# High-throughput investigation of Si and Fe effects on Al-Sc-Zr precipitation kinetics

A. Techeres, M. Perez, A. Deschamps, S. Seibt, N. Kirby, L. Jiang, T. Dorin

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A. Techeres, M. Perez, A. Deschamps, S. Seibt, N. Kirby, et al. High-throughput investigation of Si and Fe effects on Al-Sc-Zr precipitation kinetics. Materials Science and Engineering: A, 2025, 943, 10.1016/j.msea.2025.148851. hal-05247831

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# High-throughput investigation of Si and Fe effects on Al-Sc-Zr precipitation kinetics

A. Techereş<sup>a</sup>, M. Perez<sup>b</sup>, A. Deschamps<sup>c, d</sup>, S. Seibt<sup>d</sup>, N. Kirby<sup>d</sup>, L. Jiang<sup>a</sup>, T. Dorin<sup>a, e</sup>

## ARTICLE INFO

### Keywords:

Aluminium alloys

Impurities

Al<sub>3</sub>Sc

Diffusion couples

High-throughput metallurgy

SAXS

APT

## ABSTRACT

Scandium-containing aluminium alloys are receiving growing attention as promising candidates for next-generation aluminium alloys due to their exceptional precipitation strengthening potential and thermal stability. While industrial uptake remains limited, broader adoption is anticipated with improved scandium availability from emerging mining projects. In this context, understanding the influence of impurity elements—particularly silicon (Si) and iron (Fe), which accumulate in recycled aluminium—is critical for future alloy design. This study employs high-throughput diffusion couples with controlled Si gradients and two distinct Fe levels (0.01 and 0.1 wt%) to systematically investigate their individual and combined effects on Al<sub>3</sub>Sc precipitation kinetics. Ageing at 350 °C was monitored using hardness mapping, in-situ SAXS, and APT. Results show a pronounced acceleration of precipitation and strengthening with increasing Si up to ~0.1 wt%, with continued kinetic gains up to ~0.35 wt%. Fe subtly enhances early hardening and increases Si and Fe incorporation into Al<sub>3</sub>Sc. These findings provide critical insight into impurity tolerance in Sc-containing aluminium alloys, guiding alloy development for a circular economy and enabling future industrial deployment.

## 1. Introduction

In the context of a sustainable economy, materials need to satisfy both high performance for reducing emissions during product use and high recyclability for reducing emissions during product fabrication [1]. In the case of Aluminium alloys, the two trends are already visible in the industry at large. Producing primary aluminium from bauxite is an energy-intensive process, primarily due to the refining and electrolysis stages. In contrast, recycling aluminium requires up to 95 % less energy, making it a significantly more sustainable alternative for manufacturing aluminium products. Emissions can also be reduced by creating high-performance Al alloys, which enable lightweighting of structures [2], leading to improved efficiency and reduced emissions in transportation [3,4].

Pure aluminium has low mechanical properties. The strength of aluminium is traditionally improved through the addition of alloying elements, often at the expense of other properties such as ductility or corrosion resistance. One promising candidate for improving strength without compromising other properties is scandium (Sc). Sc has been

shown to significantly increase strength - by up to 100 MPa per 0.1 wt% addition - due to the formation of nanometric Al<sub>3</sub>Sc precipitates with the L1<sub>2</sub> structure [5-7]. These precipitates typically form in the temperature range of 300-400 °C and have relatively low coarsening resistance at higher temperatures. This limitation can be addressed through the addition of Zirconium (Zr), which, under the right ageing conditions, can lead to the formation of coarsening-resistant Al<sub>3</sub>(Sc,Zr) with a core-shell structure due to the differing diffusion coefficient of Sc and Zr in Al [8-11]. This ensures that these particles remain stable during solution treatment, which typically occurs in the range of 500-550 °C for most aluminium alloys.

