The characteristics of the pigments and the composition of the formulation have a decisive influence on the appearance of coloured plastics, mainly in terms of their reflectance and transmittance. This research presented a novel analytical approach to quantify the optical properties of pigments and predict the appearance of plastics containing these pigments accordingly. Firstly, the Monte Carlo method was employed to characterise the measurement geometry of the spectrophotometer and the light transfer in coloured plastics. Based on this, the paper proposed a method for determining the absorption coefficients and scattering coefficients of the substrate and the pigments according to reflectance and transmittance measurements. To verify the efficacy of the approach, 25 polyethylene-based samples were prepared. The samples 1-15 were used to determine the optical properties of the respective pigments, and the samples 16-25 to test the predictive capabilities of the model. The result showed that the method can reliably predict the reflectance, transmittance and colour of plastic formulations.

Colour formulation prediction based on a neural network aims to achieve target colours through various colourants. Currently existing methods cannot fully fit the non-linear relationship between target colour and formulation because of neglect of the potential regularities between colour samples from different processes and the lack of effective utilisation of global features and local features. To address these issues, a novel method of colour information combination and the Global-Detail Colour Feature Fusion (GD-CFFusion) network framework is proposed for the first time. Complete formulation information under specific process conditions can be provided by colour information combinations, which consist of reflectance curves of target colours, CIELab values, colourant selections, contrast considerations, backing types and colourant concentration gradients. GD-CFFusion consists of a global feature enhancer based on Transformer, a detail feature enhancer based on a convolutional neural network and a feature fusion summariser. It achieves the precise fitting of the non-linear relationships between colour information combinations and formulations as well as reflectance curves of formulation. Additionally, the underlying patterns between colour samples from different processes and intrinsic relationships of each sample through colour information combination can be learned. The plastic colour matching dataset consists of collated plastic colour samples on which GD-CFFusion was trained. Experimental results showed that GD-CFFusion achieved a total loss of 0.0203 and a formulation loss of 0.0388 on the test set of 4114 samples. This result outperforms all baseline networks and most other colour formulation prediction methods.

The prediction of colour formulation is an important step in reproducing the target colour. At present, there are relatively few researches on multi-objective colour formulation problem, and the colour matching accuracy needs to be improved. In this research, a multi-objective evolutionary meta-heuristic method based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II) was proposed to predict the target colour recipes. The method used dye concentration as a variable and included three objective functions: (1) minimising the CMC (Colour Measurement Committee) colour difference between the formulation colour and the target colour, (2) minimising the metamerism index, and (3) minimising the cost of the formulation. The algorithm could obtain the Pareto optimal solution set after iteration. On this basis, the best combination of formulations was selected from the optimal solution set by combining the Expert Scoring Method (ESM), Entropy Weight Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The prediction effect of the model was evaluated by taking cotton fabrics and reactive dyes actually used in plant as examples. The results showed that 87.5% of the formulations met the CMC colour difference value of no more than 1, the metamerism index of 90.0% of the formulations did not exceed 1, and the cost of 92.5% of the formulations was reduced relative to the maximum extent in the Pareto optimal solution set. Further studies should be focused on removing duplicate individuals to give better diversity in the Pareto optimal solution set.

为了实现国家制定的碳达峰和碳中和的目标,开发具有节能减排的新型前处理加工新技术,对国内外的相关研究进展进行综述.主要介绍生物技术、物理技术及现代化学技术在天然纤维素纤维前处理加工中的应用,包括生物酶前处理、超声波前处理、臭氧前处理、超临界CO2流体前处理、二氧化氯前处理以及电化学前处理,并与传统前处理方法进行比较分析.结果表明,新型前处理方法基本解决了传统前处理加工过程中耗能高以及污染严重等问题;指出新的前处理方法在纤维素纤维前处理中的优势与不足,并对未来的研究和发展进行展望.

