Self-healing coatings have been developed as smart surface coatings for Mg and its alloys to retain local corrosion from the coating damages. In this study, we prepared dicalcium phosphate dihydrate (DCPD) coating on biomedical Mg, and found that the artificial scratches in DCPD coating can be efficiently sealed by anti-corrosive products in both Hank's and normal saline (NS) solutions. Besides, the in-depth study revealed that DCPD was served as not only a physical barrier but also a self-healing agent, demonstrating an autonomous self-healing coating without embedded extra corrosion inhibitors. Moreover, Hank's solution provided foreign-aid film-forming ions to promote self-healing behavior. The findings might offer new opportunities for further studies and applications of efficient self-healing coatings on biodegradable Mg implants.

As a symmetry-breaking system, bent metal nanowires (MNWs) are able to guide asymmetric plasmon modes, but are also subject to deteriorated waveguiding performance. Previous studies mainly focused on bending losses or longitudinal modes, while the inherent asymmetric nature of the transverse mode was often overlooked and its potential in high-performance manipulation has remained unlocked. Meanwhile, they also showed contradictory conclusions regarding the effect of the bend that need further study. Here, we investigate the previously neglected field-deformation effects to demonstrate their significance on mode behaviors, and propose manipulation strategies of asymmetric modes with excellent qualities that are even higher than their symmetric counterparts in straight MNWs. We show that the field distributions can be translationally, rotationally, and longitudinally reformed, allowing not only extra degrees of freedom in manipulations, but also possibilities to convert the bending into a favorable factor for mode quality. Even for sharply bent MNWs with a sub-wavelength bending radius, the quality of the asymmetric mode can be greatly enhanced, even enabling a figure of merit almost twice its symmetric counterpart in straight MNWs without bending. Our findings may greatly extend the capabilities of bent MNWs, offering new opportunities in high-performance plasmonic components and flexible devices.

Retrieving waveguiding properties of plasmonic metal nanowires (MNWs) through numerical simulations is time- and computational-resource-consuming, especially for those with abrupt geometric features and broken symmetries. Deep learning provides an alternative approach but is challenging to use due to inadequate generalization performance and the requirement of large sets of training data. Here, we overcome these constraints by proposing a transfer learning approach for modeling MNWs under the guidance of physics. We show that the basic knowledge of plasmon modes can first be learned from free-standing circular MNWs with computationally inexpensive data, and then reused to significantly improve performance in predicting waveguiding properties of MNWs with various complex configurations, enabling much smaller errors (~23–61% reduction), less trainable parameters (~42% reduction), and smaller sets of training data (~50–80% reduction) than direct learning. Compared to numerical simulations, our model reduces the computational time by five orders of magnitude. Compared to other non-deep learning methods, such as the circular-area-equivalence approach and the diagonal-circle approximation, our approach enables not only much higher accuracies, but also more comprehensive characterizations, offering an effective and efficient framework to investigate MNWs that may greatly facilitate the design of polaritonic components and devices.