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Preparing 2-methoxy-4-methylphenol (MMP) through the vanillin (VAN) route is a crucial step in the high-value utilization of lignin (VTM). In this study, a series of Ni/PB catalysts were synthesized using pseudoboehmite (PB) as a carrier. Changes in the valence states of Ni were induced through H2 activation, while the impacts of reaction temperature, initial pressure, and catalyst addition were investigated based on the VTM process. The results revealed that PB exhibited a distinct mesoporous structure (average pore size of 5.8 nm), with Ni loaded in small particles size (3.88 nm). Ni species on the catalyst existed in a + 2-valence state, undergoing a change after H2 activation, with an increase in Ni0 promoting enhanced activity. The Ni/PB-450 catalyst exhibited excellent activity at 250&#176C for 3 hours (VAN conversion and MMP selectivity > 99%), and the catalyst demonstrated good durability, maintaining 99.1% VAN conversion and 80% MMP selectivity after two cycles. Combining the characterization and experimental results, the etherification and hydrogenation of VAN were identified as the primary reaction pathways. This study aimed to provide a new avenue for the high-value utilization of lignin and its derivatives through controlled catalyst preparation [ABSTRACT FROM AUTHOR]

Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R² ) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R² = 0.7807, RMSE = 24.6864 Mgha-1), The predicted range of mangrove biomass is 4.623–206.975 Mgha-1 . Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves. [ABSTRACT FROM AUTHOR]