Implantation induced phase formation in stainless steel

Nuclear Instruments and Methods in Physics Research B 211 (2003) 227–238 www.elsevier.com/locate/nimb

Implantation induced phase formation in stainless steel B. Stahl a

a,*

, E. Kankeleit b, G. Walter

c

TU Darmstadt, Material- und Geowissenschaft, D€unne Schichten, Petersenstrasse 23, 64287 Darmstadt, Germany b TU Darmstadt, Institut f€ur Kernphysik, Schlossgartenstrasse 9, 64289 Darmstadt, Germany c Gesellschaft f€ur Schwerionenforschung mbH, Planckstrasse 1, 64291 Darmstadt, Germany Received 10 October 2002

Abstract After Eu implantation, the phase formation in a surface region of 150 nm of stainless steel was studied nondestructively by a depth resolved M€ ossbauer technique (DCEMS). The observed iron phases are characterized by the hyperfine interaction of the 57 Fe nuclei. Their characteristics and depth distributions strongly depend on fluence and ion energy. A newly formed martensitic phase accompanies the implantation profile of the Eu atoms into a depth of up to about 120 nm. In a near-surface region of 15 nm, the observed phases differ from those found in larger depths. This reveals the substantial role of the surface, where implantation induced diffusion and segregation processes are effective. Concerning the DCEMS analysis, special emphasis is laid on the determination of systematic and statistical errors of the phase depth profiles.
2003 Elsevier B.V. All rights reserved. PACS: 61.82.Bg; 64.75.+g; 68.55.Nq Keywords: Ion implantation; Phase depth profiles; M€ ossbauer spectroscopy

Ion implantation leads to material modifications caused by ballistic effects as well as for instance enhanced diffusion, segregation and precipitation [1–8], where the surface and interfaces are of great importance. It is well established that stainless steel in a certain range of composition can undergo a martensitic phase transformation under mechanical stress or due to ion implantation [9–15]. It has

*

Corresponding author. Fax: +49-6151-166335. E-mail address: [email protected] (B. Stahl).

been observed that even a highly austenitic stainless steel (Fe62 Ni20 Cr18 ) that does not show any sign of a martensitic phase after severe cold working forms a martensitic phase after the implantation of Eu ions [16]. From a technical point of view, the understanding of martensitic phase transformations in neutron irradiated stainless steels during operation in fusion reactors is important [4,17], where (n,a) reactions lead to the accumulation of He which can form bubbles and exerts an internal stress on the material. The understanding of the influence of the surface on the phase formation during ion implantation is likewise important, as in many applications the aim is

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2003 Elsevier B.V. All rights reserved. doi:10.1016/S0168-583X(03)01713-0

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to improve the surface properties of a material with respect to hardness, friction, chemical stability and others. In these applications the topmost atomic layers play a major role. On this background, a systematic study of the implantation induced phase formation in stainless steel has been undertaken by a non-destructive, depth selective M€ ossbauer technique. Within a surface layer of
300 nm, referring to the density of iron, Depth Selective Conversion Electron M€ ossbauer Spectroscopy (DCEMS) is a feasible method for a phase characterization [18,19], giving complementary information to the element depth profile of the implanted Eu ions, revealed for instance by Rutherford backscattering spectroscopy. Due to the high sensitivity of the experimental setup used for the measurements, different phases could be identified as a function of depth, fluence and ion energy. The appearance of distinct phases in a thin surface layer of
15 nm underlines the strong influence of surface effects in the studied metallic system. For the first time, a systematic statistical error analysis of the resulting phase depth profiles could be established.

2. The DCEMS method A non-standard technique in M€ ossbauer spectroscopy is the detection of the conversion and Auger electrons after the resonant absorption of c-quanta [20–22]. The advantage with respect to thin film analysis lies in a high sensitivity for thin layers and the possibility of a depth resolution when combined with electron spectroscopy. The primarily monoenergetic conversion and Auger electrons add up to a characteristic energy spectrum due to energy loss and scattering in the solid sample. A detailed statistical analysis of the transport process reveals the depth distribution of the M€ ossbauer excitation via a deconvolution procedure. This is carried out by using so-called transport functions that represent the weight as a function of depth for different electron energy settings (Fig. 1). They are usually generated by detailed Monte Carlo simulations of the electron transport [23–25]. The use of a high resolution and high transmission electron spectrometer is crucial for this kind of double spectroscopy, namely the registration of events as a function of M€ ossbauer relative velocity (c-energy shift) and electron en-

