Materials capable of degradation upon exposure to light hold promise in a diverse range of applications including biomedical devices and smart coatings. Despite the rapid access to macromolecules with diverse compositions and architectures enabled by ring-opening metathesis polymerization (ROMP), a general strategy to introduce facile photodegradability into these polymers is lacking. Here, we report copolymers synthesized via ROMP that can be degraded by cleaving the backbone in both solution and solid states under irradiation with a 52 W, 390 nm Kessil LED to generate heterotelechelic low-molecular-weight fragments. To the best of our knowledge, this work represents the first instance of the incorporation of acylsilanes into a polymer backbone. Mechanistic investigation of the degradation process supports the intermediacy of an α-siloxy carbene, formed via a 1,2-photo Brook rearrangement, which undergoes insertion into water followed by cleavage of the resulting hemiacetal.

In a blend of four or more components, the mixing entropy diversifies the chemical compositions of each phase. This reduces interfacial interactions, enhances inter-phase miscibility, weakens the dependence on specific pair interactions for self-assembly, and removes diffusion barriers to forming large-scale structures. When this design principle is applied to blends containing nanoparticles, colloidal particles, small molecules, and supramolecules, hierarchically-structured composites can be obtained with enhanced formulation flexibility in the filler selection and blend composition. Here, detailed characterization and simulations confirm entropy-driven phase behavior, where each component is distributed to locally mediate unfavorable interactions, and nanostructures form near or in a miscible state. Kinetically, this facilitates molecular diffusion across microdomains and through different phases, and ultimately leads to the facile fabrication of photonic crystals in minutes and nanocomposites with tunable microstructures. Besides advancing capabilities to engineer functional materials, the present study provides molecular insights into how entropy-driven complex blends navigate variations and uncertainties without compromising structural fidelity, as commonly seen in high-entropy alloys and biological blends.

Currently, devices like these are designed and refined via many cycles of trial and error. The nanocomposite design process is particularly arduous because effective medium approximations do not accurately predict the properties of materials built from nanoscopic components. [5,6] In the case of polymer-nanoparticle blends, the spatial arrangement of components determines the overall properties through nanoparticle (NP) coupling, confinement, and organic-inorganic interface effects. [7][8][9] The final NP distribution, achieved via self-assembly, is a complex function of multiple composition and processing variables. [10][11][12] Due to the abundance of non-equilibrium states, knowledge of the kinetic pathway is particularly important for processing polymer-based blends. [13][14][15][16] Reverse engineering a specific nanostructure is challenging because the parameter space of formulations and processing conditions is very large. For example, in blends of block copolymers (BCPs) and NPs, a common class of nanoscopicallystructured composites, the final arrangement is a function of the polymer chain length, the block ratio, the particle size, the relative proportions of the components, the chemical compatibility between them, and the processing conditions. [15,16] Within the last decade, it has been established that adding organic small molecules to a BCP-NP blend facilitates the incorporation of large or anisotropic particles, accelerates assembly, and produces new nanostructures. [17][18][19] These are exciting milestones in the development of functional nanocomposites but, with the addition of small molecules, the parameter space of nanocomposite compositions has grown even larger.The challenges of working with large parameter spaces are not unique to self-assembly. Humans are excellent at finding patterns in low-dimensional data but struggle to understand trends in high-dimensional systems. Machine learning (ML) methods offer ways to predict outcomes and visualize trends in high-dimensional spaces. As these methods do not rely on hard-coded relationships between parameters, they are suited to complex systems without a solid theoretical foundation. Parameter spaces considered large by experimental standards, such as the 7D space described above, are tiny compared to the capabilities of modern ML models. [20] ML methods have recently Blends of nanoparticles, polymers, and small molecules can self-assemble into optical, magnetic, and electronic devices with structure-dependent properties. However, the relationship between a multicomponent nanocomposite's formulation and its assembled structure is complex and cannot be predicted by theory. The blends can be strongly influenced by processing conditions, which can introduce non-equilibrium states. Currently, nanocomposite devices are designed through cycles of experimental trial and error. Machine learning (ML) methods are a compelling alternative because they can use existing datasets to map high-dimensional spaces. These methods do not rely on known relationships b...