Plastics can be made scratch-proof and flame-proof, or given antibacterial properties through the addition of nano-sized additives mixed in with the pellets of plastic during the manufacturing process. However, the particle distribution within the plastic compound must be absolutely precise. A new device being developed by a group of scientists at the Fraunhofer Institute for Chemical Technology (ICT) in Pfinztal, Germany say that they are able to test the distribution in real time.

Plastic parts can be modified to have all sorts of different properties, which vary according to the type and shape of the particles of additive and their distribution within the polymer compound. Even though these particles are less than 100 nanometers across, it takes just a tiny quantity to make the plastic antibacterial, scratch-proof, and flame retardant, electrically or thermally conductive, or to give it greater mechanical rigidity. For these properties to function as intended, the ratio of pellets to additives and the overall particle distribution has to be exactly right. Until now, checking this has always been a very complicated and time-consuming process that had to be carried out after the material is made. Manufacturers are often only able to achieve the desired plastic formula after mixing several trial batches, which slows down the production process and wastes material.

Researchers at the ICT are developing a tool to characterize polymer nanocomposites during the ongoing production process itself. They say that this is not only more cost-effective in terms of material and time, it also helps them to improve the quality of the properties that the added nanoparticles bring to the polymer.

Their device, the onBOX is mounted to the exit nozzle of the conveyor, where its sensors analyze and characterize the polymer compound while it is still in the mixing plant. The sensors use a combination of technologies including spectroscopy, ultrasound, and microwaves to test the composition of the polymer-nanoparticle compound. They measure its viscosity, pressure, and particle distribution, including any possible fluctuations in concentration, while simultaneously measuring the compound’s temperature and its thermal and electrical conductivity. A computer then compares this data to the system’s command variables and processes it inside an artificial neural network.

The computer determines the precise mixing ratios needed to achieve the intended effect as well as the manufacturing process this requires, and feeds this information directly to the machine’s control system.

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