During recycling, impurities such as silicon (Si) and iron (Fe) accumulate in aluminium. As the proportion of scrap use increases, impurity levels also rise. Si has been shown to significantly influence the formation of Al<sub>3</sub>Sc and Al<sub>3</sub>(Sc,Zr) precipitates. Prior studies have demonstrated that even low levels of Si, below 0.1 wt%, can significantly accelerate the formation kinetics of Al<sub>3</sub>Sc dispersoids due to the strong binding energies between Si and Sc at nearest-neighbour distances [12]. More recently, this accelerating effect was also observed in alloys

<footnote><sup>a</sup> Deakin University, Institute for Frontier Materials, Geelong, Australia</footnote>

<footnote><sup>b</sup> Institut National des Sciences Appliquées Lyon (INSA Lyon), MATEIS, Villeurbanne, France</footnote>

<footnote><sup>c</sup> SIMAP, Grenoble INP - Phelma, Univ. Grenoble Alpes, Saint Martin d'Hères Cedex, France</footnote>

<footnote><sup>d</sup> Australian Synchrotron, ANSTO, 800 Blackburn Road, Clayton, VIC, 3168, Australia</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: thomas.dorin@deakin.edu.au, thomasdorin@gmail.com (T. Dorin).</footnote>
containing up to 0.5 wt% Si, typical of 6xxx-series alloys [13]. The general consensus is that the presence of Si leads to the acceleration of Al₃Sc precipitation kinetics [14–16]. While it is clear that both low and relatively high levels of Si can promote the formation of Al₃Sc, the effect of intermediate Si concentrations remains unclear, and the ideal Si content for achieving maximum acceleration and strengthening has not been identified.

In contrast, the role of Fe impurities in Al-Sc systems has received less attention. Beeri et al. [17] concluded that Fe levels below ~0.06 wt % have little effect on the hardening of Al-Sc alloys. However, increasing Fe to much higher levels, up to 0.7 wt%, has been shown to be detrimental as it leads to the formation of coarse Al₃Fe particles which have been shown to scavenge Sc solutes, as Sc has been observed to partition into these coarse intermetallics [18]. Despite these findings, no systematic study has explored the combined effects of Fe and Si impurities across varying concentrations. Moreover, the interplay of Si and Fe at intermediate levels, particularly their influence on Al₃Sc formation kinetics, remains poorly understood.

High-throughput investigation methods can help accelerate experimental data acquisition in the material discovery, property screening and the implementation of computational approaches in metallurgy. These methods have been successfully applied to steels [19–21], high entropy alloys [22], Ni-based super alloys [23] and Al alloys [24,25]. A high-throughput study typically involves two components: a sample exhibiting a gradient in one or more parameters (e.g. composition or processing conditions) and a high-throughput investigation technique. The investigation method should match the gradient's scale, ensuring the probed area is significantly smaller than the gradient to assume local homogeneity.

One type of such samples is a diffusion couple (DC), initially used to generate phase diagrams [26] or atomic mobility studies. Its application has more recently expanded to include precipitation studies [27,28]. Diffusion couples can be manufactured from bulk alloys with different compositions, which are joined together to form a metallic bond at the interface. A composition gradient is then induced by interdiffusion during heat treatment. Factors such as temperature, duration, diffusing species, matrix properties, and the bond's quality all influence the gradient's final length. Gumbmann et al. [25] demonstrated the successful fabrication of diffusion couples with gradients as long as 4 mm using linear friction welding. The metallic bond achieved was strong enough to enable further plastic deformation, extending the gradient length to 10 mm. While such gradients are ideal for characterisation, linear friction welding equipment is not widely available.

To systematically investigate the effects of Si and Fe on precipitation kinetics and hardening, this study adopts a practical and reproducible approach by fabricating Al-Sc-Zr-Fe diffusion couples with a continuous Si gradient (~0–0.5 wt%) and two distinct Fe levels (~0.01 and ~0.1 wt %). This design enables the exploration of a wide compositional space within a limited number of samples while maintaining consistent Sc content and ageing conditions.

This study employs the created diffusion couples to systematically investigate the influence of Si on Al₃Sc formation for Si content ranging from ~0 to 0.5 wt%. To investigate the effect of Fe impurities, two diffusion couples are created with ~0.01 and ~0.1 wt%Fe (termed Fe-lean and Fe-rich). The diffusion couples are quantified with scanning electron microscopy (SEM) and Electron Probe Microanalysis (EPMA). The precipitation kinetics are monitored across the diffusion couples during ageing at 350 °C using a combination of hardness and small-angle X-ray scattering (SAXS) measurements. Atom probe tomography (APT) is used on two samples with the same Si level and varying Fe content to evaluate the impact of Fe on the stoichiometry of the formed precipitates. At the ageing temperature studied, only the formation of Al₃Sc is expected, and the formation of Al₃Zr is hence not discussed in this paper. The results are discussed in terms of the single and combined impact of Si and Fe impurities on the formation kinetics of Al₃Sc and to conclude on the “ideal” Si content for maximum effect.

