Antibacterial wound dressings are promising materials to treat infected skin wounds, which greatly affect the wound-healing process. In this study, tannic acid (TA), a natural antibacterial agent, was successfully loaded by electrospinning into poly(caprolactone) (PCL) fibers in a high concentration. It is suggested that the addition of TA was beneficial for producing uniform and continuous PCL nanofibers. Hydrogen bonds existed between the PCL and TA molecules based on the analysis of FTIR spectra and DSC results. The interactions and continuous network improved the mechanical properties of the scaffolds. Meanwhile, increasing the amount of TA also enhanced the hydrophilicity and water absorption capacity of the scaffold, both of which are beneficial for accelerating wound healing. Moreover, a burst release of the TA in the initial stage and a controlled, steady release behavior over time contributed to the highly antibacterial properties of the PCL/TA scaffolds. The fabrication of the composite scaffold supplies a facile, efficient, and controllable approach to address the issue of antibacterial treatment in wound dressing.

The dual-beard image method, which has been developed in recent years as a fast and economical method for fiber length measurement, consists of dual-beard specimen preparation, image processing, fibrogram extraction, and length parameter calculations. However, one of the shortcomings of this method is that it can only produce extremely limited length parameters such as mean length, coefficient of variation, modal length, and quality length (UHML, upper half mean length). This study introduces a new algorithm for converting the dual-beard fibrogram into a length distribution histogram which can be used to calculate most of the current length parameters. The algorithm is based on the short fiber content formulae but modified by theoretical analysis and experimental comparison. The length distributions of 24 cotton samples and 12 wool samples are measured by dual-beard image method with the new algorithm, and Advanced Fiber Information System (AFIS) and Almeter are employed for comparison. Comparative analysis shows that the peaks and ranges of the distribution histograms using the dual-beard method are similar to those from the reference methods, and the shapes of histograms from difference methods match well with one another. In addition, five length parameters calculated from the dual-beard distributions are verified to be consistent with those measured by AFIS and Almeter. The new algorithm employed in the dual-beard image method avoids the differential operation which amplifies the curve error, giving the dual-beard image method the ability to output more comprehensive length information.

There is a complex interaction between primary fibers in pre-colored fiber blends, which leads to the impracticality of Duncan’s additive theory in the single-constant Kubelka–Munk (KM-1) model, resulting in poor accuracy for color prediction. This paper builds an optimized combined spectral calibration model based on the KM-1 function to solve this issue. This proposed model involves colored and non-colored parts with trained spectral coefficients, and was established to achieve a good matching between the predicted and actual ratio of the absorption coefficient K to the scattering coefficient S, K/ S of samples for color prediction from the perspective of the spectrum. Experimentally, five primary cotton fibers were selected and prepared into fiber blends to demonstrate its validation, and its optimal number of training samples was found to be only 14, which was much less than the general training sampling method using half of all samples directly. Finally, a remarkable average color difference of only 0.63 CIEDE2000 units for 74 test specimens was achieved on this optimized model, which was significantly lower than that of the KM-1 model (∼1.16), two-constant KM model (∼1.03), Stearns–Noechel model (∼1.16) and Friele model (∼1.11). The results indicate that the optimized model behaves well and could be applied in the color prediction of pre-colored fiber blends.

The random beard image method is a newly developed fiber length measurement method with the advantages of speed, reliability and low cost. This paper proposed the methods of acquiring an accurate and stable fibrogram from the transmission image with the random beard image method based on the derived Kubelka–Munk theory. In the dual-beard preparation process, the optical properties of the short fiber region in the beard were changed by the napping effect. To solve this problem, an imitating experiment with five types of wool fibers was designed to obtain the optical coefficient of the maximum value of the fibrograms of 1.03. To eliminate the random error of the sample, the symmetry principle was adopted to determine the start line of the dual-fibrogram, and the weighted smoothing average fibrogram from two dual-beards is used as the final fibrogram. In addition, upon testing 20 types of wool fiber samples by the random beard image method and the international standard instrument Almeter100, the waviness coefficient of 1.10 was determined. A Z-test and Bland–Altman plot were also applied to verify the accuracy and repeatability of the random beard image method. The results show that under the significance level α = 0.05, the accuracy and the repeatability of the two methods are at the same level. This method sets the foundation for measuring the fiber length distribution parameters, especially the short fiber content.