Characterization of bimetallic nanoparticles by fractal analysis

Accepted Manuscript Characterization of bimetallic nanoparticles by fractal analysis

G. Dobrescu, F. Papa, R. State, I. Balint PII: DOI: Reference:

S0032-5910(18)30581-3 doi:10.1016/j.powtec.2018.07.083 PTEC 13565

To appear in:

Powder Technology

Received date: Revised date: Accepted date:

7 February 2018 19 June 2018 23 July 2018

Please cite this article as: G. Dobrescu, F. Papa, R. State, I. Balint , Characterization of bimetallic nanoparticles by fractal analysis. Ptec (2018), doi:10.1016/ j.powtec.2018.07.083

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ACCEPTED MANUSCRIPT Characterization of Bimetallic Nanoparticles by Fractal Analysis G. Dobrescua†, F. Papaa, R. Statea and I. Balinta Romanian Academy, Institute of Physical Chemistry “Ilie Murgulescu”, Spl. Independentei 202, PO Box 12-194, RO-060041, Bucharest, Romania

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†Corresponding author: [email protected]

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ACCEPTED MANUSCRIPT Abstract The bimetallic nanoparticles, used as catalysts due to their activity and selectivity, were prepared using one of the most versatile and convenient synthesis route: the alkaline polyol method. Mono and bimetallic nanoparticles, with both core-shell and alloy structure were obtained. The nanoparticles were characterized by TEM and XRD. The fractal dimension of each micrograph was computed using the “box-counting” method and the “information” method. Computing the box-counting fractal dimension at

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different grey-level thresholds one can obtain a specific curve. The paper will discuss the relation

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between the fractal dimension versus the grey-level threshold curve, the nanoparticles morphology and

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the preparation variables. The novelty of the work is that the shape of the fractal dimension versus the grey-level threshold curve is an indicator if the nanoparticles have alloy or core-shell structure. Also, the

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paper will provide a method to compute both fractal dimensions: of the nanoparticles’ cores and of the

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nanoparticles’ shells.

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Keyword: bimetallic nanoparticles, palladium, fractal theory, alloy, core-shell, alkaline polyol method

ACCEPTED MANUSCRIPT 1. Introduction Over the last years, mono and bimetallic palladium–based nanoparticles have been intensively studied for their direct application in modern technology as electronic devices [1], sensors [2,3],

catalysts [4-6],

biodetectors [7] etc. Bimetallic nanoparticles including Pd with various transition metals were studied as catalysts in nitrate reduction reaction [8], hydrogenation of olefins [9], acrylonitrile hydration [10], photo-

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induced hydrogen generation from water [11,12]. One of the typical approach in improving the catalytic activity and selectivity of the nanoparticles is to

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modify the microstructure of catalysts in order to obtain controlled morphology (size, shape). The

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bimetallic nanoparticles revealed a greater interest than the monometallic ones due to the addition of the second metal that can very much improve the catalytic performance by adjusting the effects induced by

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the first metal: ligand effect, geometric effect or lattice strain effect [13,14].

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It was proved that, in order to obtain intimate contact between the metals and the desired size and structure (core shell or alloy) of the bimetallic nanoparticles [15], the synthesis should be directed by

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adjusting the preparation variables.

The classical method for catalysts synthesis by impregnation route has some disadvantages: the influence

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of the support on formation of the catalytic phases, the difficulty of controlling the size and the composition of the active phase and the low concentration of the active components compared to the

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supporting materials.

Thus, carrying out the synthesis of the bimetallic nanoparticles in a controlled way and dispersing the nanoparticles over high surface area supports will lead to a better control over the composition and size of the catalytic active phase(s). So, in the colloidal synthesis stage, adjusting the preparation variables, the size and the structure of the bimetallic catalysts can be managed effectively. When mild conditions are used, deposition onto the oxide support will usually have little effect on the size of the nanoparticles.

ACCEPTED MANUSCRIPT In our work, the bimetallic nanoparticles were prepared using the alkaline polyol method. Polyols have the advantage to be used both as reducing agents and solvents. Protective agents, such as PVP (polyvinylpyrrolidone), control the particle growth and prevent agglomeration. Mono (Pd) and bimetallic nanoparticles (Pd-Cu, Ag-Pd), with both inverse or direct core-shell and alloy structure, with various molar ratios between the two metals were obtained.

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The characterization of nanoparticles is very important in order to control their quality in the technological process and to select the experimental conditions leading to the desired structure [16-18].

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The nanoparticles were characterized by TEM and XRD. The fractal dimension of each micrograph was

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a higher specific surface and better catalyst properties.

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computed. The final goal is to obtain higher fractal dimensions, as a higher fractal dimension is related to

The fractal theory is an important tool in understanding and describing objects with irregular geometries

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and phenomena leading to such objects. It provides a method to assign a number – the fractal dimension – for every structure or surface, describing how irregular, porous, agglomerate it is [19]. Most materials

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exhibit surface fractal behaviour as their defects and geometrical irregularities are self-similar [20]. Fractal theory was used as a complimentary technique to provide information about nanoparticles

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morphology [21-26].

The fractal dimension was computed using the “box-counting” and the “information” method. These

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methods were extensively described elsewhere [25]. The paper will discuss the relation between the “boxcounting” and “information” fractal dimension versus the grey-level threshold curve, the nanoparticles morphology and the preparation variables. Bimetallic nanoparticles were obtained using two different preparation routes which we expect to lead to different structures: alloy and core-shell nanoparticles. In the following we shall prove that the shape of the box-counting fractal dimension versus the grey-level threshold curve is a direct indicator of the nanoparticles’ structure. More of that, a method to compute the fractal dimension of the cores, on the one hand, and of the shells, on the other hand, is described.

ACCEPTED MANUSCRIPT 2. Experimental Section The polyvinylpyrrolidone (PVP)-protected mono- and bimetallic nanoparticles were prepared by polyol method [27]. The modified protocol of alkaline polyol method has been described in detail in a previous publication [28]. Pd(NO 3 )2 sollution, Pd 8.5%

w/w (Alfa Aesar), AgNO 3 (Alfa Aesar) and

Cu(CH3 COO)2 *2H2 O Merck were used as precursors for the synthesis of metal and bimetal

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nanoparticles. The dissolution of the Pd precursors in ethylene glycol (EG) with a concentration of metal ions in EG of 3.810-2 M was used. The alkaline solution 0.25 M NaOH/0.38 M PVP/EG was prepared by

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ratio of polymer protector PVP and metal precursor was 1/10.

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dissolving NaOH in EG and then, the PVP (MW = 8,000) was dissolved in the alkaline EG. The molar

Sample A (Pd monometallic nanoparticles) was synthesized in two steps: first, the alkaline solution 0.25

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M NaOH/0.38 M PVP/EG was heated at 160 °C in hydrogen flow (30cm3 /min) under vigorous stirring. In

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the second step Pd(NO 3 )2 Alfa Aesar precursor ( 3.8*10-2 Pd2+/EG ) was added dropwise and stirring, meanwhile the temperature is maintained at 160 °C for 1h to ensure the nucleation process and growing

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of the metallic particles as the metallic precursor is totally reduced (single reduction step). Sample B (Pd-Cu bimetallic nanoparticles) was synthesized similar to Sample A, except the second step,

(single reduction step).

