J. Phys. Chem. B 2000, 104, 8779-8781
8779
Computer Image Processing of Transmission Electron Micrograph Pictures as a Fast and Reliable Tool To Analyze the Size of Nanoparticles Manfred T. Reetz,* Matthias Maase, Tobias Schilling, and Bernd Tesche* Max-Planck-Institut fu¨ r Kohlenforschung, Kaiser-Wilhelm-Platz 1, D-45470 Mu¨ lheim/Ruhr, Germany ReceiVed: January 27, 2000; In Final Form: May 30, 2000
A simple and time-saving method of analyzing images obtained from transmission electron microscopic (TEM) investigation of nanostructured transition metal and metal oxide colloids has been developed. It is based on computer image processing using the routine MORFO of the commercially available IMAGIC-5 software package. This high-throughput method provides statistically meaningful data such as the mean value of the colloidal particle diameter, number of nanoparticles considered and standard deviation.
Introduction
Results and Discussion
Nanostructured transition metal colloids are of considerable interest as catalysts for organic and inorganic reactions, as electrocatalysts in fuel cells and as components for materials with special electronic, optical or magnetic properties.1 Nanoparticles of metal sulfides and oxides are also the focus of past and current research.2 In many cases the size of the nanoparticles in the range of 1-10 nm influences the electronic and catalytic properties, which is the reason a great deal of research efforts are devoted toward the development of new methods for the size-selective preparation of such materials.1,2 The standard analytical method for the determination of particle size is transmission electron microscopy (TEM).3 Accordingly, the colloidal particles are deposited by simple dip-coating onto a carbon support film which is fixed on a copper grid, followed by TEM investigation. Typically, a TEM micrograph contains images of several hundred particles. The determination of size distribution then needs to be carried out. With the help of a simple magnifying glass it is customary to scrutinize and measure manually as many particles as possible, the goal being to establish a statistically meaningful histogram from which the average particle diameter and the size distribution can be deduced. However, this process is extremely time-consuming, especially if many samples containing hundreds of particles have to be evaluated. Moreover, there seems to be no generally accepted convention on how to proceed. In many publications the data is scarce; i.e., only the value of the average particle size is given. Thus, such papers provide no statistical information concerning the number of particles actually measured. This type of data presentation may be the result of a subjective analysis since only a small number of particles or even only certain regions of the imaged area of the TEM micrograph may have been considered. Finally, additional uncertainty exists in the case of particles which do not exist in perfectly uniform shape, since it is not clear how mixtures of particles with different geometries should be treated.4 This is relevant, for example, if not all of the particles are round. In this contribution we describe a simple and efficient method of analyzing TEM images of nanoparticles which not only delivers reliable and statistically significant data, but which is also time-saving.
The underlying principle in our approach is the use of computer image processing. First, the negative of a TEM micrograph to be analyzed is digitized. This is accomplished with the help of a flatbed scanner (HP Scanjet 4C/T) which enables a resolution of 1 200 × 1 200 dots per square inch (dpi). A single pixel (point of the bitmap of the digitized TEM micrograph) has an edge length of 0.2 nm at an electron microscopic magnification of 100 000. Thus, the resolution in the analysis of nanoparticles in the size range of 1-10 nm is clearly satisfactory and corresponds to that of a typical electron microscope. A maximally 2048 × 2048 pixel sector, which corresponds to 4.3 × 4.3 cm on the negative or 410 × 410 nm on the TEM sample, is then subjected to computer image processing. For this task a computer program is necessary capable of object recognition. Specifically the routine MORFO of the commercially available IMAGIC-5 software package5 which is used in a variety of other fields such as medicine and biology,6 was applied. Then an IDL source code converting the data provided by MORFO into a convenient presentation of all statistical data was developed.7 Accordingly, the analysis of the picture sector is possible, providing the distribution function of the particle diameter as well as the average diameter (dh), the number of particles studied (N), and the standard deviation (s). The operator has the option to apply low- and high-pass filters which lower the background noise and eliminate extensive light/ dark contrasts. However, if the quality of the TEM micrographs is acceptable, such improvements are not necessary. The operator can also optimize the process of analysis by specifying the values for “minimum particle size”, “maximum particles size”, and “threshold”.8 The following examples demonstrate the method. Samples of Pd colloids were prepared using our previously described method for the size selective fabrication of (n-Oct)4N+X-stabilized transition metal nanoparticles.9 As an example, a (nOct)4N+Br--stabilized Pd colloid was analyzed by TEM. Figure 1a shows a typical TEM micrograph (positiv) which is used for conventional particle analysis by means of a magnifying glass. For computer aided analysis it is sufficient to use the negative of the TEM micrograph, which is digitized and subjected to the algorithms of MORFO that recognize the objects and reconstructs a new image on the basis of the results of the
* E-mail:
[email protected]. Fax: +49 208 306 2985.
