Identification and Visualization of Fine Precipitates in a Cr–Mo Steel Based on SEM Contrast

Abstract

Precise characterization of fine precipitates in steels is crucial owing to the influence of these precipitates on the mechanical properties. Various types of carbide precipitates occur in heat-resistant Cr–Mo steels. Their identification typically requires characteristic X-ray and diffraction analysis because it is usually not possible to differentiate carbides based solely on their size and morphology. Modern scanning electron microscopes equipped with multiple image detectors can provide abundant microstructural information. Thus, we investigated the use of secondary electron (SE) and backscattered electron (BSE) image contrast to determine the types of precipitates in the commonly used 2.25Cr–1Mo steel. A mechanically polished bulk specimen was examined and the observation parameters were optimized by systematically varying the SEM accelerating voltage and working distance. We found that the use of a low accelerating voltage and short working distance enabled the differentiation of four types of carbides and AlN precipitate in the steel specimen. A small penetration depth of the primary electrons and selective acquisition of SE/BSE were the key to extracting the material contrast. This technique is expected to allow quantification of the size, distribution, and area fraction of precipitates over large areas of bulk specimens.

 

This Paper was Originally Published in Japanese in J. Japan Inst. Met. Mater. 82 (2018) 169–175. Refinements of English were made for the title of the paper, the abstract, and the captions of Fig. 6, Fig. 7, and Table 2. The Ref. 24) was newly added.

Fig. 7 (a) In-lens SE image of the 2.25Cr–1Mo steel specimen obtained using an accelerating voltage of 1 kV and a WD of 5 mm. (b) Multilevel thresholding image of (a).

1. Introduction

The mechanical and other properties of steels are greatly influenced by the material microstructure. Thus, microstructural control permits optimization of the material properties. For example, a dramatic improvement in mechanical properties was achieved via the precise control of fine carbides of several nanometers in diameter.¹⁾ Evaluating the characteristics of fine precipitates is crucial for designing metals and alloys.

Transmission electron microscopy (TEM) examination of thin film or extraction replica samples is one of the conventional methods for evaluating fine precipitates in steel materials. In the extraction replica method, the matrix is selectively etched and precipitates in the matrix are extracted onto a supporting film.²⁾ This method has the advantage of allowing the analysis of the precipitates without matrix effects. The precipitates are typically identified using energy-dispersive X-ray spectroscopy (EDX) elemental analysis and electron diffraction.

Scanning electron microscopy (SEM) is another microscopic technique that uses an electron probe. Sample preparation is easier for SEM than for TEM. Magnification ranging from several tens of times to over a hundred thousand times can be achieved for bulk samples using SEM. Furthermore, field-emission electron gun (FEG) SEM enables high-resolution imaging, while observation at accelerating voltages lower than 1 keV is becoming common owing to advances in SEM optics. The use of multiple image detectors has also made possible the visualization of various types of information regarding the specimen. To maximize the amount of information obtained, optimization of the observation parameters and appropriate detector selection are crucial.^3–7)

SEM using multiple image detectors provides abundant microstructural information.⁴⁾ We are interested in developing methods for identifying precipitates using secondary electron (SE) and backscattered electron (BSE) image contrast without the need for elemental analysis.

Several previous reports have described the evaluation of precipitates in heat-resistant steels using SEM image contrast.^8–10) Creep damage is strongly influenced by precipitates (e.g., type, size, distribution, and number density). Consequently, the particle size, area ratio, and average interparticle distance have been investigated by various researchers.^8,11–13) However, no systematic study of the SEM observation parameters has yet been reported.

In this work, we discuss how we optimized the SEM observation conditions, or signal acceptance conditions, to allow the differentiation of various types of precipitates in a Cr–Mo steel based on the SEM contrast. In addition, we describe a method for quantifying different types of precipitates by analyzing the contrast in SEM images.

