Objective The purpose of this study is to develop a mathematical model for efficient prediction of the packing density of different filler formulations in dental resin composites (DRCs), and to study properties of DRCs at the maximum filler loading (MFL), thereby providing an effective guidance for the design of filler formulations in DRCs to obtain excellent properties. Methods The packing density data generated by discrete element model (DEM) simulation were used to re-derive the parameters of 3-parameter model. The modifier effect was also induced to modify the 3-parameter model. DRCs with 10 filler formulations were selected to test properties at the MFL. The packing densities of binary and ternary mixes in DRCs were calculated by 3-parameter model to explore the regularity of composite packing. Results The predicted packing density was validated by simulation and experimental results, and the prediction error is within 1.40 vol%. The optimization of filler compositions to obtain a higher packing density is beneficial to enhancing the mechanical properties and reducing the polymerization shrinkage of DRCs. In binary mixes, the maximum packing density occurs when the volume fraction of small fillers is 0.35−0.45, and becomes higher with the reduction of particle size ratio. In ternary mixes, the packing density can reach the maximum value when the volume fractions of large and small fillers are in the 0.5−0.75 and 0.15−0.4 ranges, respectively. Significance The modified 3-parameter model can provide an effective method to design the multi-level filler formulations of DRCs, thereby improving the performance of the materials.
Electrospinning is a versatile technique to fabricate nanofiber filters with high PM 2.5 removal efficiency and relatively low pressure drop. The eletrospun nanofiber filters may therefore be applied in buildings to reduce indoor exposure to PM 2.5 and the associated adverse health effects . This study investigated the influence of various filter parameters, including fiber diameter , filter thickness, and packing density, on the PM 2.5 removal efficiency. In this work, 25 nylon electrospun nanofiber filters with different filter parameters were prepared, and the PM 2.5 removal efficiency of each sample was measured at five different face velocities. In total, 125 sets of measured data were obtained. The results show that the PM 2.5 removal efficiency of nylon electrospun nanofiber filters was negatively associated with the fiber diameter, and positively associated with the thickness of the filter. However, there was no clear correlation between PM 2.5 removal efficiency and packing density. This investigation further developed a semi-empirical model for predicting the PM 2.5 removal efficiency of nylon nanofiber filters. The accuracy of the model was satisfactory with a median relative error of 7.9%.
危险品化学品经营许可证（带存储）