NANOMECHANICS OF THE RUBBER–FILLER INTERFACE

In Situ Nanostress Visualization Method to Reveal the Micromechanical Mechanism of Nanocomposites by Atomic Force Microscopy

An in situ atomic force microscopy (AFM) nanomechanical technique was used to directly visualize the micromechanical behaviors of polymer nanocomposites during compressive strain. We obtained a stress distribution image of carbon black (CB)-filled rubber at the nanoscale for the first time, and we traced the microscopic deformation behaviors of CB particles. Through this experiment, we directly revealed the microscopic reinforcement mechanisms of rubber composites. We found that CB-filled rubbers exhibited heterogeneous local microscopic deformations, which were related to the dispersion of CB particles in rubber matrices. The local stress distributions of the rubber composites showed heterogeneity, and the stresses were concentrated in the regions near the CB particles during compression. The area of stress concentration gradually expanded with increasing strain and eventually formed a stress network structure. This stress network bore most of the macroscopic stress and was considered the key reinforcement mechanism of CB-filled rubber. The stress transfer process in the rubber matrix was visualized in real space for the first time. Based on the image data from the AFM experiments, we used finite-element method (FEM) simulations to reproduce the microscopic deformation process of CB-filled rubber. The stress distribution images simulated by FEM showed heterogeneity consistent with AFM. In this study, an in situ visualization of material deformation confirmed the predictions of microscopic deformation behavior from previous theories and models; it also provided new insights into the microscopic reinforcement mechanisms of CB-filled rubber composites based on microscopic stress distribution images.

Cellulose nanocrystals as nucleating agents for the strain induced crystallization in natural rubber

Vulcanized natural rubber (NR)/cellulose nanocrystals (CNC) composites with a CNC content of up to 5 wt% using physical blending and dicumyl peroxide crosslinking were prepared. The tensile properties were investigated at slow and high strain rates. The slow strain rate tests revealed an increase of the elastic modulus concomitant with a decrease of strain at the crystallization onset while increasing the CNC fraction. The high strain rate tests performed near adiabatic conditions demonstrated the ability of the CNC to improve the elastocaloric properties of the NR matrix, with an increase of 30% and 15% of heating and cooling capacities, respectively, in the presence of 3 wt% CNC. Such results were ascribed to (i) a higher thermoelastic effect, due to strain amplification in the NR matrix in the presence of CNC and (ii) a nucleating effect of the CNC on strain induced crystallization. This series of materials can be proposed as a promising eco-friendly alternative to conventional carbon black filled rubber as potential green elastocaloric materials (heating pump, cooling machines).

Direct Visualization of Interfacial Regions between Fillers and Matrix in Rubber Composites Observed by Atomic Force Microscopy-Based Nanomechanics Assisted by Electron Tomography

In order to explain or predict the macroscopic mechanical properties of polymer composites with complex nanostructures, atomic force microscopy (AFM)-based nanomechanics is one of the most appropriate tools because the local mechanical properties can be obtained by it. However, automatic force curve analysis based on contact mechanics would mislead us to the wrong conclusion. The purpose of this study is to elucidate this point by applying AFM nanomechanics on a carbon black (CB)-reinforced isoprene rubber (IR). The CB aggregates underneath the rubber surface prevent us from quantitatively evaluating the ratio of CB and interfacial polymer region (IPR), which is an important parameter to determine the macroscopic mechanical properties. In order to overcome this problem, transmission electron microtomography was incorporated to investigate the 3D structure in the same field of view as AFM nanomechanics. As a result, it was found that there are buried structures that do not appear in the AFM topographic image. In addition, we were able to reveal the existence of a force curve with an inflection point, which is characteristic of such "false" IPRs. To put it another way, we evidenced the existence of true IPRs for the first time by combining these state-of-the-art techniques.

Reinforcement Mechanism of Carbon Black-Filled Rubber Nanocomposite as Revealed by Atomic Force Microscopy Nanomechanics

In this study, atomic force microscopy (AFM) nanomechanics were used to visualize the nanoscale stress distribution in carbon black (CB)-reinforced isoprene rubber (IR) vulcanizates at different elongations and quantitatively evaluate their volume fractions for the first time. The stress concentrations in the protofibrous structure (stress chains) that formed around the CB filler in CB-reinforced IR vulcanizates were directly observed at the nanoscale. The relationship between the local nanoscale stress distribution and macroscopic tensile properties was revealed based on the microscopic stress distribution and microscopic spatial structure. This study can help us gain insight into the microscopic reinforcement mechanism of carbon black-containing rubber composites.

