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Understanding the interaction between nanoparticles and the matrix and the properties of interphase is crucial to predict the macroscopic properties of a nanocomposite system. Here, we investigate the interaction between boehmite nanoparticles (BNPs) and epoxy using different atomic force microscopy (AFM) approaches. We demonstrate benefits of using multifrequency intermodulation AFM (ImAFM) to obtain information about conservative, dissipative and van der Waals tip-surface forces and probing local properties of nanoparticles, matrix and the interphase.
We utilize scanning kelvin probe microscopy (SKPM) to probe surface potential as a tool to visualize material contrast with a physical parameter, which is independent from the mechanics of the surface. Combining the information from ImAFM stiffness and SKPM surface potential results in a precise characterization of interfacial region, demonstrating that the interphase is softer than epoxy and boehmite nanoparticles. Further, we investigated the effect of boehmite nanoparticles on the bulk properties of epoxy matrix. ImAFM stiffness maps revealed the significant stiffening effect of boehmite nanoparticles on anhydride-cured epoxy matrix. The energy Dissipation of epoxy Matrix locally measured by ImAFM shows a considerable increase compared to that of neat epoxy. These measurements suggest a substantial alteration of epoxy structure induced by the presence of boehmite.
We show that complex physical and chemical interactions between boehmite nanoparticles and epoxy drastically affect matrix properties, which in the future will provide tuning of material properties for further optimization in applications from automotive to aerospace. We utilize intermodulation atomic force microscopy (ImAFM) for probing local stiffness of both particles and polymer matrix. Stiff particles are expected to increase total stiffness of nanocomposites and the stiffness of polymer should remain unchanged. However, ImAFM revealed that stiffness of matrix in epoxy/boehmite nanocomposite is significantly higher than unfilled epoxy. The stiffening effect of the boehmite on epoxy also depends on the particle concentration. To understand the mechanism behind property alteration induced by boehmite nanoparticles, network architecture is investigated using dynamic mechanical thermal analysis (DMTA). It was revealed that although with 15 wt% boehmite nanoparticles the modulus at glassy state increases, crosslinking density of epoxy for this composition is drastically low.
The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities.
Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations.
In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence.
Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems.
Understanding the interaction between boehmite and epoxy and the formation of their interphases with different mechanical and chemical structures is crucial to predict and optimize the properties of epoxy-boehmite nanocomposites. Probing the interfacial properties with atomic force microscopy (AFM)-based methods, especially particle-matrix long-range interactions, is challenging. This is due to size limitations of various analytical methods in resolving nanoparticles and their interphases, the overlap of interphases, and the effect of buried particles that prevent the accurate interphase property measurement. Here, we develop a layered model system in which the epoxy is cured in contact with a thin layer of hydrothermally synthesized boehmite. Different microscopy methods are employed to evaluate the interfacial properties. With intermodulation atomic force microscopy (ImAFM) and amplitude dependence force spectroscopy (ADFS), which contain information about stiffness, electrostatic, and van der Waals forces, a soft interphase was detected between the epoxy and boehmite. Surface potential maps obtained by scanning Kelvin probe microscopy (SKPM) revealed another interphase about one order of magnitude larger than the mechanical interphase. The AFM-infrared spectroscopy (AFM-IR) technique reveals that the soft interphase consists of unreacted curing agent. The long-range electrical interphase is attributed to the chemical alteration of the bulk epoxy and the formation of new absorption bands.