1. Sample preparation Cold worked stainless steel foils (Fe62 Ni20 Cr18 ) with an enrichment of 95% in 57 Fe have been implanted with 3, 6 and 12 · 1016 /cm2 Eu ions at an energy of 400 keV. In addition, a fourth sample was prepared with a fluence of 6 · 1016 /cm2 at 600 keV. During implantation the samples were mounted on a liquid nitrogen cooled holder to minimize thermally driven phase changes. The main vacuum chamber and the sample area were additionally separated by a shield with an opening of 6 mm in diameter for the ion beam. During the implantation process, the shield was cooled down to )20 C to keep the sample as clean as possible, i.e. to prevent adsorbates of water or hydrocarbons on the sample. The gas pressure in the main chamber was less than 107 mbar. After implantation the samples were removed at room temperature and exposed to air. All M€ ossbauer measurements were done at room temperature.

dP/dx [1/nm]

Electron Transport in Iron K Conversion: 7.3 keV KLM Auger: 6.3 keV KLL Auger: 5.6 keV

0.075 0.050 0.025

4.9 5.2 5.5 5.7 6.3 6.8 [ keV] 7.0 7.1 Energy 7.2 Electron 7.3 7.4 0 10 20 30 40 50 60 Depth x [nm] Fig. 1. Depth weight functions of the DCEMS method for the UHV orange spectrometer from the K conversion edge of 57 Fe down to 5 keV. A deconvolution of the measured M€ ossbauer spectra with respect to these functions leads to the full depth information.

B. Stahl et al. / Nucl. Instr. and Meth. in Phys. Res. B 211 (2003) 227–238

ergy. To overcome the strong reduction of the signal in comparison to a standard M€ ossbauer setup, the electron spectrometer must be optimized for a maximum possible transmission and signal to noise ratio. Considering a necessary relative elec-

229

tron energy resolution in the order of 1%, the magnetic spectrometer of the orange type is most suitable for this kind of experiment [26]. An ultra high vacuum version of this instrument has been built in our group [18]. Due to the high sensitivity

Fig. 2. A subset of the 38 M€ ossbauer spectra for the 600 keV implantation with a fluence of 6 · 1016 /cm2 is seen in the left column with a normalization to equal area. The fitted sub-spectra are indicated by lines (StSt: stainless steel; SOx: surface oxide; Mart: martensite; NSP: near-surface phase). The appropriate depth weight functions are plotted on the right.

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of the instrument implanted samples with the natural abundance of only 2.2% in the 57 Fe isotope could be characterized [27]. M€ ossbauer spectra of the four implanted samples were recorded at various electron energies. The possible number of depth bins in the deconvolution analysis, i.e. the degrees of freedom in the depth profiles, directly correlates with the number of electron energy settings in the measurement and the statistical quality of the data. Both should be optimized under the constraint of limited measuring time. As we were interested in phase depth profiles over a range of 0–150 nm combined with a resolution in the order of 10 nm, we typically recorded 10–40 M€ ossbauer spectra at different electron energy settings for each sample. For the sample with a fluence of 6 · 1016 /cm2 and an ion energy of 600 keV a representative sub-set of the 38 M€ ossbauer spectra is plotted in Fig. 2 together with the respective depth weight functions. The systematic changes in intensity of the four M€ ossbauer components as a function of electron energy reflect their different depth distributions. Especially in the vicinity of the K conversion edge of 57 Fe at 7.3 keV, the surface sensitivity reveals those phases that are located in a narrow surface region. Even the thin surface oxide with a typical thickness of 2 nm can be detected. At 560 eV the overlap of low energy Auger electrons (
650 eV) and of K conversion and KLM-Auger electrons with a high energy loss leads to a depth weight function that combines a delta function like sensitivity for the topmost monolayers with an almost equally weighted contribution from the 120 nm region below. The thin surface oxide gives an appropriately pronounced signal (35(1)%) in this M€ ossbauer spectrum. The implantation induced unusual phases in the near-surface region of the sample (
15 nm) contribute only to 0.7(2)%, corresponding to the small range of the low energy Auger electrons. On the other hand, the clear signal of the 15 nm region in the M€ ossbauer spectra at 7.3 keV, i.e. at the K conversion edge, does not show any sign of non-metallic Fe. The thin surface oxide due to the handling of the sample in air is well separated from this 15 nm region. This is confirmed by our systematic studies on stainless steel surfaces before and after implantation

[16,18,28]. From this it is well known that the surface oxide is formed due to the exposure of the samples to air and that it is confined to a 2 nm thin surface layer. An oxidation of deeper regions of the samples is not observed. Therefore, the nearsurface region where the unusual implantation induced phases are found is not affected by oxidation. This will be not the case if insufficient vacuum conditions during the implantation process lead to a recoil implantation of oxygen from adsorbates. This would be unmistakeably seen in the isomer shift of related Fe compounds [16].

