Accelerated Discovery of the Polymer Blends for Cartilage Repair through Data-Mining Tools and Machine-Learning Algorithm.

In designing successful cartilage substitutes, the selection of scaffold materials plays a central role, among several other important factors. In an empirical approach, the selection of the most appropriate polymer(s) for cartilage repair is an expensive and time-consuming affair, as traditionally it requires numerous trials. Moreover, it is humanly impossible to go through the huge library of literature available on the potential polymer(s) and to correlate the physical, mechanical, and biological properties that might be suitable for cartilage tissue engineering. Hence, the objective of this study is to implement an inverse design approach to predict the best polymer(s)/blend(s) for cartilage repair by using a machine-learning algorithm (i.e., multinomial logistic regression (MNLR)). Initially, a systematic bibliometric analysis on cartilage repair has been performed by using the bibliometrix package in the R program. Then, the database was created by extracting the mechanical properties of the most frequently used polymers/blends from the PoLyInfo library by using data-mining tools. Then, an MNLR algorithm was run by using the mechanical properties of the polymers, which are similar to the cartilages, as the input and the polymer(s)/blends as the predicted output. The MNLR algorithm used in this study predicts polyethylene/polyethylene-graftpoly(maleic anhydride) blend as the best candidate for cartilage repair.

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites.

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young's modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

On the Injection Molding Processing Parameters of HDPE-TiO2 Nanocomposites.

In recent years, the development and use of polymeric nanocomposites in creating advanced materials has expanded exponentially. A substantial amount of research has been done in order to design polymeric nanocomposites in a safe and efficient manner. In the present study, the impact of processing parameters, such as, barrel temperature, and residence time on the mechanical and thermal properties of high density polyethylene (HDPE)-TiO2 nanocomposites were investigated. Additionally, scanning electron microscopy and X-ray diffraction spectroscopy were used to analyze the dispersion, location, and phase morphology of TiO2 on the HDPE matrix. Mechanical tests revealed that tensile strength of the fabricated HDPE-TiO2 nanocomposites ranged between 22.53 and 26.30 MPa, while the Young's modulus showed a consistent increase as the barrel temperature increased from 150 °C to 300 °C. Moreover, the thermal stability decreased as the barrel temperature increased.

Polymeric nanobiocomposites for biomedical applications.

Polymeric nanobiocomposites have recently become one of the most essential sought after materials for biomedical applications ranging from implants to the creation of gels. Their unique mechanical and biological properties provide them the ability to pass through the highly guarded defense mechanism without undergoing noticeable degradation and initiation of immune responses, which in turn makes them advantageous over the other alternatives. Aligned with the advances in tissue engineering, it is also possible to design three-dimensional extracellular matrix using these polymeric nanobiocomposites that could closely mimic the human tissues. In fact, unique polymer chemistry coupled with nanoparticles could create unique microenvironment that promotes cell growth and differentiation. In addition, the nanobiocomposites can also be devised to carry drugs efficiently to the target site without exhibiting any cytotoxicity as well as to eradicate surgical infections. In this article, an effort has been made to thoroughly review a number of different types/classes of polymeric nanocomposites currently used in biomedical fields. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 1241-1259, 2017.