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Quantitative analysis of surface morphology: characterization of polypyrrole films aging
Polymer Testing 18 (1999) 217–229
Polymer Characterisation
Quantitative analysis of surface morphology: characterization of polypyrrole films aging G. Merlea,*, A. C. Grilleta, J. Allemandb, D. Lesueura a
Laboratoire Mate´riaux Polyme`res et Composites, Universite´ de Savoie, IUT, 73376 Le Bourget du Lac Cedex, France b Laboratoire d’Instrumentation et de Mate´riaux d’Annecy, Ecole Supe´rieure d’Inge´nieurs d’Annecy, 41 avenue de la Plaine, BP 806 74016 Annecy Cedex, France Received 13 February 1997; accepted 22 April 1998
Abstract The structure and properties of polypyrrole have been studied intensively in the past decade. Polypyrrole microstructure and morphology are quite well described, nevertheless they remain roughly characterized from a quantitative point of view. In a previous work, one of the authors has underlined changes in some physical and microstructural properties which are linked to molecular mobility but the influence of aging on morphology has not been studied. To fill this gap, SEM and AFM observations have been performed. The surface morphology of polypyrrole films looks like ‘cauliflowers’ that are made of clusters aggregated on a large range of scales. Fractal methods have been used to treat quantitatively the pictures: the fractal dimension D (estimated from Minkowski) has a tendency to decrease for the aged material viewed by SEM as well as by AFM. Pseudo Fractality Graphs (the variation of D along the scales) show that the difference between virgin and aged polypyrrole is higher for the largest scales. The topography analysis is compared by using AFM and SEM pictures. 1999 Elsevier Science Ltd. All rights reserved.
1. Introduction Intrinsically conducting polymers have attracted much attention as candidate materials for useful applications like, for example, batteries, sensors, electromagnetic shielding.
* Corresponding author. 0142-9418/99/$ - see front matter 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 1 4 2 - 9 4 1 8 ( 9 8 ) 0 0 0 2 7 - 0
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G. Merle et al. / Polymer Testing 18 (1999) 217–229
Usually, electricity conduction in polymers is obtained through doping special molecular structures, for example conjugated bonds. Generally, the conduction properties appear to correspond to the effect of an assembly of regions having different properties. One of the main parameters for conduction is, in this way, the molecular structure and the superstructure for charges motion. Since the first electrochemical synthesis of polypyrrole films in 1979 [1], interest has been focused on this polymer due to its good properties, i.e. mechanical stiffness, stability and electrical conductivity which are higher in polypyrrole than in other conductive polymers. Moreover it is easy to produce films. The structure and properties of polypyrrole have been studied intensively in the past decade. Although the effects of many physical and chemical parameters of synthesis have been investigated, polypyrrole microstructure remains approximately the same, whatever the process. However, the general aspect of the surface morphology can vary, depending on the electrochemical conditions of the synthesis [2]. SAXS or WAXS experiments usually show that polypyrrole exhibits a more or less disordered molecular organization depending on the doping anion nature [3–5]. Recently, it has also been proposed that the doping anion steric hindrance influences molecular mobility in polypyrrole. So the glass transition Tg is respectively located at 100 and 175°C for p-toluene sulfonate and anthraquinone sulfonate anions [6]. Polypyrrole surface morphology has also been investigated by SEM or TEM techniques [7–11]. It is generally described as nodular. More precisely, Ishizu et al. have shown that the pyrrole oxidation polymerization in water can produce microspheres about 120 nm in size, forming lattices with a body centered cubic structure [10]. However, the morphology appears generally as a complex feature resulting from electrochemical aggregation. Scanning tunneling or atomic force microscopy (STM, AFM) are now attractive techniques for surface studies, and have begun to be used for polypyrrole [2,12]. Thus, Froeck et al. have described the surface as made of polymeric nodules or islands having extremely different sizes [2]. For batteries applications, the analogy can be made between polypyrrole surface morphology and the fractal morphology of electrodes which is a fundamental parameter for efficiency [13]. Besides, the morphology of electrolytic deposits, which often show very special features, has motivated the development of the Diffusion Limited Aggregation model to describe it [14] and remains as a classical model of fractal structures. A remarkable illustration can be found in the sequence of AFM images taken at the beginning of the electrochemical deposition of polypyrrole [12]. Polypyrrole properties go under severe degradation when submitted to thermal or thermo-oxidative aging. The evolution of conductivity during aging in various conditions is easy to study and can be correlated to microstructural changes like, for example, Tg increase. This could indicate the occurrence of crosslinking reactions in polypyrrole during aging [6]. These microstructural evolution’s are expected to be accompanied by morphological modifications. More precisely, after observations of the aging effect on the molecular mobility, annealing is also expected to have an effect on the morphology. Rearrangements and segregation’s similar to the ones of the heterogeneous inorganic materials could occur, resulting in modifications of the surface roughness. Besides, it has been observed [2] that the redox conditions greatly affect the surface morphology of polypyrrole. Finally, in agreement with Li et al. [12], we can suggest that the surface morphology plays an important role in electron transport.
