As essential components in intelligent systems, printed soft electronics (PSEs) are playing crucial roles in public health, national security, and economics. Innovations in printing technologies are required to promote the broad application of high-performance PSEs at a low cost. However, current printing techniques are still facing long-lasting challenges in addressing the conflict between printing speed and performance. To overcome this challenge, we developed a new corona-enabled electrostatic printing (CEP) technique for ultra-fast (milliseconds) roll-to-roll (R2R) manufacturing of binder-free multifunctional e-skins. The printing capability and controllability of CEP were investigated through parametric studies and microstructure observation. The electric field generation, material transfer, and particle amount and size selecting mechanisms were numerically and experimentally studied. CEP printed graphene e-skins were demonstrated to possess outstanding strain sensing performance. The binder-free feature of the CEP-assembled networks enables them to provide pressure sensitivity as low as 2.5 Pa, and capability to detect acoustic signals of hundreds of hertz in frequency. Furthermore, the CEP technique was utilized to pattern different types of functional materials (e.g., graphene and thermochromic polymers) onto different substrates (e.g., tape and textile). Overall, this study demonstrated that CEP can be a novel contactless and ultrafast manufacturing platform compatible with R2R process for fabricating high-performance, scalable, and low-cost soft electronics.
The phase-type distribution (also known as PH distribution) has mathematical properties of denseness and closure in calculation and is, therefore, widely used in shock model constructions describing occurrence time of a shock or its damage. However, in the case of samples with only interval data, modeling with PH distribution will cause decoupling issues in parameter estimation. Aiming at this problem, an approximate parameter estimation method based on building PH distribution with dynamic order is proposed. Firstly, the shock model established by PH distribution and the likelihood function under samples with only interval data are briefly introduced. Then, the principle and steps of the method are introduced in detail, and the derivation processes of some related formulas are also given. Finally, the performance of the algorithm is illustrated by a case with three different types of distributions.
The traditional shock model generally describes the magnitude of the cumulative damage caused by a random shock sequence and compares the magnitude with a predetermined threshold to obtain the failure time of a component. There are two limitations in this kind of models in practice: First, the statistical characteristics of the damage due to a single shock may be difficult to obtain, which means the magnitude of the damage may not be described by an appropriate distribution; Second, the cumulative shock magnitude may be difficult to measure, or it may be difficult for a failure mode to be described by a threshold, meaning that the magnitude of the damage and the threshold may not be compared with each other. Considering both failure and censored samples, a reliability modeling method is proposed in this work to address the above problems. The shock model is first established by using both continuous and discrete phase-type (PH) distributions. Then the parameter estimation method of the shock model is derived based on EM method and the identifiability of the parameters in PH distributions is also given. Finally, the adaptability of the model is analyzed using three different types of simulation cases.
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