Zeolitic imidazolate framework (ZIF) materials have advantages such as large specific surface area, high porosity, adjustable skeleton structure, and easy functionality, but their poor electrical conductivity limits their application in the field of piezoresistive sensors and electrochemistry. To solve this problem, we prepared polypyrrole hollow tubes on the surface of polyester-cotton fabric using the soft template method and in situ polymerization method, the cross-leaf ZIF-L (L means leaf shape) was grown on the surface of polypyrrole hollow tubes using the in situ growth method, and the properties of piezoresistive sensing, electrochemistry, and surface wettability were tested. The experimental results showed that the three-dimensional network structure polypyrrole hollow tube/cross-leaf ZIF-L composite combined the advantages of polypyrrole hollow tube and ZIF-L materials, and the average sensitivity reaches 6.12 kPa−1, which was 5.3 times that of the polypyrrole hollow tube composite, and 1.9 times that of the cross-leaf ZIF-L composite. As an excellent energy storage material, the specific capacitance was 42.4 F · g−1 at a scan rate of 0.01 V/s. The composite was also an excellent superhydrophobic material, and its contact angle was up to 168.5°, which can facilitate the practical application of sensor materials and electrode materials. Polypyrrole hollow tube/cross-leaf ZIF-L composite had advantages of a simple process, thin thickness, light weight, and low price, and can be widely used in the field of smart wearables.

Electrolytic active water was prepared by electrolytic treatment of sodium chloride aqueous solution using a self-made device. In order to investigate the properties of electrolytic active water , the pH value, available chlorine contents and decolorization ability on methylene blue solution were tested and compared with that of sodium hypochlorite aqueous solution. Experimental results showed that the electrolytic active water has higher oxidation capacity and decolorization efficiency for methylene blue compared with sodium hypochlorite solution, implying maybe other reactive oxygen species components besides hypochlorite existed in the electrolytic active water. The electrolytic active water was stable under long-term storage, and mechanical force with oxidation activity did not decrease. In addition, the decolorization kinetics of methylene blue in the electrolytic active water was explored, and it was found that, in general, the decolorization reaction of methylene blue in electrolytic active water was consistent with the first-order reaction kinetics model, but when the pH value of electrolytic active water solution was weakly alkaline or neutral, the decolorization reaction of methylene blue was closer to the second-order reaction kinetics model.

Herein, the application of a low-cost, multifunctional polypyrrole hollow tube composite was investigated in the building field. Firstly, polydopamine was made on the surface of a polyethylene geotextile that was the base material. Secondly, the complexes of FeCl 3 and methyl orange as soft templates were used in the in situ polymerization method to prepare a multifunctional polypyrrole hollow tube/polyethylene geotextile composite. Polydopamine played a role in improving the adhesion between the polyethylene fibers and polypyrrole hollow tubes. Finally, the electromagnetic shielding, sound absorption, piezoresistive sensing, and electrochemical properties of the composite were tested. The results showed that the shielding effectiveness value of the composite was higher than 32.7 dB in the X-band, which can shield at least 99% of electromagnetic waves. The average sound absorption coefficient value of the composite was 0.36, which was doubled compared with the polyethylene geotextile, and the peak sound absorption coefficient value reached 0.85 at the frequency of 5437 Hz. The composite could respond to deformation in the range of 4.9–99.9 kPa, and the highest sensitivity was 2.01 kPa −1 at a pressure of 14.2 kPa. The composite had an electrochemical response to KOH solutions, and the specific capacitance was 78.78 F/g at a sweep speed of 0.01 V/s. The composite had the advantages of simple preparation, low cost, and a wide range of use, and had broad application prospects in solving the increasingly severe electromagnetic interference, noise pollution, building structure nondestructive monitoring, and energy storage.

磁控溅射技术制备的薄膜膜层均匀,内部无气孔,密度高,与衬底的附着性良好,薄膜质量高,被广泛应用于科学研究和工业生产中,且适合应用计算机模拟来研究溅射过程和溅射结果,这样既可以检验模拟的准确性,又可以对实验现象的内在意义进行挖掘,为后续实验提供参考信息.在介绍磁控溅射薄膜生长常用模拟方法原理的基础上,详细讨论了第一性原理(First-principles calculations)、分子动力学(Molecular dynamics,MD)和蒙特卡洛(Monte Carlo,MC)等3种方法的适用条件和模拟结果,从3种方法适合解决的问题、相互之间的区别等方面,对国内外最新的研究进展进行总结与分析.发现3种方法在精确度和计算量上依次递减,在可模拟的时间和空间尺度上依次递增,在模拟对象上,第一性原理方法由于其高度的精确性被广泛应用于对薄膜本身的性质或对粒子间的运动等方面,且模拟结果可以是具体数值,从而对实验进行更加精确的预测和指导,分子动力学方法多用于模拟薄膜生长过程和原子间行为等方面,蒙特卡洛方法相较于前两者,用途更加广泛,可模拟的对象除了薄膜本身,也可以对电磁场等进行模拟.最后,对磁控溅射薄膜生长模拟未来的研究方向进行了展望.