G. Merle et al. / Polymer Testing 18 (1999) 217–229
219
This contribution deals with the characterization of polypyrrole aging by morphological quantitative analysis methods at supramolecular and microscopic scales. 2. Experimental 2.1. Films synthesis and properties Polypyrrole film doped with p-toluene sulfonate anions was electrochemically synthesized using a EG and G Princeton Applied Research Model 273A Potentiostat. Working electrode and counter electrode were both made of stainless steel (40 ⫻ 40 mm) and the potential was measured vs a saturated calomel electrode (SCE). Aqueous solutions containing 0.05 mol/l of pyrrole (Aldrich, vacuum distilled) and 0.05 mol/l of doping (Aldrich, used as received) were electrolyzed at 25°C under constant voltage (0.7 V/SCE) until a fixed charge (40 C) had passed. At the end of the synthesis, the polypyrrole film was washed under distilled water and dried in vacuum for 24 h at ambient temperature. The DC electrical conductivity of an as prepared polypyrrole film was measured by the four probe technique and is 45 ⫾ 2 S/cm. Film thickness is 25 ⫾ 5 m. 2.2. SEM technique The polypyrrole surface has been analyzed by SEM using a JSM840A (JEOL) instrument and Kevex Advanced Digital Imaging software. 2.3. AFM technique Polypyrrole surface observations were also performed by atomic force microscopy (AFM) in contact mode using an Explorer scanning probe microscope (Topometrix Company, Santa Clara, CA, USA). Topographical images were recorded under ambient conditions using commercial pyramidal silicon nitride tips. The contact force was kept below 1.2 nN, because a higher value could erase the details. The side dimension of the scanned sample surface was in the 10–150 m range. 2.4. Image processing The pictures obtained by both microscopes show surfaces as ‘cauliflowers’, that are made of clusters aggregated on a large range of scales, as mentioned in literature [2,11]. The aging does not affect the general aspect of the pictures, only slight differences are noticeable. Thus, we have to use quantitative measurement, precise and accurate enough to distinguish such complex morphology. Because of the aspect of the surfaces pictures resulting from electrochemical particles aggregation, fractal methods seem to be the most obvious treatment for image processing. A theoretical fractal object is a geometrical thing with an extremely branched or aggregated shape. This shape has the same structure whatever the observation scale may be. A fractal curve
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is longer as the unit taken to measure is smaller. Such curve is characterized by a non-integer dimension D all the nearer to 2 as the curve tends more to occupy all the plane. According to Le Me´haute´ [13], a fractal is a set characterized by a scaling law as: L 苲 1 ⫺ D, where is the value of the measure unit, L is the value of the curve measured with the unit and D is the fractal dimension of the set. The fractal dimension can be experimentally evaluated through the Minkowski dimension, which is defined by using the covering of a set by balls. The method for curves is given by Coster et al. [15]. The pictures are digitized into gray scale images files. The SEM images (384x512x256) and AFM images (500x500x256) are analyzed without any prior treatment (the first two dimensions are spatial, the third is the gray shade). 2.4.1. Binary images The clusters that can be seen on the images were simply segmented by an adaptive thresholding method. The method automatically searches the threshold between the two modes of the gray shades histogram, section by section (8x8 pixels sections), so flattening out is not required. This method is recommended for quantitative morphology [16]. Then, binary images of clusters are treated by classical granulometry methods [15]. In particular, the nearest neighbor distances (FND) have been measured. In addition, we compute the average of the reciprocal distance (the ‘proximity’ of the first neighbor FNP): if the distribution is monodisperse, the product FNDxFNP equals 1; the broader the distribution, the higher is this value. 2.4.2. Gray scale images The gray shade which represents actually the height in the case of AFM topographic images is used in the same way for SEM images. In the SEM technique, the gray shade has a complex meaning, including local height and local slope. Using the SEM technique, Pande et al. [17]. have shown that the total area of a rough surface is proportional to the secondary electrons yield from this surface. We apply that to the surface covered by each pixel, so the gray shade, considered as the height, is the third topographical dimension. Despite the physical meaning of the gray shade, its study, with variation of the resolution, allows the characterization of the fractal behavior of an image considered as a landscape [18]. The consistency of such a treatment is discussed in this paper: the same samples are studied by AFM and SEM, and the behaviors obtained, using the two kinds of images, are compared. The fractal behavior of the images is studied by the method of successive dilation’s [18]. In this process several image resolutions and several image magnifications can be combined. The gray scale image is used as a set X, and we compute the Minkowski dimension (D) of this set embedded in a usual space, that is to say the 3D space. As the space is covered by voxels, the adapted unit of measurement is linked to volumes. The first step consists in a transformation of the initial surface (represented by voxels more or less separated) into a volume which is a continuous set. For that, each voxel of the top of the surface is vertically thickened until it includes all voxels below it, up to the lowest of its four surface voxel neighbors, which constitutes the set X.