10.1021/jp000328e CCC: $19.00 © 2000 American Chemical Society Published on Web 08/24/2000
8780 J. Phys. Chem. B, Vol. 104, No. 37, 2000
Reetz et al.
Figure 1. (a) Original TEM micrograph of a Pd colloid (average diameter 3.8 nm). (b) Reconstructed image of the recognized particles. The agglomerate of particles in the center of the original TEM micrograph has not been considered since these particles are not clearly separated. The inset shows a magnified part of the image. (c) All particles that have been recognized as objects are marked with a cross. (d) Histogram showing the particle size distribution as well as statistical data such as average diameter dh, standard deviation s, and number of particles N.
digital image analysis (Figure 1b). This image exclusively displays what the computer has recognized as an object. Moreover, an area is assigned to each particle. Sections can be magnified (see inset in Figure 1b). In an interactive process the operator can then compare the original with the results in order to check the quality of the image analysis. If the agreement is satisfactory11 the output of the analyzing process is released. It contains statistical data such as mean diameter, number of particles considered, standard deviation, smallest and largest particle size. A copy of the original TEM micrograph (displayed as a positive image) is provided in which all particles that have been considered in the statistical analysis are marked with a cross (Figure 1c). Thus, particles that appear as “agglomerates” on parts of the support as a consequence of the dip-coating process are not considered. Since they are not marked, this exclusion is easily visible to the operator. The general decision regarding this is made by the operator who stipulates the “maximum particle size” to be considered in the analysis. A histogram showing the particle size distribution (Figure 1d) as well as the values for the average diameter, standard deviation and number of particles are also provided. The interactive analysis process prevents artifacts from adulterating the statistical data, thereby ensuring meaningful results. Moreover, within a short time a relatively large number of nanoparticles (thousands) can be evaluated.
The primary virtues of our method are accuracy, objectivity, and speed. A further advantage has to do with the problem of treating noncircular objects. Figure 2 shows the TEM images of (n-Oct)4N+(HOCH2CO2-)-stabilized Pd nanoparticles,9a many of which have a triangular shape. Take for example the typical triangular object marked B in Figure 2b. Obviously, there is a need to develop a standard form of treatment of such objects, because each operator may prefer a different decision, including the possibility of simply ignoring all “irregular” shapes. For the mathematical treatment leading to statistical data the present system automatically considers the area of the triangles (or other shapes) and calculates the circular area to which it would theoretically correspond, thereby providing the value of a virtual diameter. Examples are shown in Figure 2. Here again, “agglomerates” are excluded, a feature which is clearly visible to the operator. Finally, if the particles are very small, e.g., 1.2 nm or smaller, the distinction between genuine particles and background noise may become difficult. This is where the limitation of TEM imaging techniques3 in general becomes apparent, although it will depend on the quality of the specific instrument and the mode of sample preparation. It is then necessary to draw a line using the “minimal particle size” value. Several explanatory remarks are in order. Because of the algorithm in the program, the particles must be white. Upon using the TEM negative, an additional step is avoided. If the
Computer Processing of TEM Pictures
J. Phys. Chem. B, Vol. 104, No. 37, 2000 8781 Setting down the maximum particle size prevents “particles” from being considered which are connected. In the absence of these settings the size distribution would be influenced in a way that would not correspond to true experimental conditions. In summary, we have introduced a statistically reliable method for the high-throughput analysis of TEM images of nanoparticles. Since the operator only checks the quality of the analysis, and human factors are effectively excluded from the actual determination of geometric parameters, subjective decisions are avoided. Thus, objectivity is maintained over the whole process of analysis. References and Notes
Figure 2. (a) TEM micrograph of a Pd-colloid fraction that contains 40% of particles with a triangular cross-section. (b) Reconstructed image showing the recognized Pd particles. For particle size analysis the area of triangular objects (e.g., B) was treated as a being a virtual circular area from which the diameter was determined and used for further analysis. In the reconstructed image the objects still appear as triangles in order to allow the control of the analyzing process. The agglomerate of particles in the center (A) was not considered for the analysis. (c) Histogram showing the particle size distribution of the Pd colloid corresponding to the sample shown in Figure 2a.
