Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data

Manufacturing polypropylene (PP) composites to meet customers’ needs is difficult, time-consuming, and costly, owing to the ever-increasing diversity and complexity of the corresponding specifications and the trial-and-error method currently used to satisfy the required physical properties. To address this issue, we developed three models for predicting the physical properties of PP composites using three machine learning (ML) methods: multiple linear regression (MLR), deep neural network (DNN), and random forest (RF). Further, the industrial data of 811 recipes were acquired to verify the developed models. Data categorization was performed to account for the differences between data and the fact that different recipes require different materials. The three models were then deployed to predict the flexural strength (FS), melting index (MI), and tensile strength (TS) of the PP composites in nine case studies. The predictive performance results differed according to the physical properties of the composites. The FS and MI prediction models with MLR exhibited the highest R2 values of 0.9291 and 0.9406. The TS model with DNN exhibited the highest R2 value of 0.9587. The proposed models and study findings are useful for predicting the physical properties of PP composites for recipes and the development of new recipes with specific physical properties

Enhancement of mass transport in fuel cells using three-dimensional graphene foam as flow field

Graphene foam is a three-dimensional graphene-based material with interconnected macropores and it combines the advantage of graphene and structural characteristics of metal foam. Various kinds of metal foam have been developed as flow fields because their high porosity distributes reactants in an entire area and removes generated water. However, metal foam is highly susceptible to corrosion under the operating conditions of polymer-electrolyte-membrane fuel cells. In this work, we proposed using graphene foam as a flow field to investigate its effect on enhancing mass transport of reactants and products. Single-cell tests of the graphene-foam flow field showed the enhancement of mass transport, which led to increased performance at high current densities. Measurements of the oxygen gain (i.e., the difference in voltage under O2 and air atmospheres), electrochemical impedance spectra, and simulation results also revealed that the graphene-foam membrane-electrode assembly (MEA) exhibited lower mass-transport resistance than a conventional MEA because graphene foam is advantageous for the mass transport of reactants and water. © 2018 Elsevier L

One-Pot Transformation of Technical Lignins into Humic-Like Plant Stimulants through Fenton-Based Advanced Oxidation: Accelerating Natural Fungus-Driven Humification

Commercial humic acids mainly obtained from leonardite are in increasing demand in agronomy, and their market size is growing rapidly because these materials act as soil conditioners and direct stimulators of plant growth and development. In nature, fungus-driven nonspecific oxidations are believed to be a key to catabolizing recalcitrant plant lignins, resulting in lignin humification. Here we demonstrated the effective transformation of technical lignins derived from the Kraft processing of woody biomass into humic-like plant fertilizers through one-pot Fenton oxidations (i.e., artificially accelerated fungus reactions). The lignin variants resulting from the Fenton reaction, and manufactured using a few different ratios of FeSO₄ to H₂O₂, successfully accelerated the germination of Arabidopsis thaliana seeds and increased the tolerance of this plant to NaCl-induced abiotic stress; moreover, the extent of the stimulation of the growth of this plant by these manufactured lignin variants was comparable or superior to that induced by commercial humic acids. The results of high-resolution (15 T) Fourier transform-ion cyclotron resonance mass spectrometry, electrostatic force microscopy, Fourier transform-infrared spectroscopy, and elemental analyses strongly indicated that oxygen-based functional groups were incorporated into the lignins. Moreover, analyses of the total phenolic contents of the lignins and their sedimentation kinetics in water media together with scanning electron microscopy- and Brunauer–Emmett–Teller-based surface characterizations further suggested that polymer fragmentation followed by modification of the phenolic groups on the lignin surfaces was crucial for the humic-like activity of the lignins. A high similarity between the lignin variants and commercial humic acids also resulted from autonomous deposition of iron species into lignin particles during the Fenton oxidation, although their short-term effects of plant stimulations were maintained whether the iron species were present or absent. Finally, we showed that lignins produced from an industrial-scale acid-induced hydrolysis of wood chips were transformed with the similar enhancements of the plant effects, indicating that our fungus-mimicking processes could be a universal way for achieving effective lignin humification