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Our procedure differs from the ones most often used in the case of 3D shapes. Generally they consist of working in 2D space after cutting the surface by a plane. This plane is either perpendicular to the image basis, giving a so-called image profile analyzed as a curve, or parallel, producing ‘islands’. The classical islands method (implemented in commercial microscopes) is based on the linear behavior log(perimeter length) vs log(area) of the set of ‘islands’ created in this plane, but it is not accurate enough because it takes into account only a part of the surface. Generally, each slice yields its own value, different from each other, and so the results are too much scattered. Moreover the same resolution is taken for small and large islands. Our method processes all the surface as a whole. Then the change in resolution is achieved through the dilation of the set we study. Let us denote M(X) the measure of the dilated set of X using balls with radius . We compute: D⫽
lim
冋
→0
n⫺
册
ln[M(X)] ln
where n ⫽ 3 according to the 3D space we are working in. Of course will only take integer values (because we compute discrete sets, the minimum length is one pixel). The computation of the limit is possible because ln[M(X)] varies in a linear manner with ln . This behavior proves that the image is a fractal. The fractal dimension is deduced from the slope of the straight line fitting the points (ln , ln[M(X)]). Actually, the graphs obtained are not perfectly linear and the image of the natural phenomenon studied is not mathematically fractal, which often appears in physics [13]. Consequently two ways can be considered: on one hand we consider that the average value of the fractal dimension is sufficient, or on the other hand we propose to use a Pseudo Fractality Graph (PFG). This graph represents the variation of the value of D (the slope of the curve ln[M(X)] vs ln ) along the studied scale range. Moreover, we group together several images, in order to improve the accuracy statistically and, by using images taken at different magnifications, to extend the analysis scale, which is important in fractal analysis. The method used to gather on the same PFG the results from images taken at different magnifications consists in choosing a reference magnification G0, and shifting the results obtained at the G magnification along the abscissa logarithmic scale with the shift factor log(a) ⫽ log(G/G0). (More precisely, on the graphs shown in this paper, the zero value on the abscissa scale corresponds to the first dilation step made on a 50 m image, with a resolution of 100 nm by pixel). So, the PFG obtained combines the two ways to characterize the fractal behavior, that is resolution and magnification changes.
3. Results 3.1. SEM In order to check out if aging induces phase segregation or doping migration, distribution cartography of C, O and S atoms has been investigated. As already described [19], whether
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polypyrrole film is aged or virgin, no significant contrast is obtained, even at resolution up to 9 ⫻ 12 m images. Thus, no migration or segregation is detectable. Secondary electron image of the growth surface of polypyrrole film is shown in Fig. 1. The typical ‘cauliflower’ like nodular structure of electrochemically synthesized polypyrrole films is observed [7–9]. As sometimes reported in literature, effects of aging on the nodular structure are not really visible. The general appearance of the surface only seems to be less ‘fresh’. Here and there, by careful examination along the samples, a tendency appears for tracks to be ready for fissure propagation. Moreover, as mentioned by Moss et al. [11], some cracks that could result from thermal strains are observed on the edges of the most aged samples. The fractal behavior of these images is well characterized. The calculated values of D are the same for images from 12 to 60 mm long and a little more for 300 mm long images. As it can be seen in Table 1, the consequence of aging could be a small decrease of D.
Fig. 1. SEM images of polypyrrole films: secondary electron images at 5 kV. Side length 60 m: (a) virgin; (b) aged. Side length 12 m: (c) virgin; (d) aged.
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Table 1 Average fractal dimensions D
Virgin Aged
D (MEB)
D (AFM)
2.64, S.D. 0.04 (4) 2.61, S.D. 0.04 (8)
2.28, S.D. 0.09 (10) 2.21, S.D. 0.08 (15)
S.D.: standard deviation (in brackets: number of processed images).
3.2. AFM The same kind of landscape is obtained by AFM, as can be seen in Fig. 2. As a result of aging, slight reorganization of clusters is observed. Initial clusters bulk in bigger but less rough new clusters. This is noticeable on the image profiles. A first treatment can be made, using roughness measurements, according to Froeck et al. [2]. However, as expected, the roughness measure depends on the image magnification. The variation of the difference Z between the highest point to the lowest varies in respect to the size of the image. This is the simplest way to check the behavior of a surface: the slope of the graph of log(Z) vs log(image size) is below 1 for a fractal surface, i.e. the variation of roughness is not proportional to the image length.
Fig. 2.
AFM images of polypyrrole films. Side length 100 m: (a) virgin; (b) aged.
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This fact is illustrated Fig. 3. Unfortunately, virgin or aged surfaces cannot be distinguished from each other. To investigate precisely the effects of aging, AFM images have been treated in binary mode and then in gray scale mode. 3.2.1. Binary images In order to measure the clusters evolution’s, a segmentation is necessary. Even using an adaptive thresholding, it is not a simple operation on a fractal image because clusters are aggregated. In Fig. 4, the same treatment has been applied to compare two images. One drawback for such an arbitrary method is, as can be seen in Fig. 4, that it generates artificial straight lines (interference between the 8 ⫻ 8 pixels sections, see upper panel). However, the regions appearing in contrasted relief in their direct neighborhood are well detected. Measurements of surfaces and distances is then possible. As a matter of fact, aging has induced a decrease of the average surface from 237 to 183 pixels which means a decrease in contrasted relief. Statistics from nearest neighbors distances analysis are reported in Table 2. Comparison between minimum and maximum values shows that clusters tend to move away from each other. After aging, whereas average distance is shorter, contradictorily the average proximity is weaker. It is due to the narrower distribution as illustrated by the standard deviation, the product FND ⫻ FNP, and the histogram of nearest neighbors distances (Fig. 5), which shows that weaker distances have disappeared for aged polypyrrole. It seems that a kind of rearrangement has occurred, with clusters grouping together and, at the same time, disappearance of the shortest distances: by eye it is noticeable, but binary image processing is not very well adapted for that, due to the complex morphology of the surfaces investigated.
Fig. 3. Log–log non integer correspondence between roughness (Z) and image size.
G. Merle et al. / Polymer Testing 18 (1999) 217–229
Fig. 4.
225
Binary images of the clusters (adaptive thresholding of the images seen Fig. 2).