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which consists of the addition of both Pd and Cu-precursors, simultaneously, with a molar ratio Pd:Cu 4:1

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Sample C (Pd-Cu nanoparticles, molar ratio Pd:Cu 1:1) was synthesized like sample A, Pd nanoparticles being prepared as described above; after that the Cu2+ precursor was added and the sample was cooled at room temperature for 30 min in hydrogen flow. The colloidal suspension was heated again at 160 °C to reduce the metallic precursors and cooled again, at room temperature. Black stable colloidal suspensions of bimetallic nanoparticles in EG were obtained (successive reduction steps). Sample D (Cu-Pd bimetallic nanoparticles, molar ratio Cu-Pd 1:1) was synthesized in two successive reduction steps: first, Cu nanoparticles were obtained using Cu-precursor added, under vigorous stirring,

ACCEPTED MANUSCRIPT in the alkaline solution, 1:25 diluted in ethylene glycol at 160 °C and hydrogen flow (30 cm3 /min). In the second step, after cooling the solution in hydrogen flow, Pd-precursor was added and the temperature was raised at 160 °C for 1 h, ensuring the nucleation and particles growing process and, also, the totally reduction of the metallic precursor. Sample E (Pd-Cu bimetallic nanoparticles) was prepared in a single step, adding Pd and Cu precursors

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simultaneously and the equivalent quantity of the alkaline solution 0.25 M NaOH/0.38 M PVP/EG to obtain a molar ratio of Pd:Cu 1:1. Synthesis was achieved in hydrogen, with a 30 minutes air-evacuation

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stage and then, the sample was heated at 160 °C. The nucleation and the metallic precursors reduction in

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hydrogen flow were conducted at 160 °C for 60 minutes (single reduction step).

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Sample F (Pd-Cu bimetallic nanoparticles) was prepared similar to sample E, using convenient quantities of metallic precursors simultaneously reduced in alkaline solution at molar ratio of Pd:Cu 1:4 (single

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reduction step).

Sample G (Pd-Ag nanoparticles, molar ratio Pd:Ag 1:1) was synthesized similar to Sample C (successive

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reduction steps), using in the second stage Ag-precursor AgNO 3 / EG ( 3.8*10-2 Ag+/EG ). Sample H (Ag-Pd nanoparticles, molar ratio Pd:Ag 1:1) was synthesized similar to Sample D (successive

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reduction steps), using in the first stage Ag-precursor AgNO 3 / EG ( 3.8*10-2 Ag+/EG). Finally, the stable colloidal suspension of mono or bimetallic black nanoparticles was obtained. The

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metallic or bimetallic nanoparticles were separated by the addition of an equivalent volume of acetone to the colloidal suspensions. The mixture was then cooled to -16 °C for 24 h; the precipitates were filtered and washed several times with acetone to remove any traces of remained EG and afterward dried in oven at 100 °C for 6 h. The nanoparticles were characterized by TEM (Transmission Electron Microscopy, using a JEOL 1200 EXS) and XRD (X-ray Diffraction). The crystalline structure of PVP-protected mono and bimetallic powders was analyzed with D8 Advance (Bruker-AXS) apparatus using CuKα radiation (k = 1.54 A °). The diffraction patterns were recorded in the 2H = 30–135o domain. The data were fitted

ACCEPTED MANUSCRIPT using Pawley method to obtain the values of lattice parameters. The average crystallite sizes were calculated from peaks broadening, using Lorentzian function to fit the peaks’ shapes.

3. Fractal Analysis Although it is not the ultimate answer to the question: how can describe better the geometry of natural

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forms, the fractal theory provides some insights in understanding properties of objects with irregular geometries and phenomena leading to such objects. The method computes a number – the fractal

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dimension – capable to describe how irregular, porous, or agglomerate is such an object. More of that, the

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fractal dimension shows if an object is self-similar or not [19, 29].

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The fractal dimension was computed using two methods: the “box-counting” method and the “information” method. As both methods use black and white images, the transmission electron

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micrographs were transformed using a grey-level threshold: all pixels with the grey level greater than the threshold will be turn into 255 (white pixel), meanwhile the pixels with the grey level lower than the

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threshold will be turn into 0 (black pixel).

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In literature, Prakash reported a method to threshold the grey level images using fractal dimension and transform the two dimensional grey level images into the corresponding binary images [30]. The method computed the fractal dimension of an equivalent three dimensional surface, considering the grey level of

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the pixel as the point height and determining the scale of resolution that maximizes the fractal dimension. The gray level of each pixel is converted into a binary value by comparing with a threshold for the locality surrounding that pixel. We propose the reverse problem: to compute the fractal dimension for each grey-level threshold and therefore, obtaining a characteristic fractal curve of each image. The method was used elsewhere [31] to analyse AFM images.

ACCEPTED MANUSCRIPT The box-counting fractal dimension and the information dimension were computed for every black and white image. The relation between the fractal dimension versus the grey-level threshold curve, the nanoparticles morphology and the preparation variables will be discussed. The box-counting dimension is defined as [19, 32]: N(r) = Ar-D,

(1)

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where D is the fractal dimension, N(r) is the number of boxes of size r which cover the object and A is the

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prefactor, named lacunarity, which is a measure of how the space is filled, a measure of the gap or of the

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object texture [33].

Information fractal dimension is generally different from the box dimension. It effectively assigns

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weights to the boxes in such a way that boxes containing a greater number of points count more than

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boxes with less points. The information dimension Di is defined by [25]: I(r) ~ -Di log r

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where I(r) is the information entropy:

𝑁(𝑟 )

and

mi = Mi/M,

(3)

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𝐼 (𝑟) = − ∑𝑖 =1 𝑚 𝑖 𝑙𝑜𝑔𝑚 𝑖

(2)

Mi is the number of points in the i-th box and M is the total number of points in the set. The box-counting fractal dimension and the information dimension were computed using Benoit 1.31

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computer code by TruSoft Int'l Inc.

4. Results and Discussions

The bimetallic nanoparticles were obtained by successive reduction steps of metal ions (samples C, D, G and H) or by a single reduction step (B, E and F). In the first case, Pd (sample C and G)/Cu (sample D)/Ag(sample H) were reduced in the first step. The strong reducing ability of the hydrogen atoms adsorbed onto the surface of the reduced metal (Pd, Cu or Ag) will lead to the reducing of the second

ACCEPTED MANUSCRIPT metal ions (Cu/Ag or Pd respectively) on the metal seed. Thus, we expected that samples C, D, G and H exhibit direct core-shell structure (with Pd core) or inverse core-shell structure (with Cu or Ag core). We expected that the core-shell structure to be favoured by successive reduction steps of metal ions. In the second case, as a consequence of the simultaneously reduction of metal ions occurs in the alkaline solution we expected that PdCu solid solution to be formed. 4.1 XRD characterization

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XRD analysis is usually used to obtain information about the size, composition and chemical state of

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crystalline domains of bimetallic nanoparticles with nanoscale crystallites. The subnanometric-sized

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XRD. Fig.1 presents the XRD pattern of the samples.

