In this work, bimodal Cu nano-inks composed of two different sizes of Cu nanoparticles (NPs) (40 and 100 nm in diameter) were successfully sintered with a multi-pulse flashlight sintering technique. Bimodal Cu nano-inks were fabricated and printed with various mixing ratios and subsequently sintered by a flash light sintering method. The effects of the flashlight sintering conditions, including irradiation energy and pulse number, were investigated to optimize the sintering conditions. A detailed mechanism of the sintering of bimodal Cu nano-ink was also studied via real-time resistance measurement during the sintering process. The sintered Cu nano-ink films were characterized using x-ray photoelectron spectroscopy and scanning electron microscopy. From these results, it was found that the optimal ratio of 40-100 nm NPs was found to be 25:75 wt%, and the optimal multi-pulse flash light sintering condition (irradiation energy: 6 J cm, and pulse duration: 1 ms, off-time: 4 ms, and pulse number: 5) was found. The optimally sintered Cu nano-ink film exhibited the lowest resistivity of 5.68 μΩ cm and 5B adhesion level.

The present report details research work on the photonic sintering of ZnO nanosheets (ZnO NSs), which were synthesized via a solid-state synthesis method.

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In this work, addressable conducting network (ACN) was used for the damage sensing and self-healing of continuous carbon fiber reinforced nylon composite (CFRP). The machine-learning was used for accurate damage sensing by training the resistance change of composites along ACN due to their structural damage. Also, self-healing of the carbon fiber composite material was performed by applying the electrical current to generate local heating through the detected damage location. The self-healing conditions such as the current input pairs of ACN and amount of the electrical current were determined through the artificial neural network (ANN)-based machine-learning technique. As a result, high-accuracy damage sensing based on machine learning with ACN was conducted, and self-healing with a healing efficiency of 98% could be achieved.