Table 2 Analysis of the cluster distances
Virgin Aged
FND
S.D.
3.5 3.3
1 0.9
Min
0.27 1.4
Max
FNP
FND × FNP
7.2 6.8
0.34 0.33
1.18 1.06
Counts
374 405
FND: average first neighbor distance (m); S.D.: standard deviation; Min: minimum value; Max: maximum value; FNP: average first neighbor reciprocal distance (m−1).
3.2.2. Gray scale images AFM interest is the obtention of real topographic images from sample surfaces. The process used for SEM images is then applied, using a greater number of images in order to have better statistics. As shown in Table 1, the same tendency in decreasing the average fractal dimension after aging is noticed, the difference being larger than for SEM images, but the standard deviation is longer too. If we have a better measurement of topography, better statistics together with a larger deviation, another phenomenon has to be considered, that is the fractal dimension variation along the scale, i.e. the pseudo fractal behavior. To study the fractal behavior in order to check the constancy or the variation of the D value,
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Fig. 5. Effect of aging on the clusters first neighbor distances distribution showing the disappearance of the shorter distances.
we have drawn the Pseudo Fractality Graph which is the values of fractal dimension, D, at the corresponding scales, using all images processed whatever the magnification (Fig. 6). Over two decades of scales, the pseudo-fractal behavior of the surface is described by such a PFG. Considering that results from different samples and different magnifications are grouped
Fig. 6. Virgin polypyrrole PFG from 10 AFM images having side length from 10 to 151 m.
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together, the scatter is not important. The higher fractal dimension means that the surface tends to be more irregular at higher scales. The same PFG made with aged samples (Fig. 7), shows two different points. On one hand, the results seem to be more scattered. On the other hand, a slight decrease in the value of the fractal dimension at higher scales is observed. This could correspond to the slight smoothing observed visually. Note that it is not contradictory to the values of standard deviation (S.D.) obtained by computing an average value of D (see Table 1), because a flattening of the PFG results in a decreased S.D. This explains the apparent abnormal effects comparing AFM and SEM results, as previously discussed. To complete the results, PFG from SEM pictures of polypyrrole surfaces have been computed, and are presented on Figs. 8 and 9, for virgin and aged polypyrrole, respectively. The same comment can be made about the results dispersion, which increases after aging. Also, note that the PFG are more ‘flat’ than in the case of AFM pictures, which explains the lower value of S.D. in Table 1. In addition, the values of D are higher. This point has to be related to the physical meaning of the third dimension of SEM images (see upper panel), which also encompasses edges and shading effects. As a result, the image contrast is higher, which tends to increase D. Moreover, even carefully using the same parameters for all the pictures, on one hand the scatter is higher, on the other hand the curves obtained from images at different magnifications are not well superimposed. Due to the scatter, the effect of aging cannot be distinguished comparing Figs. 8 and 9, while Figs. 6 and 7 show a difference for the higher scales.
Fig. 7.
Aged polypyrrole PFG from 15 AFM images having side length from 10 to 151 m.
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Fig. 8. Virgin polypyrrole PFG from four SEM images having side length from 12 to 300 m).
Fig. 9.
Aged polypyrrole PFG from eight SEM images (side length from 12 to 300 m).
4. Conclusion By way of image processing, quantitative morphology analysis becomes a tool, illustrated in this paper to characterize the aging evolution of polypyrrole surfaces. In a first step, the complex architecture of the polypyrrole surfaces has been quantitatively described, over a large range of scales (two decades). Secondly, changes in morphology have been detected after aging, like gathering of aggregates and slight smoothing of the surface irregularity. Moreover, these variations are better quantified by AFM than by SEM. AFM is more suitable for using different magnifications and the physical meaning of the measurement is more simple.
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Nevertheless, despite their complex physical meaning, SEM pictures can be used to study the fractal behavior of surfaces, but more for relative comparisons between samples from the same series than for absolute determinations. Further experiments are in progress to quantify morphological evolution’s in relation to microstructural and electrical aging of polypyrrole in various atmospheres. References [1] Diaz AF, Keiji Kanazawa K, Gardini GP. Electrochemical polymerisation of pyrrole. Journal of the Chemical Society, Chemistry Communications 1979;0:635–6. [2] Froeck C, Bartl A, Dunsch L. STM- and AFM-investigations of one- and two-dimensional polypyrrole structures on electrodes. Electrochimica Acta 1995;40(10):1421–5. [3] Cheung KM, Bloor D, Stevens GC. The influence of unusual counterions on the electrochemistry and physical properties of polypyrrole. Journal of Materials Science 1990;25:3814–37. [4] Otero TF, De Larreta E. Electrochemical control of the morphology, adherence, appearance and growth of polypyrrole films. Synthetic Metals 1988;26:79–88. [5] Sutton SJ, Vaughan AS. On the growth of polypyrrole from solutions of methanol and water. Synthetic Metals 1993;58:391–402. [6] Lesueur D. Etude de la mobilite´ mole´culaire dans le polypyrrole par spectrome´trie me´canique dynamique. Application: relation microstructure–durabilite´ des proprie´te´s de conduction. Ph.D. thesis, Universite´ de Savoie, France, 1996. [7] Bloor D, Monkman AP, Stevens GC, Cheung KM, Pugh S. Structure–property relationships in conductive polymers. Molecule Crystal and Liquid Crystallography 1990;187:231–9. [8] Montemayor MC, Vazquez L, Fata´s E. Morphological model of the electrical anisotropy of a conducting polypyrrole. Physics 1994;75:1849–51. [9] Yang R, Dalsin KM, Evans DF, Christensen L, Hendrickson WA. Scanning tunneling microscopic imaging of electropolymerised doped polypyrrole. Visual imaging of semicrystalline and helical nascent polymer growth. Journal of Physical Chemistry 1989;93:511–2. [10] Ishizu K, Honda K. Architecture of polymeric superstructures with polypyrrole spherical lattices. Polymer 1997;38(3):689–93. [11] Moss BK, Burford RP. A kinetic study of polypyrrole degradation. Polymer 1992;33:1902–8. [12] Li J, Wang E, Green M, West PE. In situ AFM study of the surface morphology of polypyrrole film. Synthetic Metals 1995;74:127–31. [13] Le Me´haute´ A. Les ge´ome´tries fractales. Herme`s, Paris, 1990. [14] Sander LM. Fractal growth processes. Nature 1986;322:789–93. [15] Coster M, Chermant JL. Pre´cis d’analyse d’images. Editions du CNRS, Paris, 1985. [16] Nishi T, Ikehara T. Morphology quantification of multiphase polymers. VAMAS report No. 26, ISSN 10162186, 1997. [17] Pande CS, Loaut N, Masumara RA, Smith S. Fractal characterization of rough surfaces using secondary electrons. Philosophical Magazine Letters 1987;55(3):99–104. [18] Boulec¸ane H, Vincent N, Ruffier M, Emptoz H. Control of composite material structure by fractal methods. Lecture Notes in Computer Science, No. 719: Computer Analysis of Images and Patterns. In: Dmitry Chetverikov, Walter G. Kropatsch, editors. Berlin: Springer-Verlag, 1993. p.726–31. [19] Vautrin M. Stabilite´ thermique du polypyrrole. Ph.D. thesis, Universite´ Paris 7, 1996.
