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The outbreak of coronavirus disease 2019 has seriously threatened human health. Rapidly and sensitively detecting SARS-CoV-2 viruses can help control the spread of viruses. However, it is an arduous challenge to apply semiconductor-based substrates for virus SERS detection due to their poor sensitivity. Therefore, it is worthwhile to search novel semiconductor-based substrates with excellent SERS sensitivity. Herein we report, for the first time, Nb2C and Ta2C MXenes exhibit a remarkable SERS enhancement, which is synergistically enabled by the charge transfer resonance enhancement and electromagnetic enhancement. Their SERS sensitivity is optimized to 3.0 × 106 and 1.4 × 106 under the optimal resonance excitation wavelength of 532 nm. Additionally, remarkable SERS sensitivity endows Ta2C MXenes with capability to sensitively detect and accurately identify the SARS-CoV-2 spike protein. Moreover, its detection limit is as low as 5 × 10−9 M, which is beneficial to achieve real-time monitoring and early warning of novel coronavirus. This research not only provides helpful theoretical guidance for exploring other novel SERS-active semiconductor-based materials but also provides a potential candidate for the practical applications of SERS technology.

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In order to achieve a longer life cycle, higher reliability is necessary for products. Generally, the traditional reliability analysis methods can be performed based on sufficient failure data. However, it is difficult to get such amount of failure data in practical engineering, which brings challenges to the traditional reliability analysis methods. Consequently, the traditional reliability analysis methods are not suitable for the case of no failure data or less failure data. In order to tackle the above challenges, an uncertainty analysis strategy using accelerated performance degradation information is given. While, in this method, the utilization of acceleration factor increases the number of model parameters, which lead to the loss of the accuracy of the model parameter estimation under the finite degradation data. To enhance the above strategy, a reliability analysis method of accelerated performance degradation based on Bayesian strategy is proposed in this study. The accelerated performance degradation analysis method combining historical degradation data and empirical information is introduced here. An engineering example of CNC machine tool function milling head is also used to illustrate the effectiveness of the given method.

Ag–ZnO heterojunction promotes the separation of photogenerated pairs and thus exhibits high catalytic activity under UV-visible light.

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