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contributor authorحسین بیگی نصرآبادیen
contributor authorحسین وفائی نژادen
contributor authorسیدعبدالکریم سجادیen
contributor authorHossein Beygi Nasrabadifa
contributor authorhossein vafaeenezhadfa
contributor authorSeyed Abdolkarim Sajjadifa
date accessioned2020-06-06T13:08:12Z
date available2020-06-06T13:08:12Z
date issued2012
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/3342986?show=full
description abstractIn this study, Ni coated aluminum nanoparticles were fabricated by electroless nickel deposition. Effect

of two groups of parameters on the process plating rate were investigated: bath composition (main salt,

reducing agent and complexing agent concentration) and process parameters (pH, plating time and bath

temperature). Simulation of the process was performed using artificial neural network (ANN) media.

Based on the presented model it is possible to design a high efficiency electroless bath, while minimum

received materials are used and maximum plating rate is obtained. According to the model’s results,

0.07 mol/l NiSO4·2H2O, 0.245 mol/l NaH2PO2·H2O and 0.098 mol/l Na3C6H5O7·H2O were chosen as the

optimum electroless bath composition. The optimum bath parameters also were selected as pH of 9.5,

temperature of 80 ◦C and 30 min of plating. At such condition, the most efficient Ni deposition, with

maximum plating rate of 45%, was acquired on the surface of aluminum particles. These samples were

characterized by scanning electron microscopy (SEM) equipped with energy dispersive spectroscopy

(EDS) and X-ray diffraction (XRD). The results showed that a low phosphorus and nanocrystalline Ni

layer, with about 30 nm thickness, has been coated on the aluminum nanoparticles.
en
languageEnglish
titleModeling the electroless nickel deposition on aluminum nanoparticlesen
typeJournal Paper
contenttypeExternal Fulltext
subject keywordsAluminum nanoparticleen
subject keywordsElectroless nickel depositionen
subject keywordsArtificial neural network (ANN)en
journal titleApplied Surface Sciencefa
pages7744-7750
journal volume258
journal issue127
identifier linkhttps://profdoc.um.ac.ir/paper-abstract-1028065.html
identifier articleid1028065


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