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.

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.

Degradation of a Metal-Polymer Interface Observed by Element-Specific Focused Ion Beam-Scanning Electron Microscopy.

The degradation of a metal-polymer interface was studied in three dimensions using focused ion beam-scanning electron microscopy (FIB-SEM) with energy-dispersive X-ray spectroscopy. A brass-rubber interface, which is important for tires, was examined as an example of a metal-polymer interface. Brass-plated steel cords were embedded in rubber, which was then vulcanized. The brass-rubber interface was treated at 70 °C under 96% humidity for up to 14 days (a wet-heat aging treatment). FIB-SEM provided clear three-dimensional images of the adhesive layer consisting of brass (CuZn), CuxS, and ZnO/ZnS between the steel cords and rubber. During degradation, CuxS at the interfaces diffused into the rubber, resulting in the direct contact of bare steel with rubber. The lack of a substantial adhesive layer explained the degradation of mechanical properties after the wet-heat treatment. In addition, electron diffraction and electron energy loss spectroscopy revealed that the Cu2S crystals in the adhesive layer changed to crystal-like CuS during the degradation, which also caused a degradation of mechanical properties because a high Cu valence of x ≈ 2 in CuxS leads to stronger adhesion than a valence of x = 1.

Angle-Resolved HAXPES Investigation on the Chemical Origin of Adhesion between Natural Rubber and Brass.

Bulk sensitivity of hard X-ray photoelectron spectroscopy (HAXPES) makes this technique suitable for chemical state analysis of bulk and deeply buried interfaces of solid materials. HAXPES is employed in the present study to examine the chemical state of adhesive interfaces between natural rubber and copper-zinc alloy, i.e., brass, while maintaining the adhesion structure in order to understand the chemical mechanism of rubber-to-brass adhesion. Angle-resolved measurements allow to distinguish between chemical species in rubber and those at the adhesive interface. We specially focus on sulfur-containing species because metal sulfides at the interface have been suggested to be crucial for adhesion. Line-shape analysis of S 1s spectra reveals that the interface that exhibits a strong adhesive property is mainly composed of copper sulfides with a predominant amount of CuS. This type of the interfacial chemical state is obtained when a rubber-bonded brass sample is subjected to vulcanization at 170 °C for 10 min. However, prolonged vulcanization leads to a partial dissolution of CuS as well as accumulation of Zn species in the form of ZnO/Zn(OH)2 and ZnS, and as a result, adhesion strength is lowered. The present study paves the way for accurate and detailed discussion on the chemical state of deeply buried interfaces through bulk sensitive in-situ measurements.

Lipid peroxidation dysregulation in ischemic stroke: plasma 4-HNE as a potential biomarker?

4-hydroxynonenal (4-HNE) is a major aldehyde produced during the lipid peroxidation of ω-6 polyunsaturated fatty acids. Recently, 4-HNE has been reported to contribute to the pathogenesis of neuronal diseases such as Alzheimer's disease. However, the role of 4-HNE in ischemic stroke is unclear yet. In this study, we found that plasma 4-HNE concentrations were higher in the genetic stroke-prone rats (stroke-prone spontaneously hypertensive rats) and experimental stroke rats with middle cerebral artery occlusion (MCAO). Moreover, administration of 4-HNE via intravenous injection before MCAO surgery not only enlarged cerebral ischemia-induced infarct area, but also increased oxidative stress in brain tissue, which was evidenced by the enhanced ROS/MPA levels, and the reduced GSH/GSSG ratio and MnSOD levels. Overexpression of aldehyde dehydrogenasesbcl-2 (ALDH2), an enzyme catalyses 4-HNE, rescued neuronal survival against 4-HNE treatment in PC12 cells. The plasma 4-HNE concentrations in patients with ischemic stroke were higher than those in control subjects. In a small sample population (N=60), the plasma 4-HNE concentration was positively correlated with the plasma homocysteine concentration, a risk factor for ischemic stroke. Taken together, our study suggests that the plasma 4-HNE level is a potential biomarker for ischemic stroke.