Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

Nanoscale structures and materials from the self-assembly of polypeptides and DNA

: The use of biological molecules with programmable self-assembly properties is an attractive route to functional nanomaterials. Proteins and peptides have been used extensively for these systems due to their biological relevance and large number of supramolecular motifs, but it is still difficult to build highly anisotropic and programmable nanostructures due to their high complexity. Oligonucleotides, by contrast, have the advantage of programmability and reliable assembly, but lack biological and chemical diversity. In this review, we discuss systems that merge protein or peptide self-assembly with the addressability of DNA. We outline the various self-assembly motifs used, the chemistry for linking polypeptides with DNA, and the resulting nanostructures that can be formed by the interplay of these two molecules. Finally, we close by suggesting some interesting future directions in hybrid polypeptide-DNA nanomaterials, and potential applications for these exciting hybrids.

Design, Characterization and Applications of Functional Nanomaterials

Nanomaterials are commonly defined as particles existing in nature or artificially manufactured materials that have one or more external dimensions in the 1–100 nm range [...]