Polymer Characterisation
Quantitative analysis of surface morphology: characterization of polypyrrole films aging G. Merlea,*, A. C. Grilleta, J. Allemandb, D. Lesueura a
Laboratoire Mate´riaux Polyme`res et Composites, Universite´ de Savoie, IUT, 73376 Le Bourget du Lac Cedex, France b Laboratoire d’Instrumentation et de Mate´riaux d’Annecy, Ecole Supe´rieure d’Inge´nieurs d’Annecy, 41 avenue de la Plaine, BP 806 74016 Annecy Cedex, France Received 13 February 1997; accepted 22 April 1998
Abstract The structure and properties of polypyrrole have been studied intensively in the past decade. Polypyrrole microstructure and morphology are quite well described, nevertheless they remain roughly characterized from a quantitative point of view. In a previous work, one of the authors has underlined changes in some physical and microstructural properties which are linked to molecular mobility but the influence of aging on morphology has not been studied. To fill this gap, SEM and AFM observations have been performed. The surface morphology of polypyrrole films looks like ‘cauliflowers’ that are made of clusters aggregated on a large range of scales. Fractal methods have been used to treat quantitatively the pictures: the fractal dimension D (estimated from Minkowski) has a tendency to decrease for the aged material viewed by SEM as well as by AFM. Pseudo Fractality Graphs (the variation of D along the scales) show that the difference between virgin and aged polypyrrole is higher for the largest scales. The topography analysis is compared by using AFM and SEM pictures. 1999 Elsevier Science Ltd. All rights reserved.
1. Introduction Intrinsically conducting polymers have attracted much attention as candidate materials for useful applications like, for example, batteries, sensors, electromagnetic shielding.
* Corresponding author. 0142-9418/99/$ - see front matter 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 1 4 2 - 9 4 1 8 ( 9 8 ) 0 0 0 2 7 - 0
218
G. Merle et al. / Polymer Testing 18 (1999) 217–229
Usually, electricity conduction in polymers is obtained through doping special molecular structures, for example conjugated bonds. Generally, the conduction properties appear to correspond to the effect of an assembly of regions having different properties. One of the main parameters for conduction is, in this way, the molecular structure and the superstructure for charges motion. Since the first electrochemical synthesis of polypyrrole films in 1979 [1], interest has been focused on this polymer due to its good properties, i.e. mechanical stiffness, stability and electrical conductivity which are higher in polypyrrole than in other conductive polymers. Moreover it is easy to produce films. The structure and properties of polypyrrole have been studied intensively in the past decade. Although the effects of many physical and chemical parameters of synthesis have been investigated, polypyrrole microstructure remains approximately the same, whatever the process. However, the general aspect of the surface morphology can vary, depending on the electrochemical conditions of the synthesis [2]. SAXS or WAXS experiments usually show that polypyrrole exhibits a more or less disordered molecular organization depending on the doping anion nature [3–5]. Recently, it has also been proposed that the doping anion steric hindrance influences molecular mobility in polypyrrole. So the glass transition Tg is respectively located at 100 and 175°C for p-toluene sulfonate and anthraquinone sulfonate anions [6]. Polypyrrole surface morphology has also been investigated by SEM or TEM techniques [7–11]. It is generally described as nodular. More precisely, Ishizu et al. have shown that the pyrrole oxidation polymerization in water can produce microspheres about 120 nm in size, forming lattices with a body centered cubic structure [10]. However, the morphology appears generally as a complex feature resulting from electrochemical aggregation. Scanning tunneling or atomic force microscopy (STM, AFM) are now attractive techniques for surface studies, and have begun to be used for polypyrrole [2,12]. Thus, Froeck et al. have described the surface as made of polymeric nodules or islands having extremely different sizes [2]. For batteries applications, the analogy can be made between polypyrrole surface morphology and the fractal morphology of electrodes which is a fundamental parameter for efficiency [13]. Besides, the morphology of electrolytic deposits, which often show very special features, has motivated the development of the Diffusion Limited Aggregation model to describe it [14] and remains as a classical model of fractal structures. A remarkable illustration can be found in the sequence of AFM images taken at the beginning of the electrochemical deposition of polypyrrole [12]. Polypyrrole properties go under severe degradation when submitted to thermal or thermo-oxidative aging. The evolution of conductivity during aging in various conditions is easy to study and can be correlated to microstructural changes like, for example, Tg increase. This could indicate the occurrence of crosslinking reactions in polypyrrole during aging [6]. These microstructural evolution’s are expected to be accompanied by morphological modifications. More precisely, after observations of the aging effect on the molecular mobility, annealing is also expected to have an effect on the morphology. Rearrangements and segregation’s similar to the ones of the heterogeneous inorganic materials could occur, resulting in modifications of the surface roughness. Besides, it has been observed [2] that the redox conditions greatly affect the surface morphology of polypyrrole. Finally, in agreement with Li et al. [12], we can suggest that the surface morphology plays an important role in electron transport.
