Wetting-enhanced adhesion of photo-polymerized supramolecular adhesives for both smooth and rough surfaces.

Efficient interactions between an adhesive and a substrate surface at the molecular level are the basis for the formation of robust adhesion, which substantially relies on interfacial wetting. However, strong adhesives usually improve cohesion but compromise interfacial properties. Herein, we have reported a kind of robust supramolecular adhesive based on the outstanding mobility and interfacial wettability of adhesive precursors. In situ fast photopolymerization endows supramolecular adhesives with more outstanding adhesion for both smooth and rough surfaces in air and underwater in contrast to their counterparts from thermal polymerization. In addition to their low viscosity and high monomer concentration, supramolecular adhesive precursors without any organic solvents possess well-defined hydrogen bonding interactions. These superior properties consistently contribute to the wetting of the substrate and the formation of adhesive polymers with high molecular weights. This work highlights that enhancing interfacial wetting between an adhesive and a substrate is a promising route to achieving robust adhesion.

Fine-scale monitoring of lake ice phenology by synthesizing remote sensed and climatologic features based on high-resolution satellite constellation and modeling.

Lake ice, as a crucial component of the cryosphere, serves as a sensitive indicator of climate change. Fine-scale monitoring of spatiotemporal patterns in lake ice phenology holds significant importance in scientific research and environmental management. However, the rapid and dynamic nature of the freeze-thaw process of lake ice poses challenges to existing methods, resulting in their limited application in small lakes. In this study, we propose a novel approach of investigating ice phenology of lakes in various sizes. We conducted a case study in Hoh Xil, known for its vulnerability to climate change and a wide distribution of small lakes, to analyze the ice phenology of 372 lakes (>1 km2) during 2017-2021. Firstly, ensemble machine-learning model was developed for lake ice identification from Landsat-8/9 and Sentinel-2 A/B imagery. The accuracy evaluation reveals the overall good performance for ice extraction results based on Landsat-8/9 (97.03 %) and Sentinel-2 A/B (96.89 %). Next, the XGBoost models were employed to reconstruct ice coverages on unobserved dates for the freezeup and breakup periods, respectively. Totally, 744 XGBoost models were constructed for the study lakes, and the majority of them perform well. Based on the reconstructed daily ice coverage, phenology parameters could be extracted for examining the spatiotemporal characteristics of ice cover and possible relationships with lake sizes and terrains. From early-October to early-November, the Hoh Xil lakes freeze from the northwest to the southeast, while the breakup period starts in late-March and lasts until late-June. Moreover, the results indicate relatively small variability in freezeup-end dates among lakes, but significant differences in breakup dates, showing a greater sensitivity to temperature variations. Furthermore, ice phenology in small lakes exhibit stronger consistency with subtle climatic fluctuations. The results highlight the significant role of ice phenology in small lakes, as they dominate the overall tendency of ice phenology in Hoh Xil.