Moreover, the reversible fluorescence enabled molecular logic gate construction, highlighting multifunctional capability. Overall, this work presents a novel, cost-effective, and high-performance supramolecular platform for trace Pb 2+ detection, with strong potential for portable, real-time environmental monitoring in complex environments, providing a sustainable solution for water quality management and environmental safety. Schematic illustration of the fluorescence turn-on sensing mechanism of the TMT-PBAP chemosensor before and after Pb²⁺ ion binding • Tetramethoxyresorcin4arene-based supramolecular chemosensor for selective turn-on fluorescence detection of Pb 2+ . • Achieves detection limits below WHO drinking water standards. • Demonstrates strong stability and reproducibility under varied environmental conditions. • Exhibits integrated logic gate functionality for intelligent water quality monitoring and molecular computing.
Patel et al. (Tue,) studied this question.