2. Experimental Methods

2.1 Materials and analytical methods

In this study, 2.25Cr–1Mo steel containing various types of carbides and aluminum nitride (AlN) was used to evaluate the SEM observation parameters. This sample was cut from steel that had been subjected to a temperature of 540°C for more than 270,000 h. Table 1 shows the chemical composition of the 2.25Cr–1Mo steel sample. A carbon extraction replica specimen was examined using TEM, EDX, and electron diffraction to identify the types of precipitates present. A spherical aberration (Cs)-corrected transmission electron microscope (JEM-ARM200F, JEOL) equipped with an EDX system (NSS, Thermo Fisher Scientific) was used. TEM imaging and TEM-EDX elemental analysis were conducted using an accelerating voltage of 200 kV. Subsequent SEM imaging was performed based on the TEM results. A 10 mm square specimen of the bulk material was embedded in a conductive resin and then polished using colloidal silica. This mirror-polished cross section was used in SEM analysis. The most suitable SEM observation parameters for differentiating various types of precipitates according to the image contrast were then investigated. A Schottky field-emission SEM system (ULTRA 55, Carl Zeiss Microscopy) was used in this study. Elemental analysis was performed using the EDX system (NSS, Thermo Fisher Scientific).

Table 1 Chemical composition (mass%) of the 2.25Cr–1Mo steel sample used in this study.

2.2 Features of the SEM used in this study

The Carl Zeiss ULTRA 55 system contains multiple SE and BSE detectors, which can be utilized to obtain a variety of microstructural information. Figure 1 shows a schematic diagram of the detector configuration in this instrument.

Fig. 1

Schematic diagram of the lens and detector configuration of the SEM system used in this study.

The features of each detector have already been reported by Tachibana.¹⁴⁾ The ULTRA 55 system contains an electrostatic–magnetic hybrid lens. In this lens system, the SEs emitted from the sample surface are accelerated toward the column by the electrostatic field. The in-lens SE detector mainly detects SEs with low kinetic energies, which are sensitive to the difference in work function or change in surface potential caused by local charging. As a result, the in-lens SE detector affords images with strong material contrast.^3,14,15) An electrode referred to as the energy filter grid is installed in front of the in-lens BSE detector. Upon applying a bias voltage to this grid, SEs are rejected to allow the selective detection of BSEs. We expected the in-lens SE detector to provide material contrast and the in-lens BSE detector to provide average atomic number (Z) contrast.

2.3 SEM imaging conditions

The SE and BSE signals obtained from the detectors are dependent on the observation parameters, such as the accelerating voltage and working distance (WD; distance from the objective lens to the specimen surface). Consequently, the SEM image contrast also varies with these parameters. In this experiment, the accelerating voltage (0.3–15 kV) and WD (2–20 mm) were systematically varied to control the signal acceptance. The grid voltage of the in-lens BSE detector was set to 1500 V when the accelerating voltage was 5 or 15 kV; otherwise, the voltage was set to 100 V lower than the accelerating voltage. Based on these experiments, we selected the optimum observation parameters that satisfied the following two conditions.

1. I.    All precipitates including fine particles were visible.
2. II.    Clear image contrast that reflected the types of precipitates was obtained.

We found it useful to use the contrast from both the in-lens SE and in-lens BSE images to identify the precipitates. We discuss this issue in more detail in Section 3.3. We obtained the in-lens SE and in-lens BSE images simultaneously and attempted to determine the optimum observation parameters for differentiation of the precipitates.

3. Results

3.1 TEM analysis

Figure 2 shows typical bright-field TEM images of the precipitates in 2.25Cr–1Mo steel. Several of the precipitates were examined using TEM-EDX elemental analysis and electron diffraction, which revealed the presence of four types of carbides, namely, M₂C (hexagonal or orthorhombic), M₆C (cubic), M₂₃C₆ (cubic), and M₇C₃ (hexagonal), in addition to AlN, as indicated in Fig. 2. M₂C, M₆C, M₂₃C₆, and M₇C₃ carbides are commonly found in 2.25Cr–1Mo steel following prolonged exposure to high temperatures.^{2,12,16–21)}

Fig. 2

Typical bright-field TEM images of the precipitates in 2.25Cr–1Mo steel: (a) M₂C, (b) M₆C, M₂₃C₆, and M₇C₃, and (c) M₂₃C₆.

The M₂C carbides were rich in Mo and predominantly distributed in the interior of the grains. The M₆C carbides were enriched in Mo and Si. Both the M₂₃C₆ and M₇C₃ carbides were rich in Cr and Fe. The Mo concentration of the M₇C₃ carbides was lower than that of the M₂₃C₆ carbides. The M₂C carbides displayed a needle-shaped or rod-shaped morphology, whereas the other three types of carbides were mostly block shaped. Some of the M₂₃C₆ carbides were also rod shaped, as shown in Fig. 2(c). These observations demonstrate the difficulty in identifying the types of carbides in this steel solely on the basis of their sizes and morphologies in TEM images.