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crystals do not produce XRD peaks, these smaller particles have not the necessary size for detection using

ACCEPTED MANUSCRIPT Table 1 Characterization data of Pd, Pd–Cu and Pd–Ag nanoparticles obtained from XRD measurements Samples

Lattice constant (a) (A˚ )

XRD crystallite size a(nm)

b

Composition

Synthesis method

Proposed morphology

3.8911

9.6

100 % Pd

Si ngl e reducti on s tep

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B

3.8805

2.9

96.5% Pd

Si ngl e reducti on

Al l oy

3.5 % Cu

s tep

3.8838

3.7

97.7 % Pd

G

3.8683

3.8972

7.6

3.9125

5.3

4.6

s teps

85.9%Pd

Succes i ve reducti on

14.1 %Cu

s teps

88.4% Pd

Si ngl e reducti on s tep

Al l oy

Si ngl e reducti on s tep

Al l oy

Succes i ve reducti on s teps

Core-s hel l

88.7 % Pd

Succes i ve reducti on

Invers e cores hel l

11.3% Ag

s teps

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6.5

Core-s hel l

11.6% Cu 92.1% Pd 7.9 % Cu 96.4% Pd 3.6% Ag

Invers e cores hel l

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H

3.8582

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F

4.09

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E

3.8512

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D

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2.3 % Cu

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C

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A

a

Average crystallite size determined by Scherrer formula and by assuming Lorentzian peak shapes

b

The composition was calculated according to Vegard’s law: a alloy = xaPd + (1 – x)aCu(or aAg)where ‘‘a’’

represents the lattice constant and ‘‘x’’ the fraction of the element; a Pd= 3.890 A˚ , aCu = 3.615 A˚ and aAg = 4.090 A. A short look to the graphs showed similar average nanoparticles size for every sample. The sharp and well defined XRD peaks suggest that the samples were characterized by large average diameters. As it

ACCEPTED MANUSCRIPT can be seen in Fig. 1, the peaks of samples B, C, D, H are slightly shifted from the characteristic positions of Pd(fcc). Although, in literature there are works related to determination of shell thickness of core-shell nanoparticles using XRD [34], if any, the characteristic reflections of a copper phase could not be observed in the XRD patterns of the samples B, C, D, E and F. This behavior can be explained as a result of the low copper concentration in alloy or/and of the thickness of Cu shell in the core-shell structures, insufficient to give significant diffraction intensities. In order to produce the characteristic XRD peaks,

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the crystalline structure should be at least few nanometers in size. The Ag phase is evidenced in the XRD

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pattern of sample G as a possible consequence of the direct core-shell structure, with Pd core and Ag-

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shell. Thus, although the allied silver concentration in core is very low (Table 1-sample G), the silver shell has a sufficient size to produce significant XRD peaks. The average crystallite sizes, determined

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from the fit of the XRD peaks with a Lorentzian function of the Scherrer equation and assuming that nanoparticles are spheres and the peak width depends only by crystallite size, are presented in Table 1.

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The lattice constants "a" of the nanocrystalline particles were determined by fitting the experimental patterns using Pawley XRD data processing software. The average compositions of the crystalline nano

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domains were derived from the experimentally determined lattice constant by applying Vegard's law (see

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equation in Table 1). The composition data show that the bimetallic nanoparticles exhibit insignificant concentration of Cu or Ag allied with Pd. The shift of Pd peak positions is usually attributed to lattice contraction caused by alloy formation when Pd atoms (atomic radius=1.370 Å) are substituted by smaller

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copper atoms (atomic radius=1.278 Å) in the fcc structure. The computed lattice constant is very close to the lattice constant of Pd (a=3.890 A) as a result of weak alloy formation (low Cu and Ag concentration in alloy – see Table 1).

Comparing XRD crystallite sizes from Table 1 with average particle radius as TEM images depicted (Figs.2 and 3) there are few observations to be made. The metallic Pd (sample A) exhibit a 9.6 nm crystallite size, meanwhile TEM images reveal at least 4-5 nm. Analyzing more TEM images, it appears that there are some 9-10 nm nanoparticles (fig.2 – sample A, left corner up, 10nm scale image), with

ACCEPTED MANUSCRIPT crystalline structure, capable to produce the observed XRD peak, but the smaller 4-5 nm nanoparticles did not lead to a strong enough XRD peak. On the other hand, sample B exhibit a 2.9 nm XRD crystallite size, but 15-20 nm nanoparticles’ sizes in TEM image. Explanation is straightforward. The nanoparticles usually consist of smaller crystallites or of crystalline and amorphous areas;

XRD technique measures

only the crystalline area [35], so differences between the two values may occur. Sample C, D, E, G and H exhibit good agreement between the TEM diameters and XRD measurements. Sample F has the same

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behaviour as sample B, with large nanoparticles (~20nm), but with 7.6 nm XRD crystallite size, meaning

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that the nanoparticles consist of agglomerated crystallites.

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4.2 Box-counting fractal dimension dependence on grey-level threshold. Lacunarity versus grey-

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level threshold

Noise reduction of the images was used to enhance the image quality and cut the clipping peaks in image

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histogram. Although the grey level of each pixel is related to the local height of the sample, image normalization is not necessary as the purpose of the paper is not to compare fractal dimensions of

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different images, but to discuss the shape and form of the fractal dimension dependences on grey-level

one with another.

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threshold. Images with the same magnifier were analysed, as we shall compare each image fractal curve

The box-counting fractal dimension versus grey-level threshold curves were depicted in figs. 2 and 3,

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together with lacunarity dependence on the grey-level threshold of the micrographs. The curves show very interesting behaviour: one-plateau (fig.2 right, up) or two plateaus shapes (fig.3 right, up) are depicted, depending on the number of nucleation processes taken place. There is a strong link between the nanoparticle preparation method (single or successive reduction steps) and the fractal dimension versus the grey-level threshold curve. The single reduction step method will lead to “oneplateau curve”, meanwhile the successive reduction method will lead to “two-plateaus curves”. Therefore, the samples A, B, E, F exhibit one-plateau curves (fig.2 right, up), meanwhile the samples C, D, G, H (fig.3 right, up) exhibit two-plateaus behaviour. We supposed that the “one-plateau” fractal dimension

ACCEPTED MANUSCRIPT curve behaviour is due to the alloy structure (samples B, E and F), meanwhile the core-shell nanoparticles are responsible for the “two-plateaus” behaviour (C, D, G and H samples), as we shall try to prove in the following. TEM images of bimetallic nanoparticles prepared by successive reduction method exhibit the presence of the core-shell nanoparticles. Therefore, sample C (Pd:Cu 1:1M) shows some nanoparticles with more than

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one Pd nucleus (the dark areas) in the copper shell. Also, the core has an insignificant content of Cu (2.3%) evidenced by the XRD peaks. The Cu shell is not capable to produce a significant XRD peak as a

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result of its thickness. XRD crystallite sizes are in accord with TEM nanoparticle diameters.

