Prediction of Acrylonitrile-Butadiene-Styrene Mechanical Properties through Compressed-Sensing Techniques.

One of the challenges in the plastic industry is the cost and time spent on the characterization of different grades of polymers. Compressed sensing is a data reconstruction method that combines linear algebra with optimization schemes to retrieve a signal from a limited set of measurements of that signal. Using a data set of signal examples, a tailored basis can be constructed, allowing for the optimization of the measurements that should be conducted to provide the highest and most robust signal reconstruction accuracy. In this work, compressed sensing was used to predict the values of numerous properties based on measurements for a small subset of those properties. A data set of 21 fully characterized acrylonitrile-butadiene-styrene samples was used to construct a tailored basis to determine the minimal subset of properties to measure to achieve high reconstruction accuracy for the remaining nonmeasured properties. The analysis showed that using only six measured properties, an average reconstruction error of less than 5% can be achieved. In addition, by increasing the number of measured properties to nine, an average error of less than 3% was achieved. Compressed sensing enables experts in academia and industry to substantially reduce the number of properties that must be measured to fully and accurately characterize plastics, ultimately saving both costs and time. In future work, the method should be expanded to optimize not only individual properties but also entire tests used to simultaneously measure multiple properties. Furthermore, this approach can also be applied to recycled materials, of which the properties are more difficult to predict.

Microplastic Index-How to Predict Microplastics Formation?

The presence of microplastics in environmental compartments is generally recognized as a (potential) health risk. Many papers have been published on the abundance of microplastics at various locations around the globe, but only limited knowledge is available on possible mitigation routes. One of the mitigation routes is based on the choice of plastic materials used for products that may unintentionally end up in the environment. As a first approach, this paper presents a method to calculate the tendency of polymers to form microplastics, based on their mechanical and physical properties. A MicroPlastic Index (MPI) that correlates the microplastic formation to polymer properties is defined for both impact and wear of polymers via a theoretical particle size and the energy required to form these particles. A first comparison between calculated and experimental particle size is included. The MPI for impact and wear follow the same trend. Finally, these MPIs are correlated to the respective abundance of the microplastics in the environment, corrected for global production of the corresponding polymers: the higher the MPI, the more microplastics are found in the environment. Thus, the MPI can be used as a basis for choice or redesign of polymers to reduce microplastic formation.