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219
This contribution deals with the characterization of polypyrrole aging by morphological quantitative analysis methods at supramolecular and microscopic scales. 2. Experimental 2.1. Films synthesis and properties Polypyrrole film doped with p-toluene sulfonate anions was electrochemically synthesized using a EG and G Princeton Applied Research Model 273A Potentiostat. Working electrode and counter electrode were both made of stainless steel (40 ⫻ 40 mm) and the potential was measured vs a saturated calomel electrode (SCE). Aqueous solutions containing 0.05 mol/l of pyrrole (Aldrich, vacuum distilled) and 0.05 mol/l of doping (Aldrich, used as received) were electrolyzed at 25°C under constant voltage (0.7 V/SCE) until a fixed charge (40 C) had passed. At the end of the synthesis, the polypyrrole film was washed under distilled water and dried in vacuum for 24 h at ambient temperature. The DC electrical conductivity of an as prepared polypyrrole film was measured by the four probe technique and is 45 ⫾ 2 S/cm. Film thickness is 25 ⫾ 5 m. 2.2. SEM technique The polypyrrole surface has been analyzed by SEM using a JSM840A (JEOL) instrument and Kevex Advanced Digital Imaging software. 2.3. AFM technique Polypyrrole surface observations were also performed by atomic force microscopy (AFM) in contact mode using an Explorer scanning probe microscope (Topometrix Company, Santa Clara, CA, USA). Topographical images were recorded under ambient conditions using commercial pyramidal silicon nitride tips. The contact force was kept below 1.2 nN, because a higher value could erase the details. The side dimension of the scanned sample surface was in the 10–150 m range. 2.4. Image processing The pictures obtained by both microscopes show surfaces as ‘cauliflowers’, that are made of clusters aggregated on a large range of scales, as mentioned in literature [2,11]. The aging does not affect the general aspect of the pictures, only slight differences are noticeable. Thus, we have to use quantitative measurement, precise and accurate enough to distinguish such complex morphology. Because of the aspect of the surfaces pictures resulting from electrochemical particles aggregation, fractal methods seem to be the most obvious treatment for image processing. A theoretical fractal object is a geometrical thing with an extremely branched or aggregated shape. This shape has the same structure whatever the observation scale may be. A fractal curve
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is longer as the unit taken to measure is smaller. Such curve is characterized by a non-integer dimension D all the nearer to 2 as the curve tends more to occupy all the plane. According to Le Me´haute´ [13], a fractal is a set characterized by a scaling law as: L 苲 1 ⫺ D, where is the value of the measure unit, L is the value of the curve measured with the unit and D is the fractal dimension of the set. The fractal dimension can be experimentally evaluated through the Minkowski dimension, which is defined by using the covering of a set by balls. The method for curves is given by Coster et al. [15]. The pictures are digitized into gray scale images files. The SEM images (384x512x256) and AFM images (500x500x256) are analyzed without any prior treatment (the first two dimensions are spatial, the third is the gray shade). 2.4.1. Binary images The clusters that can be seen on the images were simply segmented by an adaptive thresholding method. The method automatically searches the threshold between the two modes of the gray shades histogram, section by section (8x8 pixels sections), so flattening out is not required. This method is recommended for quantitative morphology [16]. Then, binary images of clusters are treated by classical granulometry methods [15]. In particular, the nearest neighbor distances (FND) have been measured. In addition, we compute the average of the reciprocal distance (the ‘proximity’ of the first neighbor FNP): if the distribution is monodisperse, the product FNDxFNP equals 1; the broader the distribution, the higher is this value. 2.4.2. Gray scale images The gray shade which represents actually the height in the case of AFM topographic images is used in the same way for SEM images. In the SEM technique, the gray shade has a complex meaning, including local height and local slope. Using the SEM technique, Pande et al. [17]. have shown that the total area of a rough surface is proportional to the secondary electrons yield from this surface. We apply that to the surface covered by each pixel, so the gray shade, considered as the height, is the third topographical dimension. Despite the physical meaning of the gray shade, its study, with variation of the resolution, allows the characterization of the fractal behavior of an image considered as a landscape [18]. The consistency of such a treatment is discussed in this paper: the same samples are studied by AFM and SEM, and the behaviors obtained, using the two kinds of images, are compared. The fractal behavior of the images is studied by the method of successive dilation’s [18]. In this process several image resolutions and several image magnifications can be combined. The gray scale image is used as a set X, and we compute the Minkowski dimension (D) of this set embedded in a usual space, that is to say the 3D space. As the space is covered by voxels, the adapted unit of measurement is linked to volumes. The first step consists in a transformation of the initial surface (represented by voxels more or less separated) into a volume which is a continuous set. For that, each voxel of the top of the surface is vertically thickened until it includes all voxels below it, up to the lowest of its four surface voxel neighbors, which constitutes the set X.
