Search

This work reports a dual-ratiometric self-powered photoelectrochemical sensor (SPPS) by the combination of the photocathode with potassium persulfate (K 2 S 2 O 8) as mediator and the photoanode with spatial-resolution technique. The key point is to utilize the activation and conversion of K 2 S 2 O 8 as a mediator to generate two power peaks (P Max1 and P Max2) simultaneously. Spatial-resolution technique offers reference signals (P Max3 and P Max4) for two power peaks, respectively. With chloramphenicol (CAP) as model analytes, a dual-ratiometric SPPS was developed with aptamer as recognition unit. Briefly, the specific capture of CAP increased the power peaks gradually to produce sensing signals which is capable of quantifying CAP. The sensor can provide two sets of sensing signals related to the CAP concentration, i.e., P Max1 /P Max3 (R = 0.997) and P Max2 /P Max4 (R = 0.999) with a detection limit (S/N = 3) of 0.29 pM. Compared with single-signal or single-ratiometric SPPS, the dual-ratiometric SPPS exhibits improved analytical performances, proposing a feasible way to realize self-checking and anti-interference. [Display omitted] • A dual-ratiometric self-powered sensor was proposed for the first time. • The sensor was obtained by coupling K 2 S 2 O 8 with spatial-resolution technique. • V 2 C MXene was introduced to improve the sensitivity. • The self-powered sensor showed self-checking and anti-interference capability. • The work offers a strategy for building multiple-ratiometric self-powered sensors. [ABSTRACT FROM AUTHOR]

In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of their high accuracy rate. However, researchers have found that adversarial attacks can significantly reduce the accuracy of DNNs. Studies have been conducted to defend ECG-based DNNs against traditional adversarial attacks, such as projected gradient descent (PGD), and smooth adversarial perturbation (SAP) which targets ECG classification; however, to the best of our knowledge, no study has completely explored the defense against adversarial attacks targeting ECG classification. Thus, we did different experiments to explore the effects of defense methods against white-box adversarial attack and black-box adversarial attack targeting ECG classification, and we found that some common defense methods performed well against these attacks. Besides, we proposed a new defense method based on adversarial distillation training (named CardioDefense) which comes from defensive distillation and can effectively improve the generalization performance of DNNs. The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization. Furthermore, we found that our method performed better against PGD attacks with low noise levels, which means that our method has stronger robustness. • We explored the defense against the adversarial attack in ECG classification. • We found it is easy to defend the adversarial attack in ECG classification. • We proposed a novel method called CardioDefense, it performs better than baselines. [ABSTRACT FROM AUTHOR]