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Peptide conjugate molecules comprising a gold-binding peptide (e.g., AYSSGAPPMPPF) attached to an aliphatic tail have proven to be powerful agents for directing the synthesis and assembly of gold nanoparticle superstructures, in particular chiral helices having interesting plasmonic chiroptical properties. The composition and structure of these molecular agents can be tailored to carefully tune the structure and properties of gold nanoparticle single and double helices. To date, modifications to the β-sheet region (AYSSGA) of the peptide sequence have not been exploited to control the metrics and assembly of such superstructures. We report here that systematic peptide sequence variation in a series of gold-binding peptide conjugate molecules can be leveraged not only to affect the assembly of peptide conjugates but also to control the synthesis, assembly, and optical properties of gold nanoparticle superstructures. Depending upon the hydrophobicity of a single-amino acid variant, the conjugates yield either dispersed gold nanoparticles or helical superstructures. These results provide evidence that subtle changes to peptide sequence, via single-amino acid variation in the β-sheet region, can be leveraged to program structural control in chiral gold nanoparticle superstructures.
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This content will become publicly available on December 1, 2026
Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape
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.
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- Award ID(s):
- 2308979
- PAR ID:
- 10649881
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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