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Relating the rate of growth of metal nanoparticles to cluster size distribution in electroless deposition.
Iatalese, M; Coluccio, M L; Onesto, V; Amato, F; Di Fabrizio, E; Gentile, F.
Afiliação
  • Iatalese M; Akka Technologies Via Giacomo Leopardi 6 40122 Bologna Italy.
  • Coluccio ML; Department of Experimental and Clinical Medicine, University Magna Graecia 88100 Catanzaro Italy.
  • Onesto V; Department of Experimental and Clinical Medicine, University Magna Graecia 88100 Catanzaro Italy.
  • Amato F; Department of Experimental and Clinical Medicine, University Magna Graecia 88100 Catanzaro Italy.
  • Di Fabrizio E; Physical Science & Engineering Division, King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia.
  • Gentile F; Department of Electrical Engineering and Information Technology, University Federico II 80125 Naples Italy francesco.gentile2@unina.it.
Nanoscale Adv ; 1(1): 228-240, 2019 Jan 15.
Article em En | MEDLINE | ID: mdl-36132476
ABSTRACT
Electroless deposition on patterned silicon substrates enables the formation of metal nanomaterials with tight control over their size and shape. In the technique, metal ions are transported by diffusion from a solution to the active sites of an autocatalytic substrate where they are reduced as metals upon contact. Here, using diffusion limited aggregation models and numerical simulations, we derived relationships that correlate the cluster size distribution to the total mass of deposited particles. We found that the ratio ξ between the rates of growth of two different metals depends on the ratio γ between the rates of growth of clusters formed by those metals through the linearity law ξ = 14(γ - 1). We then validated the model using experiments. Different from other methods, the model derives k using as input the geometry of metal nanoparticle clusters, decoded by SEM or AFM images of samples, and a known reference.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article