3.2 SEM imaging

3.2.1 In-lens SE images

Figure 3 shows a series of in-lens SE images obtained by systematically varying the observation conditions. The image contrast or visibility changed with the observation conditions. In the image obtained using an accelerating voltage of 1 kV and a WD of 5 mm, three types of precipitates exhibited different contrast with a high visibility, as indicated by the letters i–iii in Fig. 3. Furthermore, fine precipitates with a diameter of approximately 150 nm were visible, as indicated by the dashed circles in Fig. 3. In contrast, in the images obtained using an accelerating voltage of 1 kV and a WD of 2 mm or an accelerating voltage of 15 kV and a WD of 5 mm, the type ii precipitates exhibited similar contrast to the matrix. The fine precipitates indicated by the dashed circles were not visible in the images obtained using high accelerating voltages, such as that acquired at 15 kV with a WD of 5 mm.

Fig. 3

In-lens SE images of the same region of the 2.25Cr–1Mo steel specimen obtained using accelerating voltages of 0.3, 1, 5, or 15 kV and WDs of 2, 5, or 20 mm.

These results indicated that an accelerating voltage of 0.3 kV and a WD of 2 mm or an accelerating voltage of 1 kV and a WD of 5 mm were the optimum observation parameters for distinguishing the types of precipitates from the in-lens SE images.

3.2.2 In-lens BSE images

Figure 4 shows a series of in-lens BSE images obtained by systematically varying the observation conditions. In the image obtained using an accelerating voltage of 1 kV and a WD of 2 mm, several types of precipitates with clearly different contrast were observed, as indicated by the letters i–iii in Fig. 4. Fine precipitates with a diameter of approximately 150 nm were also visible, as indicated by the dashed circles in Fig. 4. These particles were not visible in the images obtained using higher accelerating voltages (>1 kV). At an accelerating voltage of 15 kV, the type ii precipitates were not visible, as the contrast of these carbides was almost identical to that of the matrix. Furthermore, the borders between the matrix and type ii precipitates were not clear. In the image obtained using an accelerating voltage of 0.3 kV and a WD of 2 mm, the type i and ii precipitates could not be differentiated because all of the precipitates except type iii exhibited similar dark contrast.

Fig. 4

In-lens BSE images of the same region of the 2.25Cr–1Mo steel specimen obtained using accelerating voltages of 0.3, 1, 5, or 15 kV and WDs of 2, 5, or 20 mm.

On the basis of these results, we concluded that an accelerating voltage of 1 kV and a WD of 2 mm or an accelerating voltage of 1 kV and a WD of 5 mm were the optimum conditions for in-lens BSE imaging.

Taking the experimental results for the in-lens SE and in-lens BSE imaging together, an accelerating voltage of 1 kV and a WD of 5 mm were considered optimum for differentiating the precipitates via both imaging methods. The subsequent SEM experiments were therefore conducted using these observation parameters.

3.3 SEM image contrast for each precipitate

To further investigate the relationship between the SEM contrast and precipitate type, we examined an area different from that shown in Figs. 3 and 4. The images were obtained using an accelerating voltage of 1 kV and a WD of 5 mm as discussed in the previous section and the contrast was then analyzed. Figures 5(c) and (d) show higher-magnification images of the indicated regions of Figs. 5(a) and (b), respectively. Fine precipitates with diameters exceeding 40 nm were visible in Figs. 5(c) and (d). Comparison of the in-lens SE and in-lens BSE images revealed four types of precipitates with different contrast, as indicated by the letters A–D in Fig. 5. A fifth type of precipitate, type A′, was also observed with the same contrast as type A. The type A precipitates were approximately 400 nm in length, possessed a block shape, and were distributed along the grain boundaries, whereas almost all of the type A′ precipitates were fine with an elongated shape and distributed in the interior of the grains. On the basis of these features, we deduced that the type A′ precipitates were M₂C-type carbides.

Fig. 5

SEM images of the same region of the 2.25Cr–1Mo steel specimen obtained using an accelerating voltage of 1 kV and a WD of 5 mm with (a) the in-lens SE detector and (b) the in-lens BSE detector. Panels (c) and (d) show magnified images of the areas indicated by the rectangles in panels (a) and (b), respectively.