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Nanoparticles with core-shell structure with Pd core and copper shell together with metallic palladium nanoparticles and alloy nanoparticles were formed. A two-plateaus curve fractal dimension dependence

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on grey-level threshold is evidenced. The effect is not a significant one, as the Cu shell is very narrow, in

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accord with the absence of the copper peaks in XRD measurements. Also, it seems that not all the nanoparticles are core-shell.

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TEM images of sample D (Cu:Pd 1:1M) exhibit a core-shell structure, too. Although we expected to obtain copper core nanoparticles, XRD results indicate that the Cu-cores should be very small or

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characterized by a low copper concentration as no peaks were observed. As TEM images indicate consistent core sizes, our assumption is that the nanoparticles cores were formed by a Pd/Cu alloy with

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14,1% Cu concentration, meanwhile the surface layer (the shell) is formed by Pd. Also, the presence of some Pd metallic nanoparticles is noticed. The two-plateaus characteristic fractal curve is very strong, as an indication of the core-shell nanoparticles presence. The G sample was prepared by successive reduction of Pd and Ag. TEM images exhibit core-shell like structures. XRD analysis evidenced silver peaks, together with the Pd X-ray structure, meaning that the nanoparticles were formed by Pd insignificant allied with Ag (3.6%) cores and silver shells. Visual inspection of TEM images corroborated with XRD measurements depicted the presence of palladium and

ACCEPTED MANUSCRIPT Pd-Ag allied nanoparticles.

Both D and G samples exhibit two-plateaus curve fractal dimension

dependence on grey-level threshold. The effect is strong enough to be noticed. The H sample was prepared also by successive reduction, but in different order: first Ag is reduced and second, the Pd ions. No silver peaks were evidenced in XRD diffraction pattern. The situation seems to be similar to sample D. We assume that the core nanoparticle is the Pd/Ag alloy with 88.7% Pd, meanwhile

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the shell consists of the Pd layer. Probably, some palladium nanoparticles were formed. A two-plateau curve fractal dimension dependence on grey-level threshold is evidenced.

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The same behaviour was observed on micrographs with different magnifiers. In figure 4, a TEM image of

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Pd-Cu inverse core-shell structure obtained by successive reduction steps is presented (same preparation

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conditions as sample D), together with the fractal dimension dependence on grey-level threshold depicting the same two-plateaus curve. The two-plateaus effect depends on the system composition: it is a

to a well-define two-plateaus curve [36].

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measure of the core-shell nanoparticles ratio, meaning that a higher core-shell nanoparticles ratio will lead

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TEM images of samples B, E and F exhibit nanoparticles with alloy structures, as a result of the simultaneously reduction of the two metals in solution. Sample B and F exhibit small amount of Cu in

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Pd-Cu alloy, meanwhile in the E sample (1:1M) Cu is present in a larger amount: 11.6%. No copper peaks were noticed in the XRD pattern. Darker areas in samples B, E and F can be assigned to metallic

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palladium, meanwhile lighter areas correspond to the palladium-copper alloy, as XRD spectra reveals Pd peaks, but no copper lines can be seen in the XRD measurements. The assumption that the single reduction step method lead to core-shell nanoparticles, meanwhile the simultaneously reduction method lead to alloy structure nanoparticles is supported by visual inspection and by fractal analysis of TEM images. XRD patterns did not infirm these suppositions. It is obvious that different preparation methods lead to different behaviour of the fractal dimension dependence on greylevel threshold curves.

ACCEPTED MANUSCRIPT The explanation for the one-plateau curves is straightforward. When the grey level threshold is close to its maximum value 255, all or the most of the pixels will be turned into black, as for an empty space, meaning that the fractal dimension will be very low, 0 or close to 0. On contrary, when the grey level threshold is close to 0, all or the most of the pixels will be turned into white, as for a complete occupied space, and therefore, the fractal dimension will be close to 2.

(4)

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𝑁𝑏𝑜𝑥 (𝑟, λ ) = 𝐴𝑏𝑜𝑥 (λ )𝑟−𝐷𝑏𝑜𝑥 (λ ) .

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From eq.(1) at a defined grey level threshold λ the number of occupied r-size boxes is described by:

For r=1, the number of occupied pixels for a grey level threshold λ is 𝑁𝑏𝑜𝑥 (1, λ ) = 𝐴𝑏𝑜𝑥 (λ ). This

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number is different of the real number of occupied pixels of the greyscaled original micrograph, N 0 (1).

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Let’s define the difference between the two quantities as T(1, λ): 𝑇 (1, λ ) = 𝑁𝑏𝑜𝑥 (1, λ ) − 𝑁0 (1).

(5)

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There will be a grey level threshold λ 0 that verifies the relation:

𝑇 (1, λ0 ) = 𝑁𝑏𝑜𝑥 (1, λ0 ) − 𝑁0 (1) = 0 .

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Similar to eq.(5), one can define:

𝑁𝑏𝑜𝑥 (𝑟, λ ) = 𝑁𝑜 (𝑟) + 𝑇(𝑟, λ ) = 𝐴𝑜 𝑟 −𝐷𝑜 + 𝑇(𝑟, λ )

,

(7)

, is the self-similarity relation of the bi-dimensional projection of the

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where 𝑁𝑜 (𝑟) = 𝐴𝑜 𝑟−𝐷𝑜

(6)

nanoparticles set, the micrograph, and Do is the box counting fractal dimension of the micrograph, considering nanoparticles as a set of black balls. From the eqs. (5)-(7), it is straightforward that: 𝐴𝑏𝑜𝑥 (λ ) = 𝑇 (1, λ ) + 𝑁0 (1) + 𝑐𝑜𝑛𝑠𝑡.

(8)

The last term, the constant, was introduced as the number of pixels resulted from the image noise. For λ= λ0, T(1,λ0 )=0 and Abox(λ0 )=N0 (1) + const.

ACCEPTED MANUSCRIPT Figs.2 and 3, right, down, showed the dependence of lacunarity Abox on grey level threshold λ . The curves have the same behaviour. An empirical analysis of the micrographs, computing the fractal dimension D0 for the nanoparticles represented as black balls for sample B, gives: D0 =1.912±0.014 and A0 = 8.24 105 . The function T(r,λ) versus r for a set of λ from 10 to 250 step 10 is represented in fig. 5.

