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Abstract Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform. 

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 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. 

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. 

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.