Flexible wafer-scale bifunctional metasurface based on nanoimprinting.

Metasurfaces have demonstrated remarkable capabilities in manipulating light fields across diverse applications. However, current research tends to examine these functionalities in isolation, prompting a growing interest in integrating different functionalities within a singular metasurface device. In this paper, we propose and experimentally demonstrate a bifunctional metasurface capable of providing concealment and sensing functions simultaneously. Specifically, the proposed nanostructure effectively operates as a one-way mirror, exhibiting an average reflection rate of approximately 90% under external illumination, alongside an absorption rate of 87.9% from the opposite direction of incidence. This functionality renders it suitable for privacy-enhancing building windows. Meanwhile, this nanostructure also integrates liquid sensing capabilities boasting a sensitivity of 464 nm/RIU, which is particularly valuable for monitoring liquid-based corrosion. The experimental performance of the prepared 6-inch nanohole-patterned metasurface closely aligns with the simulations, and the utilization of flexible polyethylene terephthalate (PET) film, coupled with nanoimprint lithography technology, enables a direct and cost-effective manufacturing process that can be scaled up for widespread applications.

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model.

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.