3. Data analysis To get the full information that is covered in DCEMS data, a simultaneous deconvolution with respect to the hyperfine interaction and depth profile parameters is essential. In the following, an indirect method of data deconvolution by a least square fitting routine is used. The necessary set of theoretical M€ ossbauer spectra is generated on the basis of the calculation of the depth dependent resonant absorption of the 14.4 keV c-radiation in the sample (including secondary emission and absorption processes) and the Monte Carlo simulated depth weight functions for the detection of the conversion and Auger electrons. Also included are parameters for the electron energy dependent detection response of the experimental set-up. These spectrometer parameters can be determined independently by a calibration measurement of a known standard absorber. The resonant excitation in the sample is calculated by the absorption of the Lorentzian c-spectrum of the source in different depths of the sample. It is important to mention that the transmitted c-spectrum at a certain depth depends on the absorption in the preceding layer. Thus the absorption in deeper regions has to be calculated with a modified c-spectrum, which demands an iterative procedure to determine the whole excitation as a function of depth and velocity. Following this primary excitation there is a chance of 90% in the case of 57 Fe to emit a conversion electron that may also be followed by Auger electrons of different energies as a result of the relaxation of the excited atomic

B. Stahl et al. / Nucl. Instr. and Meth. in Phys. Res. B 211 (2003) 227–238

shell. To analyze DCEMS data it is necessary to know the emission probability of these electrons as a function of their depth of origin, their primary energy, their emission angle and emission energy into the vacuum of the electron spectrometer. The resulting velocity dependent electron spectrum is a weighted superposition of all contributions from different depth regions. To complete the calculation, also secondary re-emission and re-absorption processes of resonant c-quanta, as competing with internal conversion, have to be considered. A part of the result of the Monte Carlo transport calculation that underlies the depth deconvolution of the DCEMS technique is plotted in Fig. 1 for K conversion (7.3 keV), KLM- (6.3 keV) and KLL-Auger (5.6 keV) electrons in 57 Fe for several electron energy settings of the UHV orange spectrometer. The detection response has already been included. It can be seen that the center of gravity of these depth weight functions is shifted to larger depth with decreasing electron energy when measured relative to the characteristic edges. It is clear that a measurement with a single electron energy setting cannot result in a good depth resolution as the weight functions also increase in width with increasing energy loss. Only a correlated analysis of spectra at neighboring electron energies, i.e. some kind of deconvolution procedure, will result in a detailed depth profile of phases. This will be illustrated for the Eu implanted samples.

4. Results 4.1. M€ ossbauer phase analysis The unimplanted Fe62 Ni20 Cr18 host matrix is characterized by the single M€ ossbauer line of the austenitic phase. Usually, there is also a small contribution from an iron oxide that is confined to a layer of
2 nm in thickness at the surface of the sample (dashed line in Fig. 2). Due to the implantation of Eu additional phases are formed. Their hyperfine parameters as well as their depth distributions depend on fluence and ion energy. It turns out that by the correlated analysis of the DCEMS data the hyperfine parameters of the different phases are well determined due to the

231

systematic variation in their relative contribution in the different M€ ossbauer spectra as a function of electron energy (see Figs. 2 and 3). In spite of a partial overlap of the M€ ossbauer subspectra relating to different phases, a distinction is essentially facilitated as their depth distributions do not coincide. This clearly resolves ambiguities that are unavoidable in an integral M€ ossbauer experiment. Two characteristic hyperfine parameters of the phases that are present after the Eu implantation are visualized in conjunction in Fig. 4. Plotted is the isomer shift d relative to a-iron in relation to the average magnetic hyperfine field hBhf i. In all four samples a magnetically split component with a broad but well defined hyperfine field distribution between 20 and 30 T (Fig. 5) is found, nearly independent of the various implantation conditions. Only for 12 · 1016 /cm2 a significant broadening and asymmetry of the distribution is seen in this region, indicated by the line in Fig. 4. The average hyperfine field of this ferromagnetic phase is (24.4 ± 0.3) T, the isomer shift relative to a-iron is ()0.007 ± 0.007) mm/s and the standard deviation of the gaussian field distribution amounts to r ¼ ð2:0  0:1Þ T. This phase is stable above a critical ion fluence and forms a massive layer within the austenitic matrix in all four samples (Fig. 6). A direct correlation of the phase depth profile with the element depth profile of Eu is observed [16,28]. The histogram like plots of the depth profiles are due to the discreteness of the depth axis in the least square analysis. The nonequidistant depth bins are adapted to the depth dependent resolution of the DCEMS method. Except for the sample with the lowest fluence, additional implantation induced phases are observed within a 10–20 nm near-surface region. The hyperfine parameters of these phases differ strongly in the three relevant samples. In one case a single line with an isomer shift similar to a-iron is observed. The isomer shift of the near-surface phases in the two other samples is larger by half a natural line width (Fig. 4). The average hyperfine field 33.9(1) T of the near-surface phase in the sample with the largest ion fluence is indicative for iron rich Fe–Ni alloys, showing a relatively narrow hyperfine field distribution with a variance r ¼ ð0:87  0:06Þ T. This is also accompanied by a