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The value of λ that minimizes the function: ∑𝑟 𝑇(𝑟, 𝜆)2 is λ0 = 100. The corresponding fractal dimension is D(100) = 1.935±0.023 and lacunarity A(100) = 8.98 10 5 in good agreement with D0 and A0 . It seems

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that λ=100 is the best choice to compute the box-counting fractal dimension of the sample B micrograph.

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Results are presented in Table 2.

D0

A0

λ0

D(λ0 )

A(λ0 )

A

1.427±0.457

8.39 104

220

1.325±0.973

6.8 104

B

1.912±0.014

8.24 105

100

1.935±0.023

8.98 105

E

1.681±0.015

2.57 105

180

1.621±0.452

2.54 105

F

1.799±0.035

2.36 105

160

1.666±0.169

2.96 105

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Sample

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Table 2 The fractal dimensions D 0 , D(λ0 ) and lacunarities A0 , A(λ0 ) for mono-metallic and alloy nanoparticles.

There is a good agreement between the fractal dimension D0 and D(λ0 ); to notice that the poor quality of

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the images is responsible of the great standard error for D(λ0 ) (samples A, E and F). TEM image of sample A is not so clear as we needed; also, the particle sizes is low and there are not enough particles to obtain a good statistic. Fractal properties of metallic sample seems to be lower than of the other samples. These characteristics lead to the great standard errors in Table 2. It seems that the fractal dimension of bimetallic alloy sample D0 increases as the nanoparticle size, from TEM images, increases, too. This behaviour can be explained by two effects: (i) higher particle radius will lead to a higher coverage of the space, implying a higher fractal dimension; (ii) although the fractal

ACCEPTED MANUSCRIPT dimension, as diffusion-limited-aggregation simulations showed, is unaffected by the primary particle size distribution [37], such a dependence can be assigned to different nucleation mechanisms related to different preparation conditions and different precursors concentrations. Also, it appears that there is a link between the palladium composition as XRD measurements depicted (Table 1, samples B, E and F) and the fractal dimension. The relation could be explained by the fact that palladium particles appear as

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dark areas and will affect stronger the fractal structure, increasing the computed fractal dimension. Meanwhile lacunarity versus grey level threshold curve has the same behaviour for alloy structure

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nanoparticles or core-shell nanoparticles, the fractal dimension versus grey level threshold curves are

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quite different. Therefore, the core shell nanoparticles exhibit two-plateaus curves of the box-counting fractal dimension versus grey level threshold dependences.

The preparation method of the core shell

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nanoparticles is the key for this behaviour. As we already mention, there are two steps in the preparation

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method of the core-shell nanoparticles: first, mono-metallic nanoparticles were prepared (Pd, Cu or Ag) and second, another precursor is added to produce the core shell nanoparticles after reducing and

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nucleation. So, there are two reduction/nucleation processes: one responsible for the core, and the second responsible for the shell. The two processes are closely related to the plateaus of the fractal dimension

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versus grey-level curve. When the grey-level threshold is closed to 255, the image will turn into a black one, and the fractal dimension will be close to 0. Defining λ m as the grey-level threshold separating the

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two plateaus, at higher grey-level threshold, λ>λm and λ<255 (fig.6), the image will become a set of points with higher grey-level pixels, points assigned to the core of the bi-metallic nanoparticles. When λ<λm and λ>0, the image will consist of the pixels assigned to the core shell together with the core of bi-metallic nanoparticles. From this point of view, the second zone, for lower λ describes the nanoparticles shell formation, meanwhile the first zone, for higher λ describes the nanoparticles core formation. There are two fractal dimensions D1 = D(λ1 ) and D2 = D(λ2 ) describing the self-similarity of the core and of the shell, in other words the two nucleation processes.

ACCEPTED MANUSCRIPT As we described above in eq.(4), the first nucleation process, producing the mono-metallic nanoparticles, which will be the core of the core-shell bimetallic nanoparticles, can be described using the equation: 𝑁1 (𝑟, λ ) = 𝐴1 (λ )𝑟−𝐷1 (λ ), λ>λm.

(9)

Similar, for the second nucleation process, the equation will be: 𝑁2 (𝑟, λ ) = 𝐴2 (λ )𝑟−𝐷2 (λ ), λ <λm .

(10)

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For r=1, the number of occupied pixels for a grey level threshold λ >λ mis 𝑁1 (1, λ ) = 𝐴1 (λ ) and

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𝑁2 (1, λ ) = 𝐴2 (λ ) for λ <λm. N1 (1, λ) and N 2 (1, λ) are different from the real number of occupied pixels

𝑇1 (1, λ ) = 𝑁1 (1, λ ) − 𝑁0 (1),

(11)

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of the greyscale original micrograph, N 0 (1):

(12)

and

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𝑇2 (1, λ ) = 𝑁2 (1, λ ) − 𝑁0 (1). The grey level thresholds λ 1 and λ2 verify the equations:

(13)

𝑇2 (1, λ2 ) = 𝑁2 (1, λ2 ) − 𝑁0 (1) = 0 .

(14)

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𝑇1 (1, λ1 ) = 𝑁1 (1, λ1 ) − 𝑁0 (1) = 0

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and

Therefore, one can define:

𝑁1 (𝑟, λ ) = 𝑁𝑜 (𝑟) + 𝑇1 (𝑟, λ ) = 𝐴𝑜 𝑟−𝐷𝑜 + 𝑇1 (𝑟, λ ) = 𝐴1 (𝜆)𝑟−𝐷1 (𝜆) , for λ>λm

and 𝑁2 (𝑟, λ ) = 𝑁𝑜 (𝑟) + 𝑇2 (𝑟, λ ) = 𝐴𝑜 𝑟 −𝐷𝑜 + 𝑇2 (𝑟, λ ) = 𝐴2 (𝜆)𝑟−𝐷2 (𝜆) ,

(15)

ACCEPTED MANUSCRIPT for λ<λm

(16)

with 𝑁𝑜 (𝑟) = 𝐴𝑜 𝑟 −𝐷𝑜 , the self-similarity relation of the bi-dimensional projection of the nanoparticles set, the micrograph, and Do is the box fractal dimension of the micrograph, considering nanoparticles like a set of black balls. It is straightforward that:

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𝐴1 (λ ) = 𝑇1 (1, λ ) + 𝑁0 (1) + 𝑐𝑜𝑛𝑠𝑡1 ,

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𝐴2 (λ ) = 𝑇2 (1, λ ) + 𝑁0 (1) + 𝑐𝑜𝑛𝑠𝑡2 .

(18) and λ <λm

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The constant was introduced as the number of pixels resulted from the image noise, for λ >λ m

(17)

respectively.

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From eqs.(13) and (14), T1 (1, λ1 )=0, T2 (1, λ2 )=0 and A1 (λ1 )=N0 (1) + const 1, A2 (λ2 )=N0 (1) + const 2 , so: (19)

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A1 (λ1 )= A2 (λ2 ) + const. The continuity conditions lead to the following relations:

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D1 (λm )=D2 (λm ), (20)

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A1 (λm )=A2 (λm),

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T1 (1, λm )=T2 (1, λm ) +const.

