Wood-based passive daytime radiative cooling (PDRC) materials offer a sustainable alternative to synthetic polymers but typically suffer from insufficient solar reflectance and high hygroscopicity. Herein, a superhydrophobic radiative cooling wood (RCW) was fabricated via delignification, in-situ SiO2 mineralization, and hydrophobic modification. Benefiting from the hierarchical porous structure of delignified wood (DW), the nano‑morphology of anchored SiO2, and fluorination treatment, the formed micro/nano rough surface and low‑surface‑energy components not only endowed the wood‑based material with excellent superhydrophobicity but also enhanced solar reflection/scattering and infrared emission capabilities, resulting in outstanding radiative cooling performance. The resulting RCW achieved a high solar reflectance of 92.73
The effects of xenon lamp accelerated aging on the acoustic vibration performance of Phyllostachys edulis (Carrire) J. Houz. and Paulownia elongata S. Y. Hu were thoroughly investigated. This study also evaluated the changes in chemical and physical properties, and pore structure under the xenon lamp accelerated aging conditions, and attempt was made to evaluate the relationship of the acoustic vibration performance with the chemical and physical properties, and pore structure. During the photoaging process of the bamboo and wood, the lignin content decreased by 7.76
The inherent flammability of wood constrains its application in scenarios demanding high safety standards. Conventional single-element flame-retardant strategies struggle to reconcile flame-retardant efficiency, smoke suppression, and durability, which has driven a paradigm shift toward multi-element synergistic design. Framed around flame-retardant element combinations, this review systematically examines the key mechanisms by which P, N, Si, B, S, and metals/inorganics function in both the condensed and gas phases, with particular emphasis on the construction strategies of binary and multi-element synergistic systems, including P–N, P–Si, and P–metal. The review demonstrates that the P–N binary system, relying on the acid-source/gas-source synergistic mechanism, currently represents the most widely applied and most efficient foundational strategy. The incorporation of a third component, such as metal, Si, or B, can compensate for the inherent deficiencies of binary systems, namely the susceptibility of the char layer to oxidation and insufficient smoke suppression, thereby enabling multi-functional integration. Finally, the design principles of multi-element synergistic systems are summarized, and future research directions are outlined, including green bio-based feedstocks, low-cost processing, durability, and validation under realistic fire scenarios, aiming to provide a theoretical reference for the development of next-generation high-performance, environmentally friendly flame-retardant wood.
This study reports a biogenic and eco-friendly approach for the synthesis of silver (BSCAgNPs) and iron oxide nanoparticles (BSCFeNPs) using Oriental beech (Fagus orientalis L.) steaming condensate (BSC) as a dual reducing and stabilizing matrix, enabling the valorization of an underutilized wood-industry by-product. Transmission electron microscopy revealed distinct nanostructures, with BSCAgNPs ranging from 11 to 38 nm and highly uniform ultrasmall BSCFeNPs of 3 to 6 nm, while Fourier transform infrared spectroscopy (FTIR) and EDX analyses revealed changes in BSC-derived functional groups associated with NP formation, while EDX confirmed the elemental presence of Ag and Fe in the respective samples (Ag 60.26
Lignocellulosic biomass is an abundant renewable resource with significant potential for sustainable biofuel and biorefinery applications. Efficient lignin removal is a critical step in biomass valorization because it improves cellulose accessibility and enhances downstream conversion efficiency. Recently, deep eutectic solvents (DES) have emerged as promising green solvents for biomass pretreatment due to their biodegradability, low toxicity, tunable properties, and environmentally friendly nature. However, the efficiency of DES-based lignin extraction is governed by complex nonlinear interactions among the solvent composition, biomass characteristics, and pretreatment conditions, making process optimization challenging. In this study, a deep learning–driven framework is proposed to accurately predict lignin removal efficiency and elucidate the underlying mechanisms of DES-based biomass fractionation. A comprehensive dataset comprising 509 experimental conditions was compiled, incorporating biomass composition, DES properties, and pretreatment parameters. A deep neural network (DNN) model was developed using PyTorch and optimized via the Optuna framework to capture nonlinear relationships and interaction effects. The proposed model achieved strong predictive performance on an independent test set dataset, with R² = 0.86 ± 0.01, RMSE = 6.0 ± 0.2, and MAE = 4.8 ± 0.2, demonstrating high accuracy and generalizability. To enhance interpretability, SHapley Additive exPlanations (SHAP) were employed to quantify feature contributions. The analysis revealed that the hydrogen bond donor acidity/basicity (HBD-pKa/pKb) is the most influential factor, while interaction features such as LSR × LogR0 and the HBD/HBA ratio × LogR0 significantly influence lignin removal efficiency. These findings highlight that lignin extraction is governed primarily by interaction-driven mechanisms rather than isolated variables. Compared with conventional tree-based models, the proposed DNN framework more effectively captures higher-order dependencies and provides deeper mechanistic insights. Overall, this study demonstrates the potential of integrating deep learning with explainable AI for the rational design and optimization of DES systems. The findings can support the development of efficient and sustainable biomass pretreatment strategies for future biorefinery applications.