B. Stahl et al. / Nucl. Instr. and Meth. in Phys. Res. B 211 (2003) 227–238

Intensity [arb.units]

KLL KLM K

L 6·1016/cm2, 600 keV Austenite Martensite Near-Surface Phase Surface Oxide

3000 2000 1000 0

16

3·10 /cm2 6·1016/cm2 400 keV 600 keV

0.0 6.7 0.1 0.0 5.5

2

4

6

8

10

12

14

Electron Energy [keV]

0.1 0.0 0 10 20 30 0 10 20 30

Fig. 3. Measured resonant count rate of the four different phases as a function of electron energy for the 600 keV implantation. For clarity, the oxide phase is plotted as a solid line. The statistical error bars are smaller than the size of the used symbols. The characteristic electron energies for 57 Fe are indicated by arrows.

δ [mm/s] rel. to α - Fe

6·1016 /cm2 12·1016 /cm2 400 keV 400 keV

Eel [keV] 0.1 7.2

Weight [1/T]

232

3·1016 Eu/cm 2, 400 keV 6·1016 Eu/cm 2, 400 keV 6·1016 Eu/cm 2, 400 keV 12·1016 Eu/cm 2, 400 keV Austenite: Fe62Ni20Cr18

0.2

0.1

surf. surf.

surf.

0.0

0 10 20 30 0 10 20 30

Magnetic Hyperfine Field [T] Fig. 5. Magnetic hyperfine field distributions for the implanted samples at three different electron energies. They are assumed to be a superposition of gaussian distributions. Not shown is the delta function for the austenite at 0 T. The depth weight functions can be taken from Figs. 1 and 2. The data at 7.2 keV yield surface information.

21.1(6)) T is seen in Fig. 5). The M€ ossbauer parameters of the observed components are listed in Table 1. Fig. 5 summarizes the distributions of the magnetic hyperfine field in the different samples for three electron energy settings. The systematic dependence of these distributions on electron energy facilitates the interpretation of the M€ ossbauer data and leads to the depth dependent phase identification as illustrated in Fig. 6.

matrix

4.2. Depth analysis

-0.1 0

5 10 15 20 25 30 35

[ T] Fig. 4. The correlation of isomer shift d and average magnetic hyperfine field hBhf i for the different phases seen in the M€ ossbauer spectra. Clearly visible is the concentration of data points for dM ¼ 0:01 mm/s and hBhf;M i ¼ 24:5 T as an indication of the martensitic phase. The near-surface phases are labeled with surf. The error bars are in the order of the symbol size.

modification of the hyperfine field distribution of the remaining part which is related to the martensitic phase at larger depth. A slight increase in hBhf i of the high field fraction (to 28.6(2) T) and a lowering in hBhf i of the low field fraction (to

The large number of recorded M€ ossbauer spectra for each sample allows a determination of the phase depth profiles with high accuracy. The center of gravity as well as the skewness of the distribution is statistically significant. The statistical error band for the profiles can be extracted from the least square analysis, although there are still minor limitations concerning systematic errors in the shape of the phase depth profile by uncertainties in the electron transport theory for high electron energy losses [29], i.e. for large depths. For more details see below. The depth profiles of the M€ ossbauer phases have been parameterized by the following function:

Phase Composition

B. Stahl et al. / Nucl. Instr. and Meth. in Phys. Res. B 211 (2003) 227–238

1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

Austenite

16

2

16

2

16

2

including asymmetric profiles with extremely steep or flat slopes, as well as gaussian like distributions or plateaux. A further assumption is that all phases have to add up to unity in every depth bin x. This is assured by an appropriate contribution of the matrix phase at depth x, i.e. the remainder to unity is filled up with the M€ ossbauer phase of the matrix. Usually the host matrix is treated in this way, but, as in the example of the 400 keV implantation (6 · 1016 /cm2 ) in a depth of 20–60 nm, the implantation induced phases could completely replace the former matrix and thus the phase with the largest contribution in this depth range is treated as matrix. The set of theoretical M€ ossbauer spectra for the comparison with the experimental data is generated on the basis of the following input parameters and calculations:

3·10 /cm 400 keV

Martensite

Austenite

6·10 /cm 400 keV

Martensite

Austenite

12·10 /cm 400 keV

Martensite

16

Austenite

2

6·10 /cm 600 keV

Martensite

0 20 40 60 80 100 120 140 160

Depth [nm] Fig. 6. Depth profiles of the different phases in the four implanted samples. The narrow black area on top of each samples is due to oxidation when exposed to air. The histogram like distributions are not of physical nature but due to the discreteness of the depth scale. The fractions of all phases add up to unity at any depth.