Because the surface of rabbit hair fabric is covered by scales, it has the disadvantages of wearing itchy feeling, washing felt shrinkage and low temperature dyeing difficulty. If the scales of rabbit hair fiber can not be properly peeled off, it will greatly affect the appearance and feel of rabbit hair fabric and wearing experience. This study uses the electrochemical principle, using Na Cl aqueous solution as medium in the electric field, to modify the surface of rabbit hair fiber and fabric. By means of surface morphology observation, felting properties investigation and the determination of breaking strength, friction coefficient and the dyeing performance tests of rabbit hair fibers with or without electrochemical treatment, the electrochemical mechanism of rabbit hair and modification effect are investigated. The results showed that the disulfide bond of cystine in scaly layer was oxidized and the dense structure of scaly layer was destroyed, leading to an improving dyeing properties as well as anti-felting ability.

为改善羊毛纤维的特性,通过单独使用电解活性水和电解活性水联合生物酶两种处理方法,验证电解活性水改善羊毛防缩性能的可行性.并通过DFE、缩球直径及密度等指标作为评判羊毛防缩效果的依据,结合电镜图观察羊毛纤维表面鳞片结构的变化和处理的均匀性.结果表明,在不同程度上,活性水及活性水联合生物酶的处理方法能快速有效地实现羊毛纤维鳞片层的剥除,从而改善羊毛纤维的防缩性能.

为探究磁控溅射技术在超疏水纺织品中的应用,通过预处理、碱减量处理、短时间射频溅射TiO2、疏水整理工艺,制备了超疏水涤纶(PET)织物,并对其性能进行测试.结果表明,制得超疏水PET织物表面存在相对均匀的微纳两级粗糙结构,经过疏水整理工艺后具备超疏水性能;超疏水PET织物表面各元素及含量约为:C(83％)、O(13％)、F(1％)、Ti(3％),并且C、O、F元素沿PET单丝分布,Ti元素均匀分布;超疏水PET织物静态接触角为153.30°,滚动角为9.00°,与疏水整理工艺相比,静态接触角提升接近15.00°;超疏水PET织物的力学性能有一定的保留,并且耐洗性能良好.

光与微观结构相互作用产生的颜色被称为结构色.结构色具有高亮度、高饱和度、永不褪色等诸多优点,在纺织领域有很大的发展前景.从薄膜干涉、衍射、散射和光子晶体等不同途径介绍了结构色产生的原理,以及纺织领域用结构色的制备技术和特点,指出目前所制备的结构色存在的角度依赖性、不稳定性、牢度评价和颜色重现性等制约了结构色在纺织领域的应用,但是通过努力,并且伴随着这些问题的逐渐解决,结构色终将会给纺织染整行业带来全新的变化.

In order to facilitate the design of a hybrid filament before spinning, a k-m (Kubelka-Munk) iteration model was proposed, which was based on the calculation method for reflectance of a translucent object and needed to be used in conjunction with a fabric model that can reflect the arrangement order of monofilaments. Therefore, the model can not only calculate the color of each point on the fabric surface, but also the mixed color of the fabric. Twenty fabrics with five different blending ratios of black monofilaments and white monofilaments, four multifilament fineness and three fabric weave types were woven. The relationship between the gray distribution of all points on the fabric surface captured by the camera in a DigiEye colorimeter and calculated by the k-m iteration model was analyzed, and the color difference between the mixed color of the fabric tested by the Datacolor spectrophotometer and that calculated by the k-m iteration model was calculated. The results show that the intersection distance and Pearson correlation coefficient between the gray histogram of the photographed fabric image and that of the calculated fabric image were 0.79 and 0.89, respectively. The average color difference obtained by the k-m iteration model was 0.92 Color Measurement Committee (2:1) units, which was best compared with the calculation results of other models. By discussing the fabric structure parameters causing the lightness difference, it was concluded that the calculated lightness was smaller than the measured lightness difference for fabric with a longer float length, smaller multifilament fineness and a larger black monofilament blending ratio.