An empirical analysis of the micrographs, computing the fractal dimension D0 for the nanoparticles represented as black balls, for sample C, gives: D0 =1.841±0.009 and A0 = 3.92 105 . Figure 7 depicts the dependence of the T(r,λ) versus r and grey level threshold for sample C. The ∑𝑟 𝑇(𝑟, 𝜆)2 has a minimum for λ=140. When λ≥140, the fractal behaviour occurs when r>r max /8, where rmax is the maximum cut-off limit, corresponding to the first nucleation. For this domain, the function ∑𝑟 𝑇(𝑟, 𝜆)2 is minimum when

ACCEPTED MANUSCRIPT λ1 =170 and, therefore D1 (λ1 )=1.632. When λ<140, the minimum of ∑𝑟 𝑇(𝑟, 𝜆)2 is obtained for λ=130, so D2 (λ2 )=1.826. Also, D1 (λm )=D2 (λm)=1.773 and λm=140.

The overall results for the core-shell samples are presented in Table 3. Table 3 The fractal dimensions D 0 , D(λm ), D(λ1 ) and D(λ2 ) for core-shell bimetallic nanoparticles. D0

λm

D(λm )

λ1

D(λ1 )

C

1.841±0.009

140

1.773±0.048

170

1.632±0.074

110

1.796±0.026

130

1.854±0.018

130

1.819±0.107

160

H

1.875±0.032

100

1.836±0.023

110

1.827±0.029

λ2

D(λ2 )

130

1.826±0.037

100

1.813±0.010

1.669±0.061

120

1.863±0.079

1.776±0.018

90

1.884±0.021

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G

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1.855±0.010

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D

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Sample

Sample C and sample G have similar behaviors, with the first nucleation process (palladium nucleation) characterized by a fractal dimension of 1.632 and 1.669, respectively. The second nucleation process is

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characterized by a fractal dimension of 1.826 for copper nucleation and 1.863, respectively, for silver

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nucleation. For samples D and H, the first nucleation process is characterized by a higher fractal dimension of 1.827 for copper and 1.776 for silver, meanwhile the second nucleation process (the

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palladium nucleation) is characterized by two very close values of fractal dimensions: 1.813 (sample D) and 1.884 (sample H). It seems that palladium nucleation is characterized by a fractal dimension of 1.6 for the first step, and 1.8 for the second step, meanwhile copper nucleation is characterized by a fractal dimension of 1.8, the same in the first and in the second step; silver nucleation is characterized by a fractal dimension of 1.7 in the first step and 1.8 in the second step. In conclusion, it is straightforward to notice that the number of plateaus of the fractal dimension versus grey level threshold curve is directly related to the number of reduction/nucleation processes occur. The

ACCEPTED MANUSCRIPT method offers the possibility to compute the fractal dimension of every reduction/nucleation process D1 (λ1 ) and D2 (λ2 ), together with the fractal dimension of the entire micrograph D(λ m). We proved that a core-shell structure will lead to the two-plateaus fractal curve dependence on grey-level threshold, meanwhile the alloy will lead to the single-plateau fractal curve. Visual inspection of TEM images does not offer quantitative information about the core-shell formation. Our results show the formation of the core-shell nanoparticles (samples C, D, G, H); more core-shell nanoparticles are formed,

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the effect on fractal curve will be stronger.

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The fractal analysis of TEM images supports the assumption that the successive adding of precursors

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during the preparation process lead to core-shell nanoparticles’ structure, meanwhile the simultaneously adding lead to alloy nanoparticles’ structure. Future work is necessary to investigate in detail how the

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fractal dimension associated to the core or to the shell of nanoparticles depends on the preparation

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conditions.

4.3 Information dimension dependence on grey-level.

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The information dimension versus grey level threshold curves for samples B (alloy), C (direct core-shell

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structure) and D (inverse core-shell structure) are displayed in fig. 8a, together with the information lacunarity versus grey level threshold curves fig.8b. The images showed similar behaviour of the alloy

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nanoparticles, direct core-shell nanoparticles and inverse core-shell nanoparticles for both fractal dimension and lacunarity curves. The information fractal dimension curves exhibit one-plateau behaviour. The lack of sensibility on nanoparticles’ structure of the information dimension could be explained by the information dimension computing algorithm. The method effectively assigns weights to the boxes in such a way that boxes containing a greater number of points count more than boxes with less points. It seems that the method will be less sensitive to changes caused by different nucleation processes. The method will count an overall fractal dimension, with one-plateau behaviour of the information dimension dependence on grey level threshold curve, for alloy and core shell nanoparticles, too.

ACCEPTED MANUSCRIPT 5. Conclusions The micrographs were analysed using the fractal theory. The major finding of the paper is that the boxcounting dimension versus grey-level threshold curve offer direct information about the structure of the metallic nanoparticles: it is a way to detect if the nanoparticles have alloy or core-shell structures. Pd, PdCu and AgCu nanoparticles with alloy, direct core-shell and inverse core-shell structures were

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investigated using XRD and TEM. XRD analysis showed Pd peaks together with a weak Ag pattern for the direct core-shell structure of bimetallic AgCu nanoparticles. Cu pattern could not be emphasized as a

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result of the low copper concentration in alloy together with a thick Cu shell in the core-shell structures,

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insufficient to give significant diffraction intensities. Also, the inverse core-shell structure is characterized by a Pd-Cu alloy core, with low Cu concentration.

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The paper has discussed the relation between the box-counting and information fractal dimension versus

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grey-level threshold curve, the nanoparticles morphology and the preparation method. Meanwhile lacunarity versus grey level threshold curve has the same behaviour for alloy structure nanoparticles and for core-shell nanoparticles, the “box-counting” fractal dimension versus the grey level threshold curve is

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sensitive to preparation method. Therefore, the core-shell nanoparticles exhibit two-plateaus curves of the

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box-counting fractal dimension versus grey level threshold dependences. The preparation method of the core shell nanoparticles is the key of this behaviour. To obtain the core-shell nanoparticles, there are two

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steps in the preparation method: first, mono-metallic nanoparticles were prepared (Pd, Cu or Ag) and second, another precursor is added to produce, after reducing and nucleation, the core-shell nanoparticles. So, there are two reduction/nucleation processes: one responsible for the core, and the second one, responsible for the shell. The two processes are closely related to the plateaus of the fractal dimension versus grey-level curve. It is straightforward to notice that the number of plateaus of the fractal dimension versus the grey level curve is directly related to the number of nucleation processes occurred. Also, in the paper we described a mathematical method that offers the possibility to compute the fractal dimension of

ACCEPTED MANUSCRIPT every nucleation process D1 (λ1 ) and D2 (λ2 ), the fractal dimension of the core and of the shell, together with the fractal dimension of the entire micrograph D(λ m). The information fractal dimension curves exhibit one-plateau behaviour, explained by the information dimension computing algorithm. The method effectively assigns weights to the boxes in such a way that boxes containing a greater number of points count more than boxes with less points so, the method will be less sensitive to changes caused by different nucleation processes. The method will count an overall

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fractal dimension, with one-plateau behaviour of the information dimension dependence on grey level

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threshold curve, insensitive to preparation method.

