Polyrotaxanes (PRs), a new class of supramolecular polymers, have recently attracted considerable attention in materials science because of their unique structure and intriguing effects on material properties. Here, we report that a PR is capable of toughening a rigid epoxy adhesive without phase separation morphology, unlike the interfacemediated toughening mechanisms established in conventional epoxy resins. A PR bearing polycaprolactone graft chains on wheel-like molecules was dispersed homogeneously in an epoxy adhesive via intermolecular hydrogen bonding. The PRincorporating epoxy adhesive exhibited simultaneous increase in adhesive strength, fracture displacement, and fracture toughness while retaining its high glass transition temperature and tensile modulus. Morphological, thermal, and mechanical characterizations suggested that the toughening mechanism originates from the PR supramolecular structure, allowing the wheel-like molecules to rotate around and slide along the polymer main chain. The study revealed the fracture behavior of PRcontaining epoxy adhesives, which may be beneficial for practical applications of network polymers.
Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples) limits the development of chemical intuition from experimentalists, as it constrains the use of machine-learning algorithms for extracting relevant information. We tackle this issue to predict and optimize adhesive materials by combining laboratory experimental design, an active learning pipeline and Bayesian optimization. We start from an initial dataset of 32 adhesive samples that were prepared from various molecular-weight bisphenol A-based epoxy resins and polyetheramine curing agents, mixing ratios and curing temperatures, and our data-driven method allows us to propose an optimal preparation of an adhesive material with a very high adhesive joint strength measured at 35.8 ± 1.1 MPa after three active learning cycles (five proposed preparations per cycle). A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient of determination, root mean square error and mean absolute error of 0.85, 4.0 MPa and 3.0 MPa, respectively. This study demonstrates the important impact of active learning to accelerate the design and development of tailored highly functional materials from very small datasets.
Epoxy structural adhesives have been used extensively in the automotive and aerospace industries to bond assembly parts. Much effort has been devoted to enhancing the mechanical properties of epoxy resin by incorporating fillers. Among a variety of fillers investigated for this purpose, nanocelluloses are regarded as a promising type of emerging green filler material because of their excellent mechanical and physicochemical properties. Indeed, nanocelluloses have been used as a filler for polymer nanocomposites. However, the toughening and reinforcing effects of nanocelluloses on rigid epoxy adhesives have not yet been fully revealed, particularly from the perspective of adhesive bonded joints. Here we report that epoxy adhesive containing cellulose nanocrystal (CNC) aggregates produced using a solvent-free ball milling method achieves drastically improved adhesive strength and fracture toughness compared with that of the reference adhesive without CNCs. The epoxy adhesive containing CNC aggregates exhibited an excellent adhesive strength of 29 MPa and a fracture toughness of 389 J/m2, which were 125% and 378% greater than those of epoxy adhesive without CNCs, respectively.
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