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An open-hardware automated workflow for mesoporous colloidal silica synthesis is developed and applied to study a compositional parameter space. 

Exploiting the ability of a solid-binding elastin-like peptide to micellize, we mineralize monodisperse silica nanoparticles whosepositivesurface charge enables one-step electrostatic assembly of various mono- and bi-material superstructures. 

The synthesis and ligand-mediated assembly of ultrasmall antimony(iii) sulfide nanoparticles is reported. These Sb₂S₃nanoparticles exhibit fast electrochemical cycling and long lifetimes for lithium and sodium ion systems. 

The nano- and micron scale morphology of poly(3-hexylthiophene) (P3HT) and polystyrene-block-polyisoprene-block-polystyrene (PS–PI–PS) elastomeric blends is investigated through the use of ultra-small and small angle X-ray and neutron scattering (USAXS, SAXS, SANS). It is demonstrated that loading P3HT into elastomer matrices is possible with little distortion of the elastomeric structure up to a loading of ∼5 wt%. Increased loadings of conjugated polymer is found to significantly distort the matrix structure. Changes in processing conditions are also found to affect the blend morphology with especially strong dependence on processing temperature. Processing temperatures above the glass transition temperature (Tg) of polystyrene and the melting temperature (Tm) of the conjugated polymer additive (P3HT) creates significantly more organized mesophase domains. P3HT blends with PS–PI–PS can also be flow-aligned through processing, which results in an anisotropic structure that could be useful for the generation of anisotropic properties (e.g. conductivity). Moreover, the extent of flow alignment is significantly affected by the P3HT loading in the PS–PI–PS matrix. The work adds insight to the morphological understanding of a complex P3HT and PS–PI–PS polymer blend as conjugated polymer is added to the system. We also provide studies isolating the effect of processing changes aiding in the understanding of the structural changes in this elastomeric conjugated polymer blend. 

Mechanical deformation of polymer networks causes molecular-level motion and bond scission that ultimately lead to material failure. Mitigating this strain-induced loss in mechanical integrity is a significant challenge, especially in the development of active and shape-memory materials. We report the additive manufacturing of mechanical metamaterials made with a protein-based polymer that undergo a unique stiffening and strengthening behavior after shape recovery cycles. We utilize a bovine serum albumin-based polymer and show that cyclic tension and recovery experiments on the neat resin lead to a ~60% increase in the strength and stiffness of the material. This is attributed to the release of stored length in the protein mechanophores during plastic deformation that is preserved after the recovery cycle, thereby leading to a “strain learning” behavior. We perform compression experiments on three-dimensionally printed lattice metamaterials made from this protein-based polymer and find that, in certain lattices, the strain learning effect is not only preserved but amplified, causing up to a 2.5× increase in the stiffness of the recovered metamaterial. These protein–polymer strain learning metamaterials offer a unique platform for materials that can autonomously remodel after being deformed, mimicking the remodeling processes that occur in natural materials. 

We present an autonomous data-driven framework that iteratively explores the experimental design space of silver nanoparticle synthesis to obtain control over the formation of a desired morphology and size. The objective of the method is to identify design rules such as the effects of the design variables on the structure of the nanoparticle. The framework balances multimodal characterization methods (i.e. UV-vis spectroscopy, SAXS, TEM), taking into account the cost of performing a measurement and the quality of information gained. By integrating with an AI agent, we identify important design variables in the synthesis of small colloidally stable plate-like silver particles and outline how each variable affects plate thickness, radius, polydispersity, and relative concentration. Our findings are consistent with the literature, demonstrating that the framework could be further applied to new systems that have not been well characterized and understood. The framework is generalizable and allows tangible knowledge extraction from the high-throughput experimental runs while still considering inherent stochasticity.