Irel ðxÞ ¼

233

I0 I0  xx2    ; 1 þ exp w2 1 þ exp xx1 w1

x1 < x2: ð1Þ

Irel ðxÞ is the relative contribution at depth x; I0 determines the maximum value of the distribution, x1 and x2 are the locations of inflection and w1 and w2 the widths of the rising and descending slopes of the distribution. This small number of free parameters allows for a wide range of possible shapes,

1. The primary absorption of the 14.4 keV c-radiation of the radioactive source in the sample is calculated extensively on the basis of the parameterized depth profiles of the different phases. The calculation includes the so-called thickness effect in M€ ossbauer absorption that is a dampening of the amplitude of the c-radiation as a function of depth and relative velocity of source and absorber. This numerical calculation also includes secondary processes like the re-emission of a 14.4 keV c-quanta and its secondary resonant absorption or non-resonant photo absorption. Secondary X-rays are treated similarly. The free parameters in the fitting of the data that result from the excitation process in the sample are • the hyperfine parameters of the different phases in the sample (line width, isomer shift, magnetic hyperfine field distribution, electric field gradient, relative line intensities, . . .), • the line width of the M€ossbauer source, • the depth profile parameters for each M€ ossbauer component (see Eq. (1)), • geometrical parameters of the experimental set-up (the sizes of and distance between source and absorber, they are important in the calculation of the so-called cosine smearing and of solid angle effects).

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Table 1 Hyperfine parameters of the M€ ossbauer components 3 · 1016

6 · 1016

6 · 1016

12 · 1016

400

600

400

400

A

M

M

NSPh

M

NSPh

M

0.29 0.12 )0.087 – –

0.267(6) – )0.013(2) 24.2(1) 2.09(2)

0.267(4) – )0.008(2) 24.3(1) 2.03(2)

0.427(4) 0.148(3) 0.014(2) – –

0.285(1) – )0.000(2) 24.8(1) 2.08(1)

0.285(1) – 0.07(1) 9.0(1) 2.69(3)

)0.05(1) 21.1(6) 3.7(3)

Fluence (cm2 ) Ion energy (keV) CL (mm/s) CG (mm/s) d (mm/s) hBhf i (T) rðBhf Þ (T)

Isomer shifts are relative to a-iron.  Austenite,



martensite,



2. The emission probability of the conversion and Auger electrons following the M€ ossbauer excitation is calculated on the basis of their transport functions. These are illustrated in the most important energy range in Fig. 1. The Monte Carlo data of the electron transport is a fixed input in the analysis of the DCEMS data. No fit parameters are associated with this probability distribution. This underlies the assumption that a variation in phase composition of the sample does not significantly change the transport properties in the 300 nm surface layer. For the case of significant changes in the stopping power (compared to metallic iron) due to the implanted elements, a transformation of the depth scale from nm to the areal mass density would mainly correct for this effect. 3. As a last input the detection response of the experimental set-up has to be considered. In our case, a fixed parameter is the angular acceptance of the orange spectrometer that lies between 30 and 70 relative to the normal to the surface of the sample. The other response parameters are the relative energy resolution and the energy calibration of the electron spectrometer, the shape of the spectrometer response and the detection efficiency of the channeltron detector. All profiles in Fig. 6 are generated by a special DCEMS least square routine that was developed in our group in the last years [30]. It uses the MINUIT code developed by CERN [31]. As the least square analysis gives direct access to the statistical errors of the fit parameters, the

NSPh

0.005(4) 28.6(2) 1.73(6)

0.038(4) 33.9(1) 0.87(6)

near-surface phase, CL Lorentzian width, CG Gaussian width.

profile of the 600 keV implantation was analyzed in detail. The whole set of 31 measured M€ ossbauer spectra at electron energies between 2.2 and 13.8 keV were analyzed simultaneously. Fig. 3 shows the measured resonant intensities (spectral areas) of the stainless steel, martensite, near-surface and oxide component as a function of electron energy. In a perfect simulation of the experiment these intensities should be reproduced. In the analysis of DCEMS data one has to cope with systematic errors that are still present in electron transport simulations for energies between 2 and 15 keV and uncertainties in the experimental response. In the analysis of the calibration experiments for our orange spectrometer it turned out that an energy dependent detection efficiency has to be taken into account. The remaining relative difference between the total resonant intensity of the experimental and the best simulated data is shown in Fig. 7 as a function of electron energy. It can be seen that there is a systematic deviation in the total resonant intensity towards high energy losses of the electrons. This is the case for the K conversion electrons below 4 keV and for the L conversion electrons below 10 keV. This is an evidence for some systematic errors in the electron transport calculations when dealing with plural scattering, i.e. for high energy losses. In the vicinity of the conversion edges the relative deviation between experimental data and theory shows a flat behavior with values of less than 5%. This essential part of the electron spectrum covers the topmost 70 nm of the sample for K conversion electrons and 200 nm for L conversion electrons, respectively (see Fig. 2).

