When developing new products, brand designers must analyse related products, which is a complicated and time-consuming process. Modern product design often requires complex engineering processes; product development requires extensive knowledge but there is also a demand for shorter product design cycles. Therefore, we propose a method based on extension theory and the analytic hierarchy process for identifying product-related knowledge, to aid the development of new products. First, based on our understanding of extenics, matter-element and relational meta-models of product form, function, and structure are established. Then, we define different primitives of brand identity. Finally, using an "extensional analytic hierarchy process" (EAHP), a hierarchy is established and the weights of different primitives are calculated. Various combinations of primitives are used to facilitate knowledge transfer for computer-aided intelligent design. Design data for multiple cases are analysed to verify the feasibility and effectiveness of the method. The method was verified in a physiological signal experiment, and the results show that the method can effectively accumulate product knowledge. Rapid data mining is important for market competitiveness.

As one of the most important visual characteristics in a product system, color can arouse the user's emotional demands quickly. Due to the complexity of the user's emotional needs mining process, it can be expressed by color image adjectives. Meanwhile, product color trends that meet the user's emotional demands may help decision makers to anticipate a new market positioning and reduce the blindness in the product color design. In this study, the Gray theory combined with the Kansei engineering was used to mine the macro and microscopic factors in product color design decision process based on the product color brand image. The results showed that the constructed method could be used to guide the product color design that was to meet the user's emotional needs comprehensively and quickly. The method solved the problems that exist in the current product color trend prediction research and improved the accuracy of the correlation mining between product color design elements and brand image. Lastly, the color design of the mid-range sedan was taken as an example to prove the feasibility of the research approach. This is a new attempt to guide product color design decisions in two aspects, one is known from the results of product color trends quantitative prediction, and the other one comes from the correlation calculation between product color design elements and brand image.

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Metal nanoparticles (NPs) have been commonly introduced onto flexible platforms for improving their exploitation. However, such an introducing process of NPs is hard to achieve, and additional dispersants and high-energy consumption are largely required in existing studies. In this study, a one-pot method was developed to synthesize Au NPs in a cellulose dope. The dissolved cellulose chains acted as a green reductant as well as a stabilizer. As the polysaccharide dope coagulated, a three-dimensional (3D) regenerated cellulose nanocomposite decorated with 0.29−1.07 wt % Au NPs was successfully synthesized. As supported by the "hot spot" effect among the Au NPs embedded in the 3D structure, the nanocomposite could act as a sensitive surface-enhanced Raman scattering (SERS) substrate. Accordingly, this study achieved an enhancement factor of 2.8 × 10 7 and a limit of detection of 10 −9 M when R6G was employed as a probe molecule. Moreover, as impacted by the porous structure of the SERS substrate, 2.5 mg/kg melamine in milk could be directly detected. Furthermore, organic contaminants were catalytically reduced, and the process of catalytic reduction underwent in situ SERS monitoring with excellent sensitivity.