positive image is used, the contrast has to be inverted by the program. Thus, this particular procedure was chosen, mainly because positive contrasts are customary. It should also be pointed out that the minimum particle size chosen by the operator prevents dark spots (which are produced by the phase contrast of the carbon support) from being counted as particles.
(1) See for example: (a) Schmid, G., Ed. Clusters and Colloids: From Theory to Applications; VCH: Weinheim, 1994. (b) Fendler, J. H., Ed. Nanoparticles and Nanostructured Films; Wiley-VCH: Weinheim, 1998. (c) Scho¨n, G.; Simon, U. Colloid Polym. Sci. 1995, 273, 101. (d) Meguro, K.; Torizuka, M.; Esumi, K. Bull. Chem. Soc. Jpn. 1988, 61, 341. (e) Pileni, M. P. Ber. Bunsen-Ges. 1997, 101, 1578. (f) Watzky, M. A.; Finke, R. G. J. Am. Chem. Soc. 1997, 119, 10382. (g) Brus, L. Curr. Opin. Colloid Interface Sci. 1996, 2, 197. (h) Antonietti, M.; Gro¨hn, F.; Hartmann, J.; Bronstein, L. Angew. Chem. 1997, 109, 2170; Angew. Chem., Int. Ed. Engl. 1997, 36, 2080. (i) Schmid, G.; Ba¨umle, M.; Geerkens, M.; Heim, I.; Osemann, C.; Sawitowski, T. Chem. Soc. ReV. 1999, 28, 179. (j) Teranishi, T.; Miyake, M. Chem. Mater. 1998, 10, 594. (k) Ahmadi, T. S.; Wang, Z. L.; Green, T. C.; Henglein, A.; El-Sayed, M. A. Science (Washington, D.C.) 1996, 272, 1924. (l) Yu, W.; Liu, H. Chem. Mater. 1998, 10, 1205. (m) Zhong, C. J.; Zhang, W. X.; Leibowitz, F. L.; Eichelberger, H. H. Chem. Commun. (Cambridge) 1999, 1211. (n) Grunwaldt, J.-D.; Kiener, C.; Wo¨gerbauer, C.; Baiker, A. J. Catal. 1999, 181, 223. (o) Busser, G. W.; van Ommen, J. G.; Lercher, J. A. J. Phys. Chem. B 1999, 103, 1651. (p) Liz-Marzan, L. M.; Giersig, M.; Mulvaney, P. Langmuir 1996, 12, 4329. (q) Toshima, N.; Wang, Y. Langmuir 1994, 10, 4574. (r) Giroir-Fendler, A.; Richard, D.; Gallezot, P. Faraday Discuss. 1991, 92, 69. (2) Examples of metal oxide colloids: (a) Lume-Pereira, C.; Baral, S.; Henglein, A.; Janata, E. J. Phys. Chem. 1985, 89, 5772. (b) Harriman, A.; Thomas, J. M.; Millward, G. R. New J. Chem. 1987, 11, 757. (c) Kalyanasundaram, K.; Gra¨tzel, M. Angew. Chem. 1979, 91, 759; Angew. Chem., Int. Ed. Engl. 1979, 18, 701. (d) Christensen, P. A.; Harriman, A.; Porter, G.; Neta, P. J. Chem. Soc., Faraday Trans. 