20 15 10 5 0 -5

K

3

6

L

9

12

Electron Energy [keV] Fig. 7. Relative difference in the total resonant count rate as a function of electron energy between experiment and theory, normalized to the experimental values. Larger deviations are correlated with high energy losses of the electrons. Concerning the depth information, the essential parts show deviations of less than 5%.

The difficulty at larger depths can partly be overcome by emphasizing the electron energy dependent relative fractions of the different M€ ossbauer components over their absolute count rate. This was taken into account for the profiles shown in Fig. 6. The comparison between a profile analysis with and without this emphasis on the relative fractions established differences only for depths larger than 80 nm. Thus, systematic errors in this depth range become important and have to be added to the statistical errors. For the future, the precise experimental determination of the transport matrix will be of great importance to improve the theoretical understanding of the transport process and the analysis for larger depths, i.e. for electron trajectories that are characterized by a large number of collisions. Besides the systematic errors in the transport simulation the statistical errors of the depth profile are available through a detailed analysis of v2 as a function of the fit parameters. Due to the very high statistical quality of the experimental data and the large number of electron energy settings the five fit parameters I0, x1, x2, w1 and w2 for each M€ ossbauer component are determined to high accuracy. To give an impression of the statistical precision of the martensite profile in the case of the 600 keV implantation the depth profile parameters of this phase have been varied systematically, i.e. one at a time while all the other parameters were then determined anew by the minimization of v2 .

Phase Composition

Relative Deviation [%]

B. Stahl et al. / Nucl. Instr. and Meth. in Phys. Res. B 211 (2003) 227–238

235

1.0

Austenite

0.8 0.6 0.4

Martensite

0.2 0.0 0

20

40

60

80

100

120

Depth [nm Fe] Fig. 8. DCEMS depth profile and the corresponding 2r error band of the martensite profile for the 600 keV implantation as a result of the simultaneous least square analysis of a set of 31 M€ ossbauer spectra.

The interval of variation for each parameter is determined by the two-sigma deviation of v2 . The resulting depth profiles for all these parameter combinations were put together and the resulting lower and upper envelopes are shown in Fig. 8. Whereas the left hand part of the depth profile of the martensite and the location of inflection for the right half are determined with high precision, the slope of the profile for depths larger than 80 nm shows a rather large variance. This reflects the probing depth of DCEMS. The mean projected range of the K conversion electrons lies in the order of 100 nm. The count rate of the L conversion electrons with a larger range of 300 nm in the relevant energy interval of 8–10 keV is already very low. This limits the attainable statistical quality of the M€ ossbauer data under the restriction of a limited measuring time. We hope that this can partly be overcome in the future by the implementation of a special multi channel detection system for the UHV orange spectrometer. It would allow the simultaneous registration of events in five separate energy channels in an electron energy interval with a relative width of DE=E ¼ 8%, increasing the effective count rate by a factor of 4. 5. Summary and discussion By the present work, the implantation induced phase formation in a stainless steel matrix has been

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characterized by a non-destructive, depth resolved M€ ossbauer technique. The evolution of the hyperfine interaction as a function of depth, ion energy and fluence could be studied on a set of four samples. The importance of the surface for the inhomogeneous phase formation could be substantiated. The error analysis of the experimental data and deconvolution procedure supports the reliability of the results. In the meantime, a liquid helium cryostat has been installed in the UHV orange spectrometer. This would allow for a further characterization of the phases by temperature dependent measurements. It turns out that a ferromagnetic phase with parameters dM ¼ 0:01 mm/s and hBhf;M i ¼ 24:5 T is systematically and efficiently formed by the Eu implantation into stainless steel. Though we have no direct proof for the crystallographic parameters of this phase, the comparison with [9–15] led us to suggest that the origin of the observed ferromagnetic phase is the result of the stress-induced transformation of the austenitic phase into the martensitic one. It is driven by Eu segregations leading to internal strain fields as a result of the non-solubility of Eu in stainless steel. The hyperfine interaction is very sensitive to local changes in the lattice constants, nearest neighbor co-ordination and the symmetry of the iron site. Thus, the reproducible formation of the ferromagnetic phase whose depth dependent fraction coincides with the Eu profile reveals the stability of this phase even in the presence of the Eu segregations. Only for the highest fluence of 12 · 1016 /cm2 deviations are found. The dynamics of the phase formation as a function of ion fluence and energy is most obviously revealed by the crucial role of the surface for the growth of the near-surface phases. For instance, the surface will act as a sink for vacancies and other defects. Diffusion processes will be enhanced towards or in the opposite direction to the surface. Mechanical stress is more easily released at the surface, leading to stress gradients as a function of depth. Viewed in combination with the sputtering process during implantation, the nearsurface region is the most dynamic area in the sample. Therefore it is not surprising that in contrast to the well defined martensitic phase, the

