Abstract

Supramolecular materials derived from the self-assembly of engineered molecules continue to garner tremendous scientific and technological interest. Recent innovations include the realization of nano- and mesoscale particles (0D), rods and fibrils (1D), sheets (2D), and even extended lattices (3D). Our research groups have focused attention over the past 15 years on one particular class of supramolecular materials derived from oligopeptides with embedded π-electron units, where the oligopeptides can be viewed as substituents or side chains to direct the assembly of the central π-electron cores. Upon assembly, the π-systems are driven into close cofacial architectures that facilitate a variety of energy migration processes within the nanomaterial volume, including exciton transport, voltage transmission, and photoinduced electron transfer. Like many practitioners of supramolecular materials science, many of our initial molecular designs were designed with substantial inspiration from biologically occurring self-assembly coupled with input from chemical intuition and molecular modeling and simulation. In this feature article, we summarize our current understanding of the π-peptide self-assembly process as documented through our body of publications in this area. We address fundamental spectroscopic and computational tools used to extract information regarding the internal structures and energetics of the π-peptide assemblies, and we address the current state of the art in terms of recent applications of data science tools in conjunction with high-throughput computational screening and experimental assays to guide the efficient traversal of the π-peptide molecular design space. The abstract image details our integrated program of chemical synthesis, spectroscopic and functional characterization, multiscale simulation, and machine learning which has advanced the understanding and control of the assembly of synthetic π-conjugated peptides into supramolecular nanostructures with energy and biomedical applications.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.