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Market competition and risk assessment of nanofiber composite materials

Revista Internacional de Contaminación Ambiental 2023 Score: 30 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Zhixiu Yang

Summary

This review examines the market competition and risk assessment landscape for nanocellulose composite materials, which are biodegradable alternatives to conventional plastics for some applications. Replacing plastic with plant-based materials can reduce long-term microplastic accumulation in the environment.

Nano cellulose has the advantages of high degree of polymerization, high strength, degradability, and large specific surface area. The modifier is uniformly dispersed in the matrix material to form a composite system containing nano-scale materials in one phase. With regard to the above-mentioned competitive situation of performance fibers, China should first strengthen the research on engineering scale-up, focusing on finding the factors that affect product quality and dispersion coefficient from engineering issues, and making continuous improvements. Using the analytic hierarchy process method, according to the idea of first decomposition and then synthesis, through pairwise comparison between risk indicators, the risk weights of risk factors at various levels in the risk assessment index system are rigorously calculated and sorted, and the degree of membership and validity are compared. The risk assessment index is judged, the fuzzy clustering algorithm is used to identify the bad data in the market competition data, and the conditional value-at-risk (CVaR) model is constructed to conduct risk assessment. Experimental results show that the actual logarithmic rate of return fluctuates around 0, most of which are higher than the Va R value. Only in extreme cases will it break the Va R curve; the accuracy of risk assessment is close to 100%, and the (Tech-Sector Volatility)TSV indicator begins to decline significantly. That is, the transmission sensitivity of risk data is effectively improved.

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