Xiaobin Chen

Amid intensifying environmental and energy pressures, sustainable thermal‐insulation materials that also provide effective fire protection are increasingly needed for buildings. Cellulose‐based foams are promising candidates for building‐envelope applications; however, their practical deployment is hindered by limited fire performance, inadequate structural stability, and complex processing. Here, we propose a multiscale biomimetic strategy inspired by mussel adhesion and hierarchical brick‐and‐mortar architectures. Polydopamine is introduced as an interfacial bridging layer to uniformly immobilize bentonite nanosheets within a cellulose network, enabling the fabrication of high‐efficiency flame‐retardant cellulose‐based biomimetic foam (CBF) through aqueous mechanical foaming and ambient‐pressure drying. The resulting CBF exhibits low thermal conductivity alongside improved flame retardancy and environmental compatibility. A cradle‐to‐grave life‐cycle assessment further indicates lower greenhouse‐gas emissions and reduced toxicity‐related impacts than conventional petroleum‐derived foams, while retaining recyclability and biodegradability. Collectively, these results establish a green, scalable route to high‐performance, degradable thermal‐insulation materials for safer and more energy‐efficient buildings.

Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach.

At present, the theoretical estimation of paper web’s evaporation heat is based on sorption isothermals. The measuring conditions are harsh, and the test speed is slow. This paper attempts to explore a theoretical method which can quickly determine the evaporation heat of paper web. During the new method, based on the measurement of the paper drying curve, the theoretical estimation model of paper evaporation heat was obtained by deducing the mechanism of heat and mass transfer. Compared with the traditional method based on sorption isothermals, the new model based on the drying curve has some advantages in measurement speed and easy access to basic data. Finally, the paper verifies the reliability of the model from two application scenarios of the laboratory and production line.

The pulp and paper sector is the fourth largest industrial sector in terms of energy use in the world. Of the numerous processes involved in paper-making by a paper machine, the dryer section is the process that consumes the largest amount of energy. A model-based method for estimating energy-saving potentials of the dryer section was put forward in the present study. It was done by four steps: establishing a mathematical model about energy consumption, determining the model parameters, benchmarking the drying performance to obtain the corresponding energy-saving measures, and estimating the energy-saving potentials by using the mathematical model. In a case study, a multi-cylinder dryer section was selected to illustrate the method. After a fundamental field test and observation, several operating problems that restricted the energy performance of paper drying were found. And then several reasonable energy-saving measures were suggested to the operators. Finally, applying the mathematic model, it was found that 0.32 ton of steam will be saved when producing 1 ton of paper in recommended operating conditions. With the designed capacity of 200,000 tons/year, the annual steam-savings will be 64,000 tons. Generally, the price of steam is 130–150 Chinese Yuan (about US$21–24) in China, and the annual economic benefits will be 8.32–9.60 million Chinese Yuan (about US$1.344–1.536 million).

Paper drying is a highly energy-intensive and complicated multivariate process. The dryer section plays an important role in the energy consumption of a paper machine, especially of thermal energy. A comprehensive method for assessing the energy performance of the dryer section was investigated in this study to improve energy efficiency. This method was divided into three component processes: energy and evaporation load audit, field test and observation, and energy flow analysis and energy efficiency estimation. In a case study, we found that the method could, in addition to analyzing the key factors that restrict drying efficiency, also depict the details of energy consumption clearly. At the same time, several significant energy-saving measures were suggested to improve the energy efficiency of the paper-drying process.