## 2. Materials and methods

### 2.1. Materials

Given the objective of producing a Si composition gradient with a composition range as large as possible, the Si-rich side of the diffusion couple must be close to the solubility limit of Si in the alloy. To determine this limit, the solubility of Si was investigated in two different ternary systems Al-Si-Sc and Al-Si-Zr using ThermoCalc and its TCAL6 Al alloys database. The results are shown in Fig. 1. A section of the ternary Al-Si-Sc diagram at 610 °C is displayed in Fig. 1a, while the same section of the Al-Zr-Si diagram is shown in Fig. 1b. The main goal of these diagrams is to confirm that a fully solutionised state will be achieved at the solution temperature selected for this study.

Fig. 1 reveals that the Si solubility is mostly influenced by Zr. In this study, Zr was limited to 0.1 wt% to ensure significant Si solubility. Relatively, Sc has a smaller influence on the solubility of Si. A composition of 0.1 wt%Sc was chosen, which is in line with many literature reports and maintains a 1:1 ratio in wt. % with Zr [29,30].

A total of four Al-0.1Sc-0.1Zr-(Si)-(Fe) were prepared from Al-2Sc, Al-5Zr and Al-20Si wt% master alloys through melting. Two alloys were produced with ultra-high purity aluminium to control the Fe content, while the other two used commercial pure aluminium.

The alloys were melted in an induction furnace under vacuum, with the chamber flushed with Ar gas before each melting. The molten mixture was held for 20 min at 710 °C to promote homogenisation and then poured into a copper mould to promote fast and uniform cooling. The chemical composition for the four prepared alloys was verified using an optical emission spectrometer (SPECTRO Maxx) and presented in Table 1.

### 2.2. Methods

#### 2.2.1. *Manufacturing samples with macroscopic composition gradient*

Due to its simplicity and availability, hot compression was used to produce the diffusion couples for this study.

The steps for manufacturing the diffusion couples are presented in Fig. 2. The first step consists of machining cubes with dimensions 10 × 10 mm from the two separate alloys that will form the diffusion couple. The mating surfaces of the two cubes were machined to tight parallelism tolerances to improve surface contact between the samples, as well as with the die punches. Additionally, the contact surfaces were polished down to a 1 µm finish to further improve the contact area and reduce the risk of defects at the interface. After polishing, the samples were stored in ethanol to prevent oxidation.

The second step consisted of hot compression. The samples were removed from ethanol, cleaned, and immediately placed in contact and compressed without plastic deformation. Subsequently, the samples were heated to 610 °C in air and homogenised for 1 h. After homogenisation, the samples were further compressed and plastically deformed to break the oxide layer at the interface. A total compressive deformation of 25 % was applied.

The final step involved a 72-h diffusion treatment at 610 °C, during which the mechanical load was maintained. At the end of the heat treatment, the samples were water quenched. Multiple samples were cut parallel to the expected composition gradient. This high homogenisation treatment also ensured the dissolution of any prior Si and Sc phases such as the V-Phase that is known to form in these alloys [31].

Alloys 1 and 2 were combined, as were Alloys 3 and 4, such that the primary difference between the two resulting diffusion couples was the Fe content. The first diffusion couple (Alloys 1 and 2) had an average Fe content of 0.01 wt% (Low Fe), while the second (Alloys 3 and 4) had an Fe content of 0.1 wt% (High Fe). This difference was introduced to investigate the effect of Fe on the formation of Al₃Sc.

The time and temperature for the diffusion heat treatment step were determined by numerical simulation using the single-phase diffusion
<figure><img src="image_3.png" /><figcaption>Fig. 1. Sections of the ternary diagrams at 610 °C for (a) Al-Sc-Si and (b) Al-Zr-Si.</figcaption></figure>

<caption>**Table 1**</caption>

The chemical composition measured via optical emission spectrometry of the four alloys used in this study, expressed in wt.%.