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Our procedure differs from the ones most often used in the case of 3D shapes. Generally they consist of working in 2D space after cutting the surface by a plane. This plane is either perpendicular to the image basis, giving a so-called image profile analyzed as a curve, or parallel, producing ‘islands’. The classical islands method (implemented in commercial microscopes) is based on the linear behavior log(perimeter length) vs log(area) of the set of ‘islands’ created in this plane, but it is not accurate enough because it takes into account only a part of the surface. Generally, each slice yields its own value, different from each other, and so the results are too much scattered. Moreover the same resolution is taken for small and large islands. Our method processes all the surface as a whole. Then the change in resolution is achieved through the dilation of the set we study. Let us denote M(X) the measure of the dilated set of X using balls with radius . We compute: D⫽
lim
冋
→0
n⫺
册
ln[M(X)] ln
where n ⫽ 3 according to the 3D space we are working in. Of course will only take integer values (because we compute discrete sets, the minimum length is one pixel). The computation of the limit is possible because ln[M(X)] varies in a linear manner with ln . This behavior proves that the image is a fractal. The fractal dimension is deduced from the slope of the straight line fitting the points (ln , ln[M(X)]). Actually, the graphs obtained are not perfectly linear and the image of the natural phenomenon studied is not mathematically fractal, which often appears in physics [13]. Consequently two ways can be considered: on one hand we consider that the average value of the fractal dimension is sufficient, or on the other hand we propose to use a Pseudo Fractality Graph (PFG). This graph represents the variation of the value of D (the slope of the curve ln[M(X)] vs ln ) along the studied scale range. Moreover, we group together several images, in order to improve the accuracy statistically and, by using images taken at different magnifications, to extend the analysis scale, which is important in fractal analysis. The method used to gather on the same PFG the results from images taken at different magnifications consists in choosing a reference magnification G0, and shifting the results obtained at the G magnification along the abscissa logarithmic scale with the shift factor log(a) ⫽ log(G/G0). (More precisely, on the graphs shown in this paper, the zero value on the abscissa scale corresponds to the first dilation step made on a 50 m image, with a resolution of 100 nm by pixel). So, the PFG obtained combines the two ways to characterize the fractal behavior, that is resolution and magnification changes.
3. Results 3.1. SEM In order to check out if aging induces phase segregation or doping migration, distribution cartography of C, O and S atoms has been investigated. As already described [19], whether
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polypyrrole film is aged or virgin, no significant contrast is obtained, even at resolution up to 9 ⫻ 12 m images. Thus, no migration or segregation is detectable. Secondary electron image of the growth surface of polypyrrole film is shown in Fig. 1. The typical ‘cauliflower’ like nodular structure of electrochemically synthesized polypyrrole films is observed [7–9]. As sometimes reported in literature, effects of aging on the nodular structure are not really visible. The general appearance of the surface only seems to be less ‘fresh’. Here and there, by careful examination along the samples, a tendency appears for tracks to be ready for fissure propagation. Moreover, as mentioned by Moss et al. [11], some cracks that could result from thermal strains are observed on the edges of the most aged samples. The fractal behavior of these images is well characterized. The calculated values of D are the same for images from 12 to 60 mm long and a little more for 300 mm long images. As it can be seen in Table 1, the consequence of aging could be a small decrease of D.
Fig. 1. SEM images of polypyrrole films: secondary electron images at 5 kV. Side length 60 m: (a) virgin; (b) aged. Side length 12 m: (c) virgin; (d) aged.
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Table 1 Average fractal dimensions D
Virgin Aged
D (MEB)
D (AFM)
2.64, S.D. 0.04 (4) 2.61, S.D. 0.04 (8)
2.28, S.D. 0.09 (10) 2.21, S.D. 0.08 (15)
S.D.: standard deviation (in brackets: number of processed images).
3.2. AFM The same kind of landscape is obtained by AFM, as can be seen in Fig. 2. As a result of aging, slight reorganization of clusters is observed. Initial clusters bulk in bigger but less rough new clusters. This is noticeable on the image profiles. A first treatment can be made, using roughness measurements, according to Froeck et al. [2]. However, as expected, the roughness measure depends on the image magnification. The variation of the difference Z between the highest point to the lowest varies in respect to the size of the image. This is the simplest way to check the behavior of a surface: the slope of the graph of log(Z) vs log(image size) is below 1 for a fractal surface, i.e. the variation of roughness is not proportional to the image length.
Fig. 2.
AFM images of polypyrrole films. Side length 100 m: (a) virgin; (b) aged.