To analyze the contrast of the five precipitates in more detail, their brightness values (0–255) in the in-lens SE and in-lens BSE images were plotted, as shown in Fig. 6(a). The horizontal and vertical axes show the brightness values in the in-lens BSE image (Fig. 5(b)) and in-lens SE image (Fig. 5(a)), respectively. We selected 16 pixels near the center of each precipitate and calculated the mean brightness. For the type A, B, and A′ precipitates, 20 particles were analyzed. For the type C and D precipitates, all of the particles identified in the field of view were analyzed (type C: two particles, type D: 13 particles). The contrast of the matrix was evaluated in a similar manner by selecting ten areas of grains 1–3 (Fig. 5(a)) and determining the mean brightness of 16 pixels for each area.

Fig. 6

(a) Relationship between the brightness values in the in-lens BSE and in-lens SE images shown in Figs. 5(a) and (b) for the precipitates and matrix. (b)–(e) EDX spectra of precipitates A–D.

The contrast of the matrix varied from grain to grain. We confirmed the change in the matrix contrast by tilting the samples. Thus, the matrix exhibited channeling contrast in the in-lens BSE images even at this accelerating voltage. Aoyama et al. demonstrated that the Z-contrast was dominant in the images formed by reflected electrons scattered toward the normal direction of the sample even at low accelerating voltages.⁶⁾ As shown in this example, we still observed channeling contrast even when the reflected electrons scattered toward the normal direction of the sample when areas damaged by mechanical polishing were removed. Interestingly, the contrast of the precipitates did not change upon tilting the sample. These results suggest that the crystal orientation had little effect on the contrast of the fine precipitates.

The brightness of the type A/A′ precipitates was higher than that of the type B–D precipitates in the in-lens BSE image, as shown in Fig. 6(a). Therefore, the in-lens BSE images proved useful for identifying the type A/A′ precipitates. Some of the type B and C precipitates could not be differentiated from the matrix in the in-lens BSE image owing to the overlapping brightness ranges of these precipitates and the matrix. However, the type B and C precipitates could be differentiated from the matrix using the in-lens SE images because the brightness ranges were clearly separated. All of the precipitates could be differentiated by combining the results from the in-lens SE and in-lens BSE images.

Elemental analysis using EDX was performed to examine the phases of the type A–D precipitates. Figures 6(b)–(e) show the EDX spectra of each precipitate using an accelerating voltage of 5 kV. The EDX spectrum of the type A′ precipitate was very similar to that of the matrix owing to the fineness of this precipitate, such that the spectrum of this precipitate contained elemental information predominantly from the matrix. Taking into account the composition information for each type of precipitate described in Section 3.1, the four precipitates were identified as follows: A: M₆C, B: M₂₃C₆, C: M₇C₃, and D: AlN.

The EDX analysis confirmed the one-to-one correspondence between the SEM contrast and precipitate type. Thus, the type A–D precipitates could be identified using only their SEM contrast without EDX analysis. The M₆C and M₂C precipitates can be differentiated by their shapes, sizes, and positions. Furthermore, the distributions of the data points for the M₆C (type A) and M₂C (type A′) precipitates appear to be slightly different, despite their overlapping brightness ranges (Fig. 6(a)). It may be possible to differentiate M₂C and M₆C by finding a better observation parameter or enhancing the image gradation.

4. Visualization and Area Evaluation of Precipitates for Each Phase

We attempted to visualize the distributions of the various types of precipitates using image analysis based on the relationship between the SEM contrast and precipitate type. As the brightness values for the M₂C and M₆C precipitates overlapped as mentioned in Section 3.3, we categorized these precipitates as belonging to the same class (Fig. 7 and Table 2, class III) for the purposes of the image analysis. In-lens SE images in which there was no overlap of the brightness ranges for the precipitates and matrix were used in the image analysis.

Fig. 7

(a) In-lens SE image of the 2.25Cr–1Mo steel specimen obtained using an accelerating voltage of 1 kV and a WD of 5 mm. (b) Multilevel thresholding image of (a).

Table 2 Total areas and area fractions for the five phases obtained from quantitative analysis of the multilevel thresholding image shown in Fig. 7(b).

Figure 7(a) shows the in-lens SE image used. We visualized the distributions of the precipitates by extracting pixels corresponding to the brightness ranges of the five classes, as listed in Table 2. Figure 7(b) shows the result of multilevel thresholding, and the total areas and area fractions for the five phases are summarized in Table 2.

The results revealed that the precipitate distributions were clearly visualized and the area fractions quantitatively evaluated by using image analysis. This method allows quantitative evaluation of precipitate distributions over large areas using bulk specimens. For example, this technique should allow the distributions of various types of precipitates from the matrix to weld heat-affected zones to be evaluated rapidly and quantitatively.

