A magnetic graphene oxide and UiO-66 based homogeneous dual recognition electrochemical aptasensor for accurate and sensitive detection of aflatoxin B1.

Aflatoxin (AFs) contamination is one of the serious food safety issues. Aflatoxin B1 (AFB1) is the most common and toxic aflatoxin, which has been classified as a class 1 carcinogen by the International Agency for Research on Cancer (IARC). It is extremely destructive to liver tissue. Developing a convenient and sensitive detection technique is essential. In this paper, we developed a homogeneous dual recognition strategy based electrochemical aptasensor for accurate and sensitive detection of aflatoxin B1 (AFB1) based on the magnetic graphene oxide (MGO) and UiO-66. The MGO was synthesized for the recognition and magnetic separation of AFB1 from complex samples. UiO-66/ferrocenecarboxylic acid (Fc)/aptamer composites were constructed as both recognition and signal probes. The probes would specifically capture AFB1 enriched by MGO, which enables dual recognition in homogeneous solution, thus further improving the accuracy of AFB1 detection. The electrochemical aptasensor for AFB1 had a linear range from 0.005 to 500 ng mL-1. Additionally, the limit of detection was 1 pg mL-1. It shows a favorable potential for both sensitive and accurate detection of AFB1 in real samples.

Rapid detection of foodborne pathogens in diverse foodstuffs by universal electrochemical aptasensor based on UiO-66 and methylene blue composites.

Rapid and sensitive detection of foodborne pathogens in complex environments is essential for food protection. A universal electrochemical aptasensor was fabricated for the detection of three common foodborne pathogens, including Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Salmonella typhimurium (S. typhimurium). The aptasensor was developed based on the homogeneous and membrane filtration strategy. Zirconium-based metal-organic framework (UiO-66)/methylene blue (MB)/aptamer composite was designed as a signal amplification and recognition probe. Bacteria were quantitatively detected by the current changes of MB. By simply changing the aptamer, different bacteria could be detected. The detection limits of E. coli, S. aureus and S. typhimurium were 5, 4 and 3 CFU·mL-1, respectively. In humidity and salt environments, the stability of the aptasensor was satisfactory. The aptasensor exhibited satisfactory detection performance in different real samples. This aptasensor has excellent potential for rapid detection of foodborne pathogens in complex environments.

Physics-informed multi-step real-time conflict-based vehicle safety prediction.

Real-time vehicle safety prediction is critical in roadway safety management as drivers or vehicles can be altered beforehand to take corresponding evasive actions and avoid possible collisions. This study proposes a physics-informed multi-step real-time conflict-based vehicle safety prediction model to enhance roadway safety. Physics insights (i.e., traffic shockwave properties) are combined with data-driven features extracted from deep-learning techniques to improve prediction accuracy. A time series of future vehicle safety indicators are predicted such that vehicles/drivers have enough time to take precautions. The safety indicator at each time stamp is a continuous value that the sign reflects the presence of conflict risks, and the absolute value indicates the conflict risk level to advise different magnitudes of evasive actions. A customized loss function is developed for the proposed prediction model to give more attention to risky events, which are the focus of safety management. The prediction superiority of the proposed model is proven through numerical experiments by comparing it with three benchmarks constructed based on the literature. Further, sensitivity analysis on key model parameters is carried out to advise parameter selections in developing real-world conflict-based vehicle safety prediction applications.