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This fact is illustrated Fig. 3. Unfortunately, virgin or aged surfaces cannot be distinguished from each other. To investigate precisely the effects of aging, AFM images have been treated in binary mode and then in gray scale mode. 3.2.1. Binary images In order to measure the clusters evolution’s, a segmentation is necessary. Even using an adaptive thresholding, it is not a simple operation on a fractal image because clusters are aggregated. In Fig. 4, the same treatment has been applied to compare two images. One drawback for such an arbitrary method is, as can be seen in Fig. 4, that it generates artificial straight lines (interference between the 8 ⫻ 8 pixels sections, see upper panel). However, the regions appearing in contrasted relief in their direct neighborhood are well detected. Measurements of surfaces and distances is then possible. As a matter of fact, aging has induced a decrease of the average surface from 237 to 183 pixels which means a decrease in contrasted relief. Statistics from nearest neighbors distances analysis are reported in Table 2. Comparison between minimum and maximum values shows that clusters tend to move away from each other. After aging, whereas average distance is shorter, contradictorily the average proximity is weaker. It is due to the narrower distribution as illustrated by the standard deviation, the product FND ⫻ FNP, and the histogram of nearest neighbors distances (Fig. 5), which shows that weaker distances have disappeared for aged polypyrrole. It seems that a kind of rearrangement has occurred, with clusters grouping together and, at the same time, disappearance of the shortest distances: by eye it is noticeable, but binary image processing is not very well adapted for that, due to the complex morphology of the surfaces investigated.
Fig. 3. Log–log non integer correspondence between roughness (Z) and image size.
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Fig. 4.
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Binary images of the clusters (adaptive thresholding of the images seen Fig. 2).
Table 2 Analysis of the cluster distances
Virgin Aged
FND
S.D.
3.5 3.3
1 0.9
Min
0.27 1.4
Max
FNP
FND × FNP
7.2 6.8
0.34 0.33
1.18 1.06
Counts
374 405
FND: average first neighbor distance (m); S.D.: standard deviation; Min: minimum value; Max: maximum value; FNP: average first neighbor reciprocal distance (m−1).
3.2.2. Gray scale images AFM interest is the obtention of real topographic images from sample surfaces. The process used for SEM images is then applied, using a greater number of images in order to have better statistics. As shown in Table 1, the same tendency in decreasing the average fractal dimension after aging is noticed, the difference being larger than for SEM images, but the standard deviation is longer too. If we have a better measurement of topography, better statistics together with a larger deviation, another phenomenon has to be considered, that is the fractal dimension variation along the scale, i.e. the pseudo fractal behavior. To study the fractal behavior in order to check the constancy or the variation of the D value,
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Fig. 5. Effect of aging on the clusters first neighbor distances distribution showing the disappearance of the shorter distances.
we have drawn the Pseudo Fractality Graph which is the values of fractal dimension, D, at the corresponding scales, using all images processed whatever the magnification (Fig. 6). Over two decades of scales, the pseudo-fractal behavior of the surface is described by such a PFG. Considering that results from different samples and different magnifications are grouped
Fig. 6. Virgin polypyrrole PFG from 10 AFM images having side length from 10 to 151 m.
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together, the scatter is not important. The higher fractal dimension means that the surface tends to be more irregular at higher scales. The same PFG made with aged samples (Fig. 7), shows two different points. On one hand, the results seem to be more scattered. On the other hand, a slight decrease in the value of the fractal dimension at higher scales is observed. This could correspond to the slight smoothing observed visually. Note that it is not contradictory to the values of standard deviation (S.D.) obtained by computing an average value of D (see Table 1), because a flattening of the PFG results in a decreased S.D. This explains the apparent abnormal effects comparing AFM and SEM results, as previously discussed. To complete the results, PFG from SEM pictures of polypyrrole surfaces have been computed, and are presented on Figs. 8 and 9, for virgin and aged polypyrrole, respectively. The same comment can be made about the results dispersion, which increases after aging. Also, note that the PFG are more ‘flat’ than in the case of AFM pictures, which explains the lower value of S.D. in Table 1. In addition, the values of D are higher. This point has to be related to the physical meaning of the third dimension of SEM images (see upper panel), which also encompasses edges and shading effects. As a result, the image contrast is higher, which tends to increase D. Moreover, even carefully using the same parameters for all the pictures, on one hand the scatter is higher, on the other hand the curves obtained from images at different magnifications are not well superimposed. Due to the scatter, the effect of aging cannot be distinguished comparing Figs. 8 and 9, while Figs. 6 and 7 show a difference for the higher scales.
Fig. 7.
Aged polypyrrole PFG from 15 AFM images having side length from 10 to 151 m.
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Fig. 8. Virgin polypyrrole PFG from four SEM images having side length from 12 to 300 m).
Fig. 9.
Aged polypyrrole PFG from eight SEM images (side length from 12 to 300 m).
4. Conclusion By way of image processing, quantitative morphology analysis becomes a tool, illustrated in this paper to characterize the aging evolution of polypyrrole surfaces. In a first step, the complex architecture of the polypyrrole surfaces has been quantitatively described, over a large range of scales (two decades). Secondly, changes in morphology have been detected after aging, like gathering of aggregates and slight smoothing of the surface irregularity. Moreover, these variations are better quantified by AFM than by SEM. AFM is more suitable for using different magnifications and the physical meaning of the measurement is more simple.
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