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The fractal theory is an important tool to analyse the nanoparticles structure and morphology. It offers information about the preparation process; the fractal dimension dependence on grey-level threshold can

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be related to nucleation processes occurring during preparation.

ACCEPTED MANUSCRIPT References [1] A.R. Deniz, Z. Çaldıran, Ö. Metin, H. Can, K. Meral, Ş. Aydoğan, Schottky diode performance of an Au/Pd/GaAs device fabricated by deposition of monodisperse palladium nanoparticles over a p-type GaAs substrate. Mater. Sci. Semicond. Process., 27(2014) 163–169. [2] H.T. Hien, H.T. Giang, N. Van Hieu, T. Trung, C. Van Tuan, Elaboration of Pd-nanoparticle

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assembled on reduced graphene oxide as highly efficient catalysts for the transfer hydrogenation of nitroarenes, J. Colloid Interface Sci., 498(2017) 378–386. [5] S.A. Nikolaev, E.V. Golubina, M.I. Shilina, The effect of H 2 treatment at 423–573 K on the

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ACCEPTED MANUSCRIPT [9] X. Ma, T. Jiang, B. Han , J. Zhang, S. Miao, K. Ding, G. An, Y. Xie, Y. Zhou, A. Zhu, Palladium nanoparticles in polyethylene glycols: Efficient and recyclable catalyst system for hydrogenation of olefins, Catal.Commun., 9(2008) 70–74. [10] N. Toshima, Y. Wang, Polymer-protected Cu/Pd bimetallic clusters, Adv. Mater., 6(1994) 245247. [11] P. Liu, X. Gu, H. Zhang, J. Cheng, J. Song, H. Su, Visible-light-driven catalytic activity

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[12] S.S. Li, J.J. Lv, Y.Y. Hu, J.N. Zheng, J.R. Chen, A.J. Wang, J.J. Feng, Facile synthesis of porous Pt–Pd nanospheres supported on reduced graphene oxide nanosheets for enhanced methanol

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electro oxidation, J Power Sources, 247(2014) 213-218.

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palladium-gold, Science, 310(2005) 291–293. [15] J. Sá, S. Gross, H.Vinek, Effect of the reducing step on the properties of Pd-Cu bimetallic catalysts used for denitration, Appl. Catal. A, 294(2005) 226–234.

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[17] A.W. Salamon, P. Courtney, I. Shuttler, Nanotechnology and engineered nanomaterials, A primer, 2011, PerkinElmer (Ed.). [18] V.A. Hackley, C.F. Ferraris, The use of nomenclature in dispersion science and technology, NIST Recommended Practice Guide: Special Publication, 2001, 960–963, Washington, DC. [19] B.B. Mandelbrot, 1977, Fractals: Form, Chance and Dimension, Freeman.

ACCEPTED MANUSCRIPT [20] D. Avnir, D. Farin, P. Pfeifer, Molecular fractal surfaces, Nature, 308(1984) 261-263. [21] V. Kanniah, P. Wu, N. Mandzy, E.A. Grulke, Fractal analysis as a complimentary technique for charactering nanoparticle size distribution, Powder Technology, 226(2012) 189-198. [22] X. Guo, K. Gao, A. Gutsche, M. Seipenbusch, H. Nirschl, Combined small- and wide-angle X-ray scattering studies on oxide-supported Pt nanoparticles prepared by a CVS and CVD process, Powder Technology, 272(2015) 23-33.

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nanoparticles by fractal analysis, Powder Technology, 269(2015) 532-540. [26] W.G. Shin, J. Wang, M. Mertler, B. Sachweh, H. Fissan, D.Y.H. Pui, Structural properties of

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material science: the issue of size control, Elsevier, Amsterdam, 2008, pp. 327–340. [28] F. Papa, C. Negrila, A. Miyazaki, I. Balint, Morphology and chemical state of PVP-protected Pt, Pt–Cu, and Pt–Ag nanoparticles prepared by alkaline polyol method, J. Nanopart. Res., 13(2011) 5057–5064. [29] A.L. Barabasi, H.E. Stanley, Fractal concepts in surface growth, 1995, Cambridge University Press.

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459-469.

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[37] G. Bushell, R.Amal, Fractal Aggregates of Polydisperse Particles, J.Coll.& Int.Sci. 205 (1998)

ACCEPTED MANUSCRIPT Curriculum Vitae

Personal information

First name(s) / Surname(s)

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Gianina-Elena Dobrescu Nationality Romanian Date of birth 18.03.1964 Gender female

Desired employment / Occupational field

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Senior Researcher (third class)

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Main activities and responsibilities Fractal theory, Surface science Name and address of employer Romanian Academy, Institute of Physical Chemistry “Ilie Murgulescu” Type of business or sector Research

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Physical-Chemistry Work experience

Education and training

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Dates 1993-2003 Title of qualification awarded PhD Principal subjects / occupational skills covered Physical Chemistry/Applications of Fractal Theory in Adsorption Name and type of organisation providing education and training

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University of Bucharest, Faculty of Chemistry

Name and type of organisation providing education and training University of Bucharest, Faculty of Physics Area of competence

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Europass Curriculum Vitae

Personal information

First name(s) / Surname(s)

Florica Papa Address(es) Calea Floreasca Street, 94, Bucharest , Romania Telephone(s) 0040213121679 Mobile: 0040753029683 Fax(es) 0040213121147 E-mail [email protected] Nationality

ACCEPTED MANUSCRIPT romana Date of birth 28. iulie 1968 Gender female

Desired employment / Occupational field Chemical-Senior Researcher

Work experience

Education and training

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Dates 03-1999 – 10-2006 Title of qualification awarded PhD Principal subjects / occupational skills covered Chemistry Name and type of organisation providing education and training

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Dates 04/ 2008-present Occupation or position held Senior Researcher third degree Main activities and responsibilities Catalysts synthesis, caracterization Name and address of employer Romanian Academy, Institute of Physical Chemistry “Ilie Murgulescu” Type of business or sector Research

Romanian Academy, Institute of Physical Chemistry “Ilie Murgulescu”

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Title of qualification awarded Master Degree Principal subjects / occupational skills covered Catalysis and catalytic proceses Name and type of organisation providing education and training University of Bucharest (Faculty of Chemistry) Bucharest (Romania) Principal subjects / occupational skills covered

Total oxidation of lower alcane over oxide perovskite-type: Oxidative coupling of methane over simple and doped ionic oxides

