Encapsulation of Carbon Nanotubes by Styrene and Butyl Acrylate Particles via Suspension Polymerization for Polymerized Toner Applications

Electrophotographic printing and copying processes primarily use toner, which is a mixture of colorant, polymer, and additives. Toner can be made using traditional mechanical milling techniques or more contemporary chemical polymerization techniques. Suspension polymerization provides spherical particles with less stabilizer adsorption, homogeneous monomers, higher purity, and easier control of the reaction temperature. In contrast to these advantages, however, the particle size resulting from suspension polymerization is too large for toner. To overcome this disadvantage, devices such as high-speed stirrers and homogenizers can be used to reduce the size of the droplets. This research investigated the use of carbon nanotubes (CNTs) instead of carbon black as the pigment in toner development. We succeeded in achieving a good dispersion of four different types of CNT, specifically modified with NH₂ and Boron or unmodified with long or short chains in water rather than chloroform, using sodium n-dodecyl sulfate as a stabilizer. We then performed polymerization of the monomers styrene and butyl acrylate in the presence of the different CNT types and found that the best monomer conversion and largest particles (in the micron range) occurred with CNTs modified with boron. The insertion of a charge control agent into the polymerized particles was achieved. Monomer conversion of over 90% was realized with all concentrations of MEP-51, whereas conversion was under 70% with all concentrations of MEC-88. Furthermore, analysis with dynamic light scattering and scanning electron microscopy (SEM) indicated that all polymerized particles were in the micron size range, suggesting that our newly developed toner particles were less harmful and environmentally friendly products than those typically and commercially available. The SEM micrographs clearly showed good dispersion and attachment of the CNTs on the polymerized particles (no CNT aggregation was found), which has never been published before.

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.

Identification and visualisation of microplastics / nanoplastics by Raman imaging (iii): algorithm to cross-check multi-images

We recently developed the Raman mapping image to visualise and identify microplastics / nanoplastics (Fang et al. 2020, Sobhani et al. 2020). However, when the Raman signal is low and weak, the mapping uncertainty from the individual Raman peak intensity increases and may lead to images with false positive or negative features. For real samples, even the Raman signal is high, a low signal-noise ratio still occurs and leads to the mapping uncertainty due to the high spectrum background when: the target plastic is dispersed within another material with interfering Raman peaks; materials are present that exhibit broad Raman peaks; or, materials are present that fluoresce when exposed to the excitation laser. In this study, in order to increase the mapping certainty, we advance the algorithm to combine and merge multi-images that have been simultaneously mapped at the different characteristic peaks from the Raman spectra, akin imaging via different mapping channels simultaneously. These multi-images are merged into one image via algorithms, including colour off-setting to collect signal with a higher ratio of signal-noise, logic-OR to pick up more signal, logic-AND to eliminate noise, and logic-SUBTRACT to remove image background. Specifically, two or more Raman images can act as "parent images", to merge and generate a "daughter image" via a selected algorithm, to a "granddaughter image" via a further selected algorithm, and to an "offspring image" etc. More interestingly, to validate this algorithm approach, we analyse microplastics / nanoplastics that might be generated by a laser printer in our office or home. Depending on the toner and the printer, we might print and generate millions of microplastics and nanoplastics when we print a single A4 document.