Browsing by Author "Deng, Yibin"

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    Efficient DNA walker guided by ordered cruciform-shaped DNA track for ultrasensitive and rapid electrochemical detection of lead ion
    (Elsevier, 2024-05-08) Zhu, Nuanfei; Wang, Kaixuan; Xiong, Dinghui; Xiao, Jiaxuan; Deng, Yibin; Yang, Zhugen; Zhang, Zhen
    The rational design of DNA tracks is an effective pathway to guide the autonomous movement and high-efficiency recognition in DNA walkers, showing outstanding advantages for the cascade signal amplification of electrochemical biosensors. However, the uncontrolled distance between two adjacent tracks on the electrode could increase the risk of derailment and interruption of the reaction. Hence, a novel four-way balanced cruciform-shaped DNA track (C-DNT) was designed as a structured pathway to improve the effectiveness and stability of the reaction in DNA walkers. In this work, two kinds of cruciform-shaped DNA were interconnected as a robust structure that could avoid the invalid movement of the designed DNA walker on the electrode. When hairpin H2 was introduced onto the electrode, the strand displacement reaction (SDR) effectively triggered movements of the DNA walker along the cruciform-shaped track while leaving ferrocene (Fc) on the electrode, leading to a significant enhancement of the electrochemical signal. This design enabled the walker to move in an excellent organized and controllable manner, thus enhancing the reaction speed and walking efficiency. Compared to other walkers moving on random tracks, the reaction time of the C-DNT-based DNA walker could be reduced to 20 min. Lead ion (Pb2+) was used as a model target to evaluate the analytical performance of this biosensor, which exhibited a low detection limit of 0.033 pM along with a wide detection ranging from 0.1 pM to 500 nM. This strategy presented a novel concept for designing a high-performance DNA walker-based sensing platform for the detection of contaminants.
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    Machine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions
    (Elsevier, 2025-05-15) Wei, Dali; Xu, Cheng; Wang, Ying; Feng, Weiwei; Deng, Chunmeng; Wu, Xiangyang; Deng, Yibin; Yang, Zhugen; Zhang, Zhen
    Due to the high correlation of anions in waters to environmental quality and human health, thus there is urgent need for developing simple and effective sensors to discriminate multiple anions. Herein, a machine learning-assisted fluorescent sensor array based on two luminescent metal–organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for simultaneous discrimination of five anions (F−, PO43−, ClO44−, NO3−, and SO42−). Wherein, UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 2,5-dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green fluorescence emission, possessing good fluorescence property. Interestingly, the anions could effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate diverse fluorescence responses and unique fingerprints, which could be utilized to develop a fluorescence sensor array for the rapid identification of five anions. Under the optimized conditions, the proposed sensor array showed good performance for identifying multiple anions and their mixtures with satisfactory sensitivity. More importantly, the integration of machine learning algorithm and sensor array has successfully achieved accurate identification and prediction of five anions in real water samples, affirming its practicability in actual samples. Our findings provided a promising tool for detecting multiple anions, and inspired potentials of the combination of sensor arrays and machine learning algorithm for pollution control in real waters.