Analysis on Microstructure-Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations

A better understanding of the microstructure–property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common aggregates from a complex filler morphology consisting of hundreds of filler particles. However, a method for extracting a core structure that determines the rubber mechanical properties has not been established yet. In this study, we analyzed complex filler morphologies that generated extremely high stress using two machine learning techniques. First, filler morphology was quantified by persistent homology and then vectorized using persistence image as the input data. After that, a binary classification model involving logistic regression analysis was developed by training a dataset consisting of the vectorized morphology and stress-based class. The filler aggregates contributing to the desired mechanical properties were extracted based on the trained regression coefficients. Second, a convolutional neural network was employed to establish a classification model by training a dataset containing the imaged filler morphology and class. The aggregates strongly contributing to stress generation were extracted by a kernel. The aggregates extracted by both models were compared, and their shapes and distributions producing high stress levels were discussed. Finally, we confirmed the effects of the extracted aggregates on the mechanical property, namely the validity of the proposed method for extracting stress-contributing fillers, by performing coarse-grained molecular dynamics simulations.

ORIGIN OF ENERGETIC ELASTICITY AND ENTROPIC ELASTICITY OF NATURAL RUBBER WITH NANODIAMOND NANOMATRIX STRUCTURE

The origin of energetic elasticity in conjunction with the entropic elasticity for natural rubber with a nanodiamond nanomatrix structure was investigated in terms of bound rubber formed between nanodiamonds, based on the interaction between natural rubber and nanodiamonds inside the nanomatrix. The natural rubber with a nanodiamond nanomatrix structure was prepared by reacting nanodiamonds with deproteinized natural rubber in the presence of tert-butylhydroperoxide/tetraethylenepentamine at 30 °C in the latex stage followed by drying. Morphology of the products was observed by two-dimensional and three-dimensional transmission electron microscopies. The effect of bound rubber on the mechanical properties of the products was investigated by measurements of the dynamic mechanical properties and differential scanning calorimetry. The contribution of bound rubber was estimated by combining the Takayanagi equation and modified Guth–Gold equation. A significant increase in complex modulus was attributed to the effect of the bound rubber.

INFLUENCE OF MASTICATION ON THE MICROSTRUCTURE AND PHYSICAL PROPERTIES OF RUBBER

The effects of the masticated state of isoprene rubber (IR) at the carbon black (CB) addition stage on subsequent mixing, microstructure, and physical properties in the case of a kneader with a characteristic large-diameter shaft are investigated by examining the mastication-time dependence. A sufficiently masticated IR shows a shorter black incorporation time, which results in an improved dispersion of CB and better physical properties. Observing the microstructure of a rubber compound using the atomic force microscope–based nanomechanical technique, poor CB dispersion is revealed for insufficient mastication. Specifically, large CB agglomerations surrounded by the interfacial rubber region with higher elastic modulus than that of a rubber matrix are formed. Such a large CB agglomeration, on the other hand, does not appear in rubber compounds with longer mastication times. The thickness of the interfacial region becomes shorter in these cases. These observations are further discussed by the concept of “rheological unit” introduced by Mooney et al. This study demonstrates that the microstructure of a rubber compound is highly heterogenous with rubber regions of different microscopic elastic moduli and that the microstructure has an influence on CB dispersion and the physical properties of rubber.

Nature of Carbon Black Reinforcement of Rubber: Perspective on the Original Polymer Nanocomposite

Adding carbon black (CB) particles to elastomeric polymers is essential to the successful industrial use of rubber in many applications, and the mechanical reinforcing effect of CB in rubber has been studied for nearly 100 years. Despite these many decades of investigations, the origin of stiffness enhancement of elastomers from incorporating nanometer-scale CB particles is still debated. It is not universally accepted whether the interactions between polymer chains and CB surfaces are purely physical adsorption or whether some polymer-particle chemical bonds are also introduced in the process of mixing and curing the CB-filled rubber compounds. We review key experimental observations of rubber reinforced with CB, including the finding that heat treatment of CB can greatly reduce the filler reinforcement effect in rubber. The details of the particle morphology and surface chemistry are described to give insights into the nature of the CB-elastomer interfaces. This is followed by a discussion of rubber processing effects, the influence of CB on crosslinking, and various chemical modification approaches that have been employed to improve polymer-filler interactions and reinforcement. Finally, we contrast various models that have been proposed for rationalizing the CB reinforcement of elastomers.

Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber

Molecular dynamics (MD) simulation is used to analyze the mechanical properties of polymerized and nanoscale filled rubber. Unfortunately, the computation time for a simulation can require several months’ computing power, because the interactions of thousands of filler particles must be calculated. To alleviate this problem, we introduce a surrogate convolutional neural network model to achieve faster and more accurate predictions. The major difficulty when employing machine-learning-based surrogate models is the shortage of training data, contributing to the huge simulation costs. To derive a highly accurate surrogate model using only a small amount of training data, we increase the number of training instances by dividing the large-scale simulation results into 3D images of middle-scale filler morphologies and corresponding regional stresses. The images include fringe regions to reflect the influence of the filler constituents outside the core regions. The resultant surrogate model provides higher prediction accuracy than that trained only by images of the entire region. Afterwards, we extract the fillers that dominate the mechanical properties using the surrogate model and we confirm their validity using MD.