通过对柔性温度传感器常用的热敏材料、基底材料以及加工方法等进行分析总结,指出柔性温度传感器使用的热敏材料主要包括金属材料、碳基材料和聚合物材料,其中金属Ni相对于其他金属而言,具有较高的电阻温度系数和稳定的线性度;而聚酰亚胺由于其优异的耐热性和绝缘性成为使用最多的基底材料:MEMS技术结合磁控溅射技术和喷墨打印技术两种方法制作出的电阻薄膜具有较好的线性度和稳定性,成为制作电阻薄膜的主要方法.柔性温度传感器在纺织领域的应用主要是以纺织材料为基底和将传感器与织物集成一体这两种方式,而增加热敏电阻单元的抗弯折性能,提高测量的精准度和灵敏度是以织物为基底的柔性温度传感器迫切需要解决的问题.

文章针对深色含特种毛纤维织物在显微镜下无法准确区分纤维形态特征,会造成定性定量分析不准确的问题,研发了2种还原性毛用褪色剂进行褪色,并与市售剥色剂的褪色效果对比,发现研制的褪色剂C对纤维损伤较小,褪色效果佳,操作简单.

为了更好地了解人工智能技术在麻棉纤维定性定量鉴别过程中的应用研究情况.总结了近年来麻棉纤维的鉴定过程,包括纤维图像采集、纤维图像预处理、特征值提取和纤维识别技术,尤其是基于人工智能技术的麻棉鉴定自动检测技术在检测领域的研究进展.人工智能技术为实现麻棉纤检鉴定的自动化提供了实际参考.最后提出人工智能技术目前存在的不足,认为人工智能技术要加大对图像识别领域的研究.

TPU-coated polyester fabric was used as the substrate of a flexible temperature sensor and Ag nanoparticles were deposited on its surface as the temperature sensing layer by the magnetron sputtering method. The effects of sputtering powers and heat treatment on properties of the sensing layers, such as the temperature coefficient of resistance (TCR), linearity, hysteresis, drift, reliability, and bending resistance, were mainly studied. The results showed that the TCR (0.00234 °C−1) was the highest when sputtering power was 90 W and sputtering pressure was 0.8 Pa. The crystallinity of Ag particles would improve, as the TCR was improved to 0.00262 °C−1 under heat treatment condition at 160°. The Ag layer obtained excellent linearity, lower hysteresis and drift value, as well as good reliability and bending resistance when the sputtering power was 90 W. The flexible temperature sensor based on the coated polyester fabric improved the softness and comfortableness of sensor, which can be further applied in intelligent wearable products.

文中通过对电磁屏蔽原理的介绍,对电磁屏蔽织物的制备方法进行了总结,其中包括涂层法、金属混纺法、化学镀法和磁控溅射法,并对利用上述4种方法所制备的电磁屏蔽织物的研究现状进行了分析.最后对电磁屏蔽织物在纺织领域中存在的问题和发展趋势进行了总结和展望,指出利用磁控溅射技术制备电磁屏蔽织物是未来研究的方向和重点.

Hemp fiber has excellent performance as a renewable fiber material. It is of great significance to study more efficient, energy-saving and environmentally friendly pre-treatment methods of hemp textiles. A novel hemp fiber scouring method based on electrochemical techniques was presented in the research. A type of water solution with high and stable oxidative potential due to in-situ- generated oxidative components was produced by the proposed electrical method, and was applied to scouring treatment of raw hemp fibers by just soaking the fibers in it. Hemp fibers with different treatments were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction, and residual gum rate, lignin content and whiteness of the samples were tested. Experiment results show that electrochemical scouring has a similar degumming effect of hemp fibers compared with traditional chemical scouring, with a better lignin removal and whiteness, implying an effective and short process method of hemp textiles pre-treatment with low impact on the environment.

通过在复合酶体系中加入木聚糖酶,探讨木聚糖酶对棉针织物生物酶连续快速精练工艺精练效果的影响,并改变浸轧次数对工艺进行优化.结果 表明,与传统精练工艺相比,在复配酶中加入木聚糖酶,能在改变织物润湿性的同时降解棉籽壳,最优木聚糖酶浓度为0.6 g/L;棉针织物酶精练的最优浸轧次数为10次;加入木聚糖酶后,生物酶精练工艺在毛效方面较好,但在白度和强力方面还存在不足.