<table><thead><tr><th>Alloy</th><th>Sc</th><th>Zr</th><th>Si</th><th>Fe</th><th>Other</th></tr></thead><tbody><tr><td>1</td><td>0.096</td><td>0.135</td><td>0.011</td><td>0.011</td><td>&lt;0.01</td></tr><tr><td>2</td><td>0.104</td><td>0.0879</td><td>0.517</td><td>0.023</td><td>&lt;0.01</td></tr><tr><td>3</td><td>0.094</td><td>0.100</td><td>0.038</td><td>0.110</td><td>&lt;0.01</td></tr><tr><td>4</td><td>0.109</td><td>0.106</td><td>0.544</td><td>0.116</td><td>&lt;0.01</td></tr></tbody></table>

module available in the ThermoCalc package. The latest mobility database available at the time was used TCAL v6. To simulate the diffusion distance of Si, from the rich alloy to the lean one, a perfect interface was assumed. Two 20 mm regions were created; one containing 0.1Sc, 0.1Zr and 0 Si and the other with the same level of Sc and Zr but 0.5Si. The diffusion distance after 72 h at 610 °C is simulated to be approximately 4 mm. Using EPMA, the experimentally measured length of the macroscopic composition gradient was approximately 3.6 mm. The EPMA results will be shown later in the results section. This demonstrates good agreement between the simulation and the experimental results.

### 2.2.2. Characterisation methods

The solution-treated diffusion couples were then isothermally aged for up to 32h at 350 °C. Ex-situ ageing was performed using a resistance-heated Tetlow furnace without protective atmosphere. The samples were extracted at various ageing times and water quenched for further characterisation. The samples were polished using 1200 grit sandpaper

on both sides down to a thickness of 0.2–0.4 mm. This thickness was achieved to ensure maximum transmission for the SAXS experiments. These types of samples were used for in-situ SAXS, while similarly cut but thicker samples (more than 5 mm) were used for isothermal ageing and hardness measurement, as well as SEM and EPMA measurements. APT samples were prepared into needles from the bulk alloys mentioned in Table 1.

Vickers hardness measurements were conducted across the diffusion couples. Multiple measurements were performed along the composition gradient length using a Struers Vickers Hardness tester with a diamond indenter under a load of 1 kg and holding time of 5 s. Due to the small dimension of our diffusion couple specimen, only one measurement line was conducted across the diffusion couple. This did not allow the measurement of error bars. However, the measurement error on these lean aluminium alloy samples is known to be relatively small.

A JEOL 7800F SEM equipped with Oxford X-Max 20 SDD energy dispersive spectroscopy was used for this project. Imaging was performed at various accelerating voltages between 5 and 20 kV to analyse the interface after the hot compression step.

EPMA was used to investigate the distribution of Si and Fe across the diffusion gradient. It was performed using a JEOL FE-EPMA instrument. EPMA was preferred over EDX because it offers higher chemical resolution when measuring lower element concentrations due to the better noise to signal ratio achieved by wavelength dispersive X-ray analysis (WDX) compared to EDX. Calibration was performed using standardised samples provided by the instrument manufacturer. Four hundred measurements were conducted on a 10 mm line where the middle point was on the interface between the two samples. Acquisition was performed

<figure><img src="image_4.png" /><figcaption>Fig. 2. Schematic of the manufacturing process for diffusion couples using the hot compression method.</figcaption></figure>
for 30 s/point.

In-situ SAXS was performed at the same temperature for up to 3 Hours. The ageing was performed using a Linkam THMS600 stage mounted in line with the beam (see Figure A.1). It was performed on both Low Fe and High Fe samples at 350 °C, in air, for various ageing times. The experiments were carried out at the Australian Synchrotron on the SAXS/WAXS beamline using a 15 keV beam energy. For measuring the scattering signal, a Pilatus Eiger 2M detector placed in a vacuum chamber was set at a distance of 1638 mm. The exposure time for all the measurements presented in this study was 1 s.

By combining the high time resolution of the SAXS beamline with the automated motorised stage, we performed 105 spatially resolved measurements along the 3.5 mm length of each diffusion couple in just 3 min and 13 s, using a 1-s exposure per point. Details of the SAXS data acquisition, correction procedures, and model-based data analysis are provided in Appendix A.

The APT samples were prepared from thin slices of the bulk material. The thin slices were first cut into stick-like shapes and electropolished to promote the formation of the needle-like shape required for APT following common APT sample preparation methods.

