As a new class of high-strength hydrogels, we designed a tetra-PEG gel by combining two symmetrical tetrahedron-like macromonomers of the same size. Because the nanostructural unit of the gel network was defined by the length of the tetrahedral PEG arm, the gel had a homogeneous structure and resultant high mechanical strength comparable to that of native articular cartilage. Furthermore, since the gel was formed by mixing two biocompatible macromonomer solutions, the gelation reaction itself and the resultant gel were also biocompatible. The breaking strength had local maxima at the overlap concentration of the macromonomers (C*) and at 2C*. Dynamic light scattering measurement indicated the near absence of inhomogeneities in the network at C*. Thus, we successfully designed and fabricated a high-strength hydrogel by controlling the homogeneity of network structure for the first time, which will lead to multiplied effects, i.e., contributing to the understanding of ideal networks, providing a universal strategy for designing high-strength gels, and opening up the biomedical application of hydrogels.

The structure of Tetra-PEG gel, a new class of biocompatible, easy-made, and high-strength hydrogel consisting of a four-arm polyethylene glycol (PEG) network, has been investigated by means of small-angle neutron scattering (SANS). Since the Tetra-PEG gel is prepared by cross-end-coupling two kinds of four-arm PEG macromers having different functional groups at the ends, i.e., amine group and succinimidyl ester group respectively, the coupling reaction occurs exclusively between PEG chains carrying different functional groups. SANS results showed that the four-arm PEG macromer aqueous solutions and Tetra-PEG gels were successfully described by the theoretical scattering function for multiarm Gaussian chains and the Ornstein-Zernike function, respectively. Surprisingly, no noticeable excess scattering that originated from cross-linking was observed in Tetra-PEG gels, suggesting that its network structure is extremely uniform. Investigations on nonstoichiometric Tetra-PEG gels showed weakening of the mechanical properties as well as an increase of dangling chains (defects) in the network. It is concluded that Tetra-PEG gels have an extremely uniform network structure, probably mimicking a diamond-like structure, and this is one of the reasons for the advanced mechanical properties of Tetra-PEG gels.

A series of model networks consisting of polyethylene glycol (PEG), tetra-PEG gels, have been prepared and their structure and dynamics have been investigated by small-angle neutron scattering (SANS) and static light scattering (SLS). The Tetra-PEG gels were prepared by cross-end coupling of two types of tetra-arm PEG macromers with molecular weights, M w , of (5 to 40) Â 10 3 g/mol. In the SANS regime, the structure factors of both as-prepared and swollen gels can be represented by Ornstein-Zernike-type scattering functions and superimposed to single master curves with the reduced variables, ξq and I(q)/φ 0 ξ 2 , irrespective of the molecular weight of tetra-PEG, where q, ξ, I(q), and φ 0 are the magnitude of the scattering vector, the correlation length, the scattering intensity, and the polymer volume fraction at preparation, respectively. In the SLS regime, however, a power-law-type upturn was observed, indicating the presence of PEG chain clusters. Interestingly, these inhomogeneities disappear by swelling. It is concluded that Tetra-PEG gels can be an "ideal polymer network" with a self-similar structure with respect to M w without significant entanglements and/or defects. This explains why Tetra-PEG gels have high mechanical strength as reported elsewhere (Macromolecules 2008, 41, 5379).

Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.