Yousef Mohammadi

A critical review on the synthesis, characterization, and modeling of polymer grafting is presented. Although the motivation stemmed from grafting synthetic polymers onto lignocellulosic biopolymers, a comprehensive overview is also provided on the chemical grafting, characterization, and processing of grafted materials of different types, including synthetic backbones. Although polymer grafting has been studied for many decades—and so has the modeling of polymer branching and crosslinking for that matter, thereby reaching a good level of understanding in order to describe existing branching/crosslinking systems—polymer grafting has remained behind in modeling efforts. Areas of opportunity for further study are suggested within this review.

A Monte Carlo simulation algorithm is developed to visualize the impact of various initiator feeding policies on the kinetics of free radical polymerization. Three cases are studied: (1) general free radical polymerization using typical rate constants; (2) diffusion-controlled styrene free radical polymerization in a relatively small amount of solvent; and (3) methyl methacrylate free radical polymerization in solution. The number- and weight-average chain lengths, molecular weight distribution (MWD), and polymerization time were computed for each initiator feeding policy. The results show that a higher number of initiator shots throughout polymerization at a fixed amount of initiator significantly increases average molecular weight and broadens MWD. Similar results are also observed when most of the initiator is added at higher conversions. It is demonstrated that one can double the molecular weight of polystyrene and increase its dispersity by 50% through a four-shot instead of a single shot feeding policy. Similar behavior occurs in the case of methyl methacrylate, while the total time drops by about 5%. In addition, policies injecting initiator at high monomer conversions result in a higher unreacted initiator content in the final product. Lastly, simulation conversion-time profiles are in agreement with benchmark literature information for methyl methacrylate, which essentially validates the highly effective and flexible Monte Carlo algorithm developed in this work.

The most common disorders of the musculoskeletal system are low back disorders. They cause significant direct and indirect costs to different societies especially in lifting occupations. To reduce the risk of low back disorders, mechanical lifting aids have been used to decrease low back muscle forces. But there are very few direct ways to calculate muscle forces and examine the effect of personal lift-assist devices, so biomechanical models ought to be used to examine the quality of these devices for assisting back muscles in lifting tasks. The purpose of this study is to examine the effect of a designed wearable lift-assist vest (WLAV) in the reduction of erector spinae muscle forces during symmetric squat lifting tasks. Two techniques of muscle calculation were used, the electromyography-based method and the optimization-based model. The first uses electromyography data of erector spinae muscles and its linear relationship with muscle force to estimate their forces, and the second uses a developed musculoskeletal model to calculate back muscle forces using an optimization-based method. The results show that these techniques reduce the average value of erector spinae muscle forces by 45.38 (+/- 4.80) % and 42.03 (+/- 8.24) % respectively. Also, both methods indicated approximately the same behaviour in changing muscle forces during 10 to 60 degrees of trunk flexion using WLAV. The use of WLAV can help to reduce the activity of low back muscles in lifting tasks by transferring the external load effect to the assistive spring system utilized in it, so this device may help people lift for longer.

In this work, an intelligent approach is applied for the first time in modeling and optimization of in situ polymerized black printing toner composites which enables fine tuning of toner...

Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important ‘inverse’ engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization.

Polymerization data mining is the art of revealing insights and developing new knowledge from huge amounts of data routinely generated in polymerization systems and polymer characterization (polymerization processes and properties of polymer materials are the specific topic of this article). This becomes possible via development and implementation of robust and versatile intelligent data classifiers/clusterers for precise (numerical) processing of any given large theoretical/experimental datasets. Data mining is capable of effectively “cracking” recipe–microstructure–property interrelationships in modern macromolecular reaction engineering. This work offers a perspective, which contains a brief overview of the current state‐of‐the‐art and history of the area, along with current developments and trends in the data mining field (for polymerizations) with several conceptual examples. All in all, and similar to what is happening in other areas, polymerization data mining is becoming a necessity. The first applications seem promising. Applying molecular simulation approaches and artificial intelligence techniques, the design and establishment of powerful simulators for characterization and processing of virtually synthesized macromolecules are open to future developments, being of paramount importance to both industry and academia.