observed near-surface phases show a strong variability in their hyperfine parameters. Especially the magnetic hyperfine field varies from 0 to 34 T in those three samples, where a distinct surface behavior is measured. Although the isomer shift is not as sensitive to the underlying changes in the local structure, the correlation with the magnetic hyperfine field allows a clear phase separation. For the highest fluence at 400 keV, the hyperfine parameters of the near-surface phase (especially the large value of the magnetic hyperfine field of 34 T) are accompanied by a low field part in the magnetic hyperfine field distribution. This reveals a significant change in the nearest neighbor co-ordination of the Fe atoms. The high field part is the characteristic of iron rich Fe–Ni alloys. Simultaneously, the fraction of the distribution around 21 T is attributed to Fe–Cr alloys with low concentrations of Ni. This phase formation is driven by the segregation of Cr in the strain fields of the Eu precipitates, where the presence of the surface gives rise to an additional anisotropy. For the lower fluence of 6 · 1016 /cm2 at 400 keV, a near-surface phase is still present. Its isomer shift is similar to the high field component in the previous case but the magnetic hyperfine field amounts only to 9 T. Thus in both cases, the electronic density at the iron site is comparable but the exchange interaction with the nearest neighbors is modified. The local increase in Ni concentration gives rise to the observed hyperfine field but has not reached to the extend as seen for the higher fluence. The underlying segregation process is enhanced with increasing fluence. The anisotropy in the implantation induced diffusion processes due to the vicinity of the surface leads to local variations in the Cr concentration in the order of 20%. A similar process is not observed for the larger depths of the implantation profile, although the stopping power increases at the end of the ion track. At the lowest fluence of 3 · 1016 /cm2 at 400 keV, the surface induced processes have not yet led to a distinct near-surface phase. In addition to the lower fluence, the martensite concentration at the surface is lower than in the samples with higher fluences and equal ion energy. The appearance of the martensitic phase at the surface

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seems to be as well important for the near-surface phase formation as the presence of the surface itself. This is supported by the fact that the most significant change in the near-surface region takes place for a fluence of 12 · 1016 /cm2 , which actually means that the observed depth profile for the fluence of 6 · 1016 /cm2 has been sputtered off in the course of the implantation procedure [32,16] and the subsequent phase profile has built up on this history. The presence of the martensitic phase assists the formation of a near-surface phase. The martensite itself shows a slight increase of hBhf i to 28.6 T compared to the average value of 24.4 T for the other three samples. Thus, the microstructure of this phase changes in this fluence range. In this sense, the dynamics of the implantation process is mirrored in the final phase depth profile. In the case of the higher implantation energy of 600 keV, the near-surface phase shows evidence of a change in the crystal structure compared to the original stainless steel phase. This is seen in the increased isomer shift in comparison to the austenitic phase although no magnetic hyperfine splitting is observed at room temperature. Thus, the segregation process in the near-surface layer is just observable but has not reached to the extent of the equivalent sample with lower ion energy. This is related to the lower stopping power and martensite concentration at the surface. The surface sensitivity of the non-destructive DCEMS method combined with an appropriate depth resolution has revealed the implantation induced depth dependent phase formation in the topmost 150 nm of stainless steel samples. With other methods, the averaging over a larger depth region may hide these peculiarities which are on the other hand important for the modified surface properties of implanted materials. It would be of interest, to study the near-surface properties and the dynamics of the phase formation at higher sample temperatures than 77 K during implantation, to distinguish between thermally driven or implantation induced processes. Furthermore, diffraction methods have to be applied to analyze the crystallographic structure in comparison to the hyperfine interaction. A thorough comparison with N or C implanted steels may reveal additional chemical driving forces in the phase formation.

237

Acknowledgements We thank the Deutsche Forschungsgemeinschaft (DFG), the BMBF and the Gesellschaft f€ ur Schwerionenforschung (GSI) for financial support.