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CH4 partial oxidation over well-defined metal nanoparticles supported on alumina Selective reduction of nitrate and nitrite ions in liquid phase

Curriculum vitae Europass

Personal Information Name

ACCEPTED MANUSCRIPT State Razvan Nicolae Address Bd. Uverturii 85, bl. O14, sc. 2, ap. 60, BUCURESTI, Romania Phone 0721274801 E-mail [email protected] Nationality Romana Birth Date 23.10.1983 Sex Male

Work Experience

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Period 1.12.2013 - present Job Scientific Researcher Responsibilities Research (preparation and analysis of different samples using different specific techniques (Raman Spectroscopy, DSC, etc.), catalysis techniques and synthesis (HPLC, GC, Chemisorptions); Scientific Secretary at ROMPHYSCHEM 2013, Catalysis Section Name of the Employer „Ilie Murgulescu” Institute of Physical Chemistry of the Romanian Academy Period 1.05.2011 – 31.11.2013 Job Research Assistant Responsibilities Research (preparation and analysis of different samples using different specific techniques (Raman Spectroscopy, DSC, etc.), catalysis techniques and synthesis (HPLC, GC, Chemisorptions); Scientific Secretary at ROMPHYSCHEM 2013, Catalysis Section Name of the Employer „Ilie Murgulescu” Institute of Physical Chemistry of the Romanian Academy

Education

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Period 2008 - 2011 Diploma PhD Description Advanced Ceramics – „Ceramic composite materials with advanced mechanical properties” Institution University Politehnica Bucharest Period Diploma Description Institution 2003-2005; 2006-2008 Engineer FILS, English, Materials Science University Politehnica of Bucharest Period Diploma Description Institution 2005-2006 Courses in the 3rd year of University (Erasmus Student) Scienza dei Materali Universita Politecnico di Torino

ACCEPTED MANUSCRIPT Europass Curriculum Vitae

Personal information

First name(s) / Surname(s)

Desired employment / Occupational field Senior Researcher

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Physical-Chemistry Work experience

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Ioan Balint Address(es) Babesti Street, 8, S 6, Bucharest Telephone(s) 0040213121679 Mobile: 0040746151058 Fax(es) 0040213121147 E-mail [email protected] Nationality Romanian Date of birth 1.06.1957 Gender male

Fellowships Subject

Unesco Fellow Dates

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Dates 2005-present Occupation or position held Senior Researcher (first class) Main activities and responsibilities Catalysis, Surface science, Nanomaterial synthesis, Kinetics and mechanism of non-isothermal desorption of adsorbed gases from metal supported catalysts: Defect chemistry of ionic oxides and zeolites, Catalysts preparation, surface chemistry, catalytic sensor Name and address of employer Romanian Academy, Institute of Physical Chemistry “Ilie Murgulescu” Type of business or sector Research

10.1991 – 08.1992 Organisation

Tokyo Institute of Technology, Department of Natural Resources Use Location

Tokyo, Japan, Subject

Invited professor Dates

10.1997 – 01.1998

ACCEPTED MANUSCRIPT Organisation

Tokyo Institute of Technology, Department of Chemical Engineering and Environmental Location

Tokyo, Japan Subject

post-doctoral fellowship Dates

04.1998 – 01.1999 Organisation

University Pierre et Marie Marie Curie, France, Nanoscale materials and Catalysis Laboratory.

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Location Paris, France Subject

Invited professor Dates

01. 1999 – 05. 2000

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Organisation

Tokyo Institute of Technology, Japan, Department of Chemical Engineering and Environmental

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Location

Tokyo, Japan Subject

post-doctoral fellowship JSPS

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Dates

07. 2000 – 03. 2002 Organisation

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Tokyo Institute of Technology Location

Tokyo, Japan Subject

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Grant in Aid from Scientific Research from the Ministry of Education Culture and Sport, Science and Technology Dates

07. 2002 – 03. 2005

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Organisation

Tokyo Institute of Technology, Japan, Department of Chemical Engineering and Environmental Location

Tokyo, Japan Invited professor Dates

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Subject

11. 2004 -02. 2005 Organisation

Tokyo Technical University (Tokyo Rikka Daigaku) Location

Tokyo, Japan Education and training Dates 1992-1996 Title of qualification awarded PhD Principal subjects / occupational skills covered Chemistry Name and type of organisation providing education and training

Romanian Academy, Institute of Physical Chemistry “Ilie Murgulescu”

ACCEPTED MANUSCRIPT Dates 1981 - 1982 Title of qualification awarded Master Degree Principal subjects / occupational skills covered Physical Chemistry Name and type of organisation providing education and training Polytechnic Institute of Bucharest, Faculty of Chemistry Area of competence

Surface Science: Kinetics and mechanism of non-isothermal desorption of adsorbed gases from metal supported catalysts; Defect chemistry of ionic oxides and zeolites

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Catalysis: Total oxidation of C 1 -C4 paraffins over supported platinic metals:Oxidative coupling of methane over simple and doped ionic oxides: Hydrocarbons conversion over metal supported on zeolite catalysts; NO conversion over supported well-structured metal nanocrystals; CH4 partial oxidation over well-defined metal nanoparticles supported on high surface area oxides;Selective reduction of nitrate and nitrite ions in liquid phase.

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Fig.1 XRD pattern of samples A – H.

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Nanomaterials synthesis:Preparation of nano-size oxides with high surface area and high thermal stability by using microemulsions:Preparation of well-structured metallic and bimetallic nanoparticles

Fig.2 TEM images of monometallic and bimetallic alloy samples A, B, E, F (left), box-counting fractal

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dimension versus grey level threshold curves for samples A, B, E, F (right, up) and box-counting lacunarity versus grey level threshold curves for samples A, B, E, F (right, down).

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Fig.3 TEM images of bimetallic core-shell samples C, D, G, H (left), box-counting fractal dimension versus grey level threshold curves for samples C, D, G, H (right, up) and box-counting lacunarity versus grey level threshold curves for samples C, D, G, H (right, down). Fig.4 Pd-Cu bimetallic nanoparticles, a different image magnifier, inverse core-shell structure. The boxcounting fractal dimension curve has the same two-plateaus behavior. Fig.5 The function T(r,λ) versus r curve for a set of λ from 10 to 250 step 10, for nanoparticles with alloy structure – sample B.

ACCEPTED MANUSCRIPT Fig.6 Definition of λm as the grey-level threshold separating the two plateaus; the two fractal dimensions D1 = D(λ1 ) and D2 = D(λ2 ) describe the self-similarity of the core and of the shell, in other words the two nucleation processes (sample C). Fig.7 The function T(r,λ) versus r curve for a set of λ from 10 to 250 step 10, for nanoparticles with coreshell structure – sample C. Fig.8 a) Information dimension versus grey level threshold curve for samples B (alloy), C (core-shell) and

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D (inverse core-shell); b) Information lacunarity versus grey level threshold curve for samples B, C and

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