The APT investigations were performed using a CAMECA LEAP 5000 XR instrument. The measurements were carried out at 25K under ultra-high vacuum in voltage pulsing mode using a pulse fraction of 20 % and

a pulse repetition rate of 200 kHz. The detection rate was 0.005 atoms per pulse. The CAMECA AP Suite 6 containing the Integrated Visualization Analysis Software (IVAS) was used for data analysis consisting of reconstruction, visualization, and further processing. The background correction tool in IVAS was used for Background subtraction.

Further in the analysis process, the precipitates were isolated by using iso-concentration surfaces, and concentration proximity histograms were used to analyse precipitate compositions. The detailed analysis of APT data is presented in Appendix B.

### 3. Results

#### 3.1. SEM and EPMA

The Diffusion Couple was investigated via SEM and EPMA. The results are presented in Figs. 3 and 4. SEM was performed at the interface of the two alloys forming the diffusion couple to evaluate the quality of the bond. Backscattered electron contrast images are presented in Fig. 3. Fig. 3a displays a low magnification snapshot from the edge of the bonding interface, while Fig. 3b shows a higher magnification picture of the interface, highlighted by a red circle, in the centre of the bonding surface. Overall, the bond was found to be excellent except for small defects at the sample's edges.

<figure><img src="image_5.png" /><figcaption>Fig. 3. (a) Low magnification SEM-BSE image of the edge of bond and (b) higher magnification SEM-BSE image of the bond in the middle of the interface.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Distribution of elements across (a) the low Fe and (b) the high Fe samples as a function of distance measured via EPMA.</figcaption></figure>
To create a diffusion gradient of sufficient length, the diffusion couples were homogenised for 72 h at 610 °C. The length of the gradient was assessed using EPMA measurements. The EPMA results for Si, Sc, and Fe are presented in Fig. 4. The measurements confirm that a consistent Sc content was achieved across both diffusion couples. Additionally, they indicate that a continuous Si gradient was successfully established in both samples with a total diffusion length of ~4 mm, consistent with kinetic predictions. The Fe measurements were highly noisy, making it challenging to accurately determine Fe levels. However, it does show consistent Fe level across the diffusion couple. The accurate Fe composition was measured via optical emission spectroscopy as reported in Table 1 with 0.01 and 0.1 wt% Fe for respectively the Fe-lean and Fe-rich diffusion couples.

From this point onward, all properties presented as a function of Si will be plotted based on the concentration measured via EPMA and correlated with the distance.

## 3.2. Hardness evolution during isothermal ageing

The diffusion couples were aged at 350 °C for up to 32 h. The ageing heat treatment was interrupted at specific time intervals by water quenching, and the progression of the precipitation process was indirectly measured through hardness measurements across the diffusion couple. The evolution of hardness is shown in Fig. 5 with results presented for both diffusion couples: a solid line represents the diffusion couple containing 0.01 wt% Fe, while a dotted line represents the diffusion couple containing 0.1 wt% Fe. Different colours indicate different ageing times, and the solution-treated condition is depicted in black.

As ageing progresses, the hardness increases, which can be attributed to the formation of Al<sub>3</sub>Sc precipitates. A significant impact of Si content on the ageing kinetics is observed, particularly for Si content below 0.1 wt% Si. Beyond this concentration, the effect of Si on hardness appears to saturate. The accelerating effect of Si is particularly pronounced at low concentrations, consistent with existing literature. Similar trends are observed in both the Fe-lean and Fe-rich diffusion couples.

In the as-solutionised condition, the hardness is identical for both diffusion couples. However, at early ageing times, the Fe-rich diffusion couple exhibits slightly higher hardness, especially on the Si-lean side, suggesting further acceleration of precipitation kinetics due to the presence of Fe. This difference in hardness persists throughout the ageing process, with the Fe-rich diffusion couple maintaining higher hardness after 32 h of ageing compared to the Fe-lean couple.

## 3.3. Small-angle X-ray scattering

SAXS was employed to further analyse the precipitation kinetics across the diffusion couples during in-situ ageing at 350 °C. The SAXS patterns were fitted to extract the average particle size and mean-squared fluctuation ($\bar{\eta}^2$), proportional to the precipitate volume fraction through Equation (A.4). The results are presented as heatmaps in Fig. 6, showing the evolution of mean radius and $\bar{\eta}^2$ as a function of Si concentration and time. The results are displayed side-by-side to compare the two diffusion couples with different Fe concentrations.

