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http://hdl.handle.net/1942/49880| Title: | Synergistic electrochemistry of YbBi-TCPP modified flexible laser-induced graphene for Cd2+monitoring in food | Authors: | Ma, Huimin Yang, Renhui Wang , Xiaoyue Rao, Nan Zhou, Qi Zhang , Yuanyuan YANG, Nianjun |
Issue Date: | 2026 | Publisher: | ELSEVIER | Source: | Microchemical journal, 228 (Art N° 119229) | Abstract: | Heavy metals, such as Cd2+, are risky to ecosystems and human health, largely because they are poisonous and prone to bioaccumulation. Consequently, it is vital to build a sensing platform that rapidly detects Cd2+ at very low levels. In this work, we introduce an innovative electrochemical sensing platform built upon a flexible laser-induced graphene (LIG) electrode, whose performance is further boosted by incorporating a bimetallic porphyrin MOF (i.e., YbBi-TCPP) produced via a solvothermal method. Benefitting from the orderly pore structure and numerous redox-active centers of YbBi-TCPP as well as the excellent conductivity of LIG, the gained YbBi-TCPP/ LIG displayed apparent cooperation, which substantially boosted the sensitivity of the sensing platform. With the conditions set to their optimum, the sensor exhibited a linear detection scope of 5-400 mu g/L, a detection limit of 0.43 mu g/L, and produced recovery efficiencies that were notably satisfactory (90.2%- 92.9%) in real meat samples including pork, mutton, and chicken. The manuscript introduces a scalable pathway for obtaining bimetallic porphyrin MOF and establishes a corresponding low-expense, high-durability electrochemical sensor for the appraisal of Cd2+. | Notes: | Zhang, YY (corresponding author), Wuhan Inst Technol, Sch Chem & Environm Engn, State key Lab Green & Efficient Dev Phosphorus Res, Wuhan 430205, Peoples R China. yyzhang@wit.edu.cn |
Keywords: | Laser-induced graphene;YbBi-TCPP;Electrochemical sensor;Cd2+;Porphyrin metal-organic frameworks | Document URI: | http://hdl.handle.net/1942/49880 | ISSN: | 0026-265X | e-ISSN: | 1095-9149 | DOI: | 10.1016/j.microc.2026.119229 | ISI #: | 001838743700001 | Rights: | 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. | Category: | A1 | Type: | Journal Contribution |
| Appears in Collections: | Research publications |
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