2 1984, 80, 1451. (e) Claerbout, A.; Nagy, J. B. In Preparation of Catalysts V; Poncelet, G., Ed.; Studies in Surface Science and Catalysis Vol. 63; Elsevier: Amsterdam, 1991; p 705. (f) Pileni, M. P. Langmuir 1997, 13, 3266. (g) Reetz, M. T.; Quaiser, S. A.; Winter, M.; Becker, J. A.; Scha¨fer, R.; Stimming, U.; Marmann, A.; Vogel, R.; Konno, T. Angew. Chem. 1996, 108, 2228; Angew. Chem., Int. Ed. Engl. 1996, 35, 2092. (h) Reetz, M. T.; Koch, M. G. J. Am. Chem. Soc. 1999, 121, 7933. Examples of metal sulfide colloids: (i) Weller, H.; Eychmu¨ller, A. In Semiconductor Nanoclusters: Physical, Chemical, and Catalytic Aspects; Kamat, P. V., Meisel, D., Eds.; Studies in Surface Science and Catalysis Vol. 103; Elsevier: Amsterdam, 1997; p 5. (j) Weller, H. Angew. Chem. 1993, 105, 43; Angew. Chem., Int. Ed. Engl. 1993, 32, 41. (k) Henglein, A. Ber. Bunsen-Ges. 1997, 101, 1562. (l) Feltin, N.; Levy, L.; Ingert, D.; Pileni, M.-P. AdV. Mater. (Weinheim, Ger). 1999, 11, 398. (m) Kamat, P. V.; Meisel. D. Semiconductor Nanoclusters: Physical, Chemical and Catalytic Aspects; Elsevier: Amsterdam, 1997. (3) (a) Kirkland, E. J. AdVanced Computing in Electron Microscopy; Plenum Press: New York, 1998. (b) Cowley, J. M. Prog. Surf. Sci. 1986, 21, 209. (c) Thomas, G. Ultramicroscopy 1986, 20, 239. (d) Seeger, A. J. Electron. Microsc. 1999, 48, 301. (e) Niemantsverdriet, J. W. Spectroscopy in Catalysis; VCH: Weinheim, 1993. (4) Duff, D. G.; Edwards, P. P.; Johnson, B. F. G. J. Phys. Chem. 1995, 99, 15934. (5) Image Science Software GmbH, Heilbronner Str. 10, D-10711 Berlin, Germany. (6) (a) Serysheva, I. I.; Orlova, E. V.; Chiu, W.; Sherman, M. B.; Hamilton, S. L.; van Heel, M. Nat. Struct. Biol. 1995, 2, 18. (b) Schatz, M.; Orlova, E. V.; Dube, P.; Ja¨ger, J.; van Heel, M. J. Struct. Biol. 1995, 114, 28. (c) van Heel, M.; Harauz, G.; Orlova, E. V. J. Struct. Biol. 1996, 116, 17. (7) It can be obtained from the authors. (8) Barrier of the grayscale below that pixels are regarded as potential part of an object. (9) (a) Reetz, M. T.; Maase, M. AdV. Mater. (Weinheim, Ger.) 1999, 11, 773. See also: (b) Reetz, M. T.; Helbig, W.; Quaiser, S. A.; Stimming, U.; Breuer, N.; Vogel, R. Science (Washington, D.C.) 1995, 267, 367. (10) Modul-MORFO requires the objects to be bright and the background to be dark. (11) If the quality of the TEM image is reasonable an operator needs only one or two runs to achieve maximum agreement between the original and processed image.