References [1] K.C. Russell, F.A. Garner, Metall. Trans. A 21 (1990) 1073. [2] A.M. Yacout, N.Q. Lam, J.F. Stubbins, Nucl. Instr. and Meth. B 42 (1) (1989) 49. [3] P.R. Okamoto, H. Wiedersich, Consultant Symposium on the Physics of Irradiation Produced Voids, UKAEA 231 (1975). [4] T.R. Allen, J.I. Cole, E.A. Kenik, Mater. Res. Soc. (1999) 507. [5] H. Pelletier, P. Mille, A. Cornet, J.J. Grob, J.P. Stoquert, D. Muller, Nucl. Instr. and Meth. B 148 (1999) 824. [6] S.M. Bruemmer, B.W. Arey, J.I. Cole, C.F. Windisch, Corrosion 51 (1) (1995) 11. [7] W.-J. Liu, J.-J. Kai, C.-H. Tsai, J. Nucl. Mater. 212–215 (1994) 476. [8] M.K. Bobrova, K. Ofeisa, L.B. Shelyakin, V.E. Yurasova, Vacuum 42 (14) (1991) 877. [9] E. Johnson, A. Johansen, L. Sarholt-Kristensen, H. RoyPoulsen, A. Christiansen, Nucl. Instr. and Meth. B 7–8 (1985) 212. [10] E. Johnson, A. Johansen, L. Sarholt-Kristensen, L. Grabaek, N. Hayashi, I. Sakamoto, Nucl. Instr. and Meth. B 19–20 (1987) 171. [11] G. Wagner, R. Leutenecker, T. Louis, U. Gonser, Hyp. Int. 42 (1988) 1017. [12] E. Johnson, L. Grabaek, A. Johansen, L. Sarholt-Kristensen, P. Borgesen, B.M.U. Scherzer, N. Hayashi, I. Sakamoto, Nucl. Instr. and Meth. B 39 (1989) 567. [13] A. Johansen, E. Johnson, L. Sarholt-Kristensen, S. Steenstrup, E. Gerritsen, C.J.M. Denissen, H. Keetels, J. Politiek, N. Hayashi, I. Sakamoto, Nucl. Instr. and Meth. B 50 (1990) 119. [14] I. Sakamoto, N. Hayashi, T. Toriyama, Hyp. Int. 80 (1–4) (1993) 1019. [15] N. Hayashi, L. Sakamoto, H. Tanoue, Nucl. Instr. and Meth. B 76 (1993) 268. [16] G. Walter, R. Heitzmann, D.M. R€ uck, B. Stahl, R. Gellert, O. Geiss, G. Klingelh€ ofer, E. Kankeleit, Nucl. Instr. and Meth. B 113 (1996) 167. [17] E.A. Kenik, T. Inazumi, G.E.C. Bell, J. Nucl. Mater. 183 (3) (1991) 145. [18] B. Stahl, E. Kankeleit, Nucl. Instr. and Meth. B 122 (1997) 149. [19] S. Kruijer, W. Keune, M. Dobler, H. Reuther, Appl. Phys. Lett. 70 (1997) 2696. [20] E. Kankeleit, Z. Phys. 164 (1961) 442. [21] K. Nomura, Y. Ujihira, A. Vertes, J. Rad. Nucl. Chem., Articles 202 (1–2) (1996) 103.

238

B. Stahl et al. / Nucl. Instr. and Meth. in Phys. Res. B 211 (2003) 227–238

[22] G.N. Belozersky, M€ ossbauer Studies of Surface Layers, Elsevier Science Pub., Amsterdam, 1993. [23] D. Liljequist, J. Phys. D 16 (1983) 1567. [24] D. Liljequist, J. Phys. D 11 (1978) 839. [25] R. Gellert, O. Geiss, G. Klingelh€ ofer, H. Ladst€atter, B. Stahl, G. Walter, E. Kankeleit, Nucl. Instr. and Meth. B 76 (1993) 381. [26] E. Moll, E. Kankeleit, Nukleonik 7 (1965) 180. [27] D. M€ uller, G.K. Wolf, B. Stahl, L. Amaral, M. Behar, J.B.M. da Cunha, Surf. Coat. Tech. 158 (2002) 604.

[28] G. Walter, B. Stahl, R. Nagel, R. Gellert, G. Klingelh€ ofer, E. Kankeleit, D.M. R€ uck, M. Soltani-Farshi, H. Baumann, Mat. Res. Soc. Symp. Proc. 504 (1998) 277. [29] J.M. Fern
andez-Varea, D. Liljequist, S. Csillag, R. R€aty, F. Salvat, Nucl. Instr. and Meth. B 108 (1996) 35. [30] R. Gellert, Ph.D. thesis, University of Technology Darmstadt, 1999. [31] Minuit-Function Minimization and Error Analysis, CERN Program Library entry D506 (Copyright CERN, Geneva 1994). [32] P. Sigmund, Phys. Rev. 184 (1969) 383.