The mean precipitate radius increases with ageing time and decreases with higher Si content at any given ageing time (Fig. 6a and b). For example, increasing the Si concentration from 0.05 wt% to 0.2 wt% reduces the mean radius from approximately 30 Å to 20 Å. This effect of Si on precipitate size is evident in both the Fe-lean and Fe-rich samples. $\bar{\eta}^2$ increases as a function of ageing time and Si content in both Fe-lean and Fe-rich samples (Fig. 6c and d). Notably, $\bar{\eta}^2$ is slightly higher in the Fe-lean sample compared to the Fe-rich sample.

## 3.4. Atom probe tomography

APT was used to analyse the composition of precipitates and the solid solution for specific compositions and ageing times. Two APT samples were collected for alloys containing 0.1 wt% Si, one with Fe and one without, after 1 h of ageing at 350 °C (see Fig. 7). The Fe content in the APT samples was determined by deconvoluting the peaks located at 28 and 29 Da. The results of the deconvolution indicate that Fe is present

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Comparative evolution of hardness as a function of Si concentration for samples with different Fe concentrations (solid line is the low Fe sample and dashed line is the high Fe sample) during isothermal ageing at 350 °C.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Evolution as function of time and Si concentration of the mean radius and volume fraction for (a) and (c) the Fe-lean and (b) and (d) the Fe-rich diffusion couples during in-situ SAXS.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** APT reconstructions of Sc(red), Si(green) and Fe (blue) distribution after 1h of ageing at 350 °C for samples containing 0.1 wt%Si for (a) the Fe-rich alloy and (b) the Fe-lean alloy. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

only in the Fe-rich sample, where approximately 20 % of the 28 Da peak originates from Fe ions (see Appendix Table B.1). The samples contained ~0.1 wt%Zr in the matrix, and Zr was not found inside the dispersoids.

The presence of Fe is found to have a minimal effect on the mean radius of the precipitates after 1 h of artificial ageing at 350 °C, in line with the SAXS results. The volume fraction is indirectly calculated based on the mass balance between the bulk Sc concentration and the measurement of solid solution. The volume fraction is found to be higher in the Fe-rich sample (see Table 2).

The precipitates in both APT samples were isolated by isoen concentration surfaces. Proximity histograms were generated from these surfaces and the concentration profiles for Si, Sc and Fe were plotted for the two samples in Fig. 8. The proximity histograms reveal the presence of Si inside the Al₃Sc precipitates, confirming earlier reports [16,32,33]. The Si content inside the Al₃Sc precipitates is found to be significantly higher in
<table><caption>**Table 2**
Summary of Precipitate Characteristics and Matrix Composition from APT for Samples Aged at 350 °C for 1 Hour</caption><thead><tr><th>Nominal composition<br>[wt.%]</th><th>Bulk Fe [at.<br>[%]</th><th>Bulk Sc [at.<br>[%]</th><th>Sc in Solid Solution<br>[at. %]</th><th>R<sub>m</sub><br>[nm]</th><th>Number density<br>[*e21/m<sup>3</sup>]</th><th>F<sub>v</sub> (from Mass<br>balance)</th><th>Iso-concentration surface<br>[at. %]</th></tr></thead><tbody><tr><td>0.01Fe</td><td>0.000</td><td>0.064</td><td>0.0200</td><td>4.80</td><td>3.66</td><td>0.00169</td><td>1</td></tr><tr><td>0.10Fe</td><td>0.094</td><td>0.077</td><td>0.0195</td><td>4.60</td><td>5.43</td><td>0.00221</td><td>1</td></tr></tbody></table>

the Fe-rich sample (~10 at.% Si) compared to the Fe-lean sample (~3.5 at. % Si). The presence of ~1.5 at.% Fe is found inside the Al₃Sc precipitates in the Fe-rich sample, confirming earlier results [16].

# 4. Discussion

## 4.1. Influence of Si on Al₃Sc formation

The addition of Si has a pronounced effect on the formation kinetics and stability of Al₃Sc dispersoids. Low levels of Si have previously been shown to significantly accelerate the formation kinetics of Al₃Sc dispersoids due to the strong binding energies between Si and Sc at nearest-neighbour distances [12]. A more recent study also revealed that this accelerating effect was observed in an alloy containing 0.5 wt% Si, typical of 6xxx-series alloys [16]. The present study uses a diffusion couple to bridge the gap between impurity and alloying levels of Si and identify the “ideal” Si content for maximising the acceleration of formation kinetics and the hardening response.

