Elucidation of Peptide-Directed Palladium Surface Structure for Biologically Tunable Nanocatalysts

• Published in: ACS nano 2015-05, Vol.9 (5), p.5082-5092
• Main Authors: Bedford, Nicholas M, Ramezani-Dakhel, Hadi, Slocik, Joseph M, Briggs, Beverly D, Ren, Yang, Frenkel, Anatoly I, Petkov, Valeri, Heinz, Hendrik, Naik, Rajesh R, Knecht, Marc R
• Format: Article
• Language: English
• Subjects: Alkenes - chemistry; Amino Acid Sequence; Biomimetic Materials - chemistry; Catalysis; Hydrogenation; Metal Nanoparticles - chemistry; Molecular Dynamics Simulation; Nanotechnology - methods; Oligopeptides - chemistry; Palladium - chemistry; Protein Conformation; Surface Properties
• ISSN: 1936-0851; 1936-086X
• DOI: 10.1021/acsnano.5b00168

Peptide-enabled synthesis of inorganic nanostructures represents an avenue to access catalytic materials with tunable and optimized properties. This is achieved via peptide complexity and programmability that is missing in traditional ligands for catalytic nanomaterials. Unfortunately, there is limited information available to correlate peptide sequence to particle structure and catalytic activity to date. As such, the application of peptide-enabled nanocatalysts remains limited to trial and error approaches. In this paper, a hybrid experimental and computational approach is introduced to systematically elucidate biomolecule-dependent structure/function relationships for peptide-capped Pd nanocatalysts. Synchrotron X-ray techniques were used to uncover substantial particle surface structural disorder, which was dependent upon the amino acid sequence of the peptide capping ligand. Nanocatalyst configurations were then determined directly from experimental data using reverse Monte Carlo methods and further refined using molecular dynamics simulation, obtaining thermodynamically stable peptide-Pd nanoparticle configurations. Sequence-dependent catalytic property differences for C–C coupling and olefin hydrogenation were then elucidated by identification of the catalytic active sites at the atomic level and quantitative prediction of relative reaction rates. This hybrid methodology provides a clear route to determine peptide-dependent structure/function relationships, enabling the generation of guidelines for catalyst design through rational tailoring of peptide sequences.