5. Discussion

5.1 Contrast of in-lens SE images

As mentioned in Section 2.2, material contrast is generally dominant for in-lens SE detector images because the in-lens SE detector mainly detects SEs with low kinetic energies. Varying the WD leads to changes in the signal acceptance, and thus the information and contrast of the in-lens SE detector image also changes.⁷⁾ The detection of SEs with low kinetic energies is crucial for obtaining in-lens SE images with clear contrast that reflects the phases of the precipitates.

The following criteria are essential for obtaining high spatial resolution and high contrast for each precipitate:

1. (1)    Selective acquisition of low-energy SEs that are sensitive to the chemistry of the precipitates. This is mainly achieved by optimizing the WD.
2. (2)    Use of low accelerating voltages to attain high spatial resolution. The key factor is reducing the interaction volume of the primary electron beam and specimen.²²⁾

SEs collected using the above acceptance control enabled differentiation of the precipitates. However, further research is required to obtain a quantitative understanding of the SE yield, which is affected by various factors including the work function and change in the surface potential caused by local charging.

5.2 Contrast of in-lens BSE images

We examined whether the contrast of in-lens BSE images can be simply explained by the difference in mean atomic number. Figure 8 shows the relationship between the mean atomic number of each type of precipitate or matrix and the brightness value from the in-lens BSE image. We calculated the mean atomic number of each type of carbide based on the chemical compositions described in a previous study.²¹⁾ The resulting values were as follows: M₂C: 27.1, M₆C: 29.2, M₂₃C₆: 21.8, M₇C₃: 19.8. These Z values correspond to the chemical compositions we used to calculate the mean atomic number, which were as follows: M₂C: (Cr_0.2Fe_0.05Mo_0.75)₂C, M₆C: (Cr_0.05Fe_0.5Mo_0.45)₆C, M₂₃C₆: (Cr_0.45Fe_0.5Mo_0.05)₂₃C₆, M₇C₃: (Cr_0.57Fe_0.38Mo_0.05)₇C₃. The mean atomic number of the matrix was calculated to be 26.0 (Fe_97.0Cr_2.42Mo_0.58). As shown in Fig. 8, the results for the four carbides (M₂C, M₆C, M₂₃C₆, and M₇C₃) could be approximately fitted by a straight line, demonstrating an almost linear correlation between the mean atomic number of the carbides and the brightness in the in-lens BSE images. This finding strongly suggests that the contrast in the in-lens BSE image reflects the mean atomic number of the carbide. The data for the matrix deviated from the linear fit, which may be attributable to the channeling contrast. A large deviation from the straight line was observed for AlN, which can probably be ascribed to the small size and mean atomic number of AlN compared with the other precipitates, thus allowing the incident electrons to easily reach the matrix. As a result, the signals from the matrix affect the contrast of AlN in the in-lens BSE images. The linear relationship between the contrast in BSE images and the mean atomic number is lost at accelerating voltages lower than 1 kV.^23,24) Therefore, the quantification of BSE images acquired at low accelerating voltages will require further understanding of the nature of backscattered electrons.

Fig. 8

Relationship between the mean atomic number and brightness value from the in-lens BSE image for the precipitates and matrix.

6. Conclusions

We used SEM to examine a mirror-polished cross section of 2.25Cr–1Mo steel and characterize the precipitates. We determined the optimum observation parameters (accelerating voltage and WD) for differentiating various types of carbides (M₂C, M₂₃C₆, M₇C₃, and M₆C) and AlN. The following conclusions can be drawn:

1. (1)    M₂₃C₆, M₇C₃, and AlN can be differentiated based on their contrast in the in-lens SE and in-lens BSE images obtained using an accelerating voltage of 1 kV and a WD of 5 mm.
2. (2)    M₂C and M₆C can be differentiated based on their shapes and positions. However, the differentiation of these precipitates based on their SEM contrast requires further investigation.
3. (3)    Collecting low-energy SEs by acceptance control is essential for the differentiation of precipitates. The use of low accelerating voltages is also crucial for attaining high spatial resolution.
4. (4)    The contrast of carbides in the in-lens BSE images at an accelerating voltage of 1 kV and a WD of 5 mm reflects the mean atomic number of the precipitates.
5. (5)    The distributions and area fractions of various types of precipitates can be evaluated via image analysis. This method is expected to permit the quantitative evaluation of precipitate distributions over large areas of bulk specimens, such as weld zones and matrices.

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