### 4.1.1. Ideal Si content for maximum strengthening

The present study reveals a significant acceleration in kinetics and additional strengthening when the Si content increases from 0 to 0.1 wt %. For the same bulk Sc content, the alloy free of Si reaches a peak hardness of ~33 HV, while an alloy with 0.1 wt% Si achieves a peak hardness of over 60 HV after 32 h of ageing at 350 °C. The peak hardness plateaus for Si contents above ~0.1 wt%. Consequently, the ideal Si content for achieving maximum strengthening potential during reasonable ageing times at 350 °C is ~0.1 wt% Si.

Interestingly, the as-solutionised alloy shows increased hardness as a function of Si due to solid solution strengthening from Si solute. However, after 32 h of ageing at 350 °C, all alloys with more than 0.1 wt% Si exhibit the same peak hardness. This suggests that the observed decrease in hardness increment (peak HV minus as-solutionised HV) with increasing Si may result from the loss of solid solution strengthening as Si precipitates out of solution. Nonetheless, the strengthening contribution from Al₃Sc likely remains consistent for Si contents above 0.1 wt%.

### 4.1.2. Ideal Si content for enhanced kinetics

Although the peak hardness levels off for Si contents above 0.1 wt%, the acceleration of formation kinetics continues to improve with increasing Si content. This effect is most evident during early ageing. For instance, after 1 h of ageing, the alloy containing 0.35 wt% Si achieves a peak hardness of ~60 HV, whereas the alloy with 0.1 wt% Si reaches only ~50 HV over the same period.

### 4.1.3. Conclusion on the impact of Si on Al₃Sc formation and associated strengthening

Our findings confirm earlier literature reports of the accelerating effect of Si on Al₃Sc formation. This is particularly relevant as most Al alloys contain Si, either as an impurity or as an alloying element. Our study is the first to demonstrate that the effect of Si is non-linear, with a significant acceleration and strengthening effect observed when increasing Si content from 0 to 0.1 wt%, and a maximum acceleration of formation kinetics occurring at ~0.35 wt% Si.

Interestingly, our findings challenge the notion that Si levels should be minimised in Al-Sc alloys. While earlier works have cautioned against exceeding impurity thresholds due to concerns about forming deleterious phases such as AlSc₂Si₂ (V-phase) [31], our study demonstrates

that a comparable strengthening response can be achieved with Si levels up to ~0.5 wt%. This was made possible by homogenising the alloys at 610 °C for extended durations, ensuring the dissolution of any V-phase formed during solidification. However, it should be noted that such homogenisation conditions may not be feasible under industrial alloy manufacturing constraints.

## 4.2. Influence of Fe on Al₃Sc formation

While prior studies have mostly investigated either very low or excessively high Fe concentrations, the effect of intermediate Fe levels on Al₃Sc precipitation remains insufficiently explored. Beeri et al. [17] reported that Fe concentrations below ~0.06 wt% had little influence on the hardening response in Al-Sc alloys, whereas Zakharov [18] showed that levels approaching 0.7 wt% promote the formation of coarse Al₃Fe particles that scavenge Sc from solid solution. However, the specific role of Fe at ~0.1 wt%, a level commonly found in commercial alloys and recycled feedstock, has not been systematically studied, particularly in combination with varying Si levels. This work addresses this gap by comparing precipitation and strengthening responses in Fe-lean and Fe-rich Al-Sc-Zr-Si alloys, using a controlled diffusion couple approach.

In this study, we used two diffusion couples: one lean in Fe and the other containing 0.1 wt% Fe, a typical level for commercial Al alloys. In the as-solutionised condition, both diffusion couples exhibited similar hardness as a function of Si, regardless of Fe content. However, differences emerged upon ageing. The Fe-rich diffusion couple consistently displayed higher hardness, particularly during early ageing. For instance, at ~0.075 wt% Si, the alloy with 0.1 wt% Fe reached a hardness of ~40 HV after short ageing times, while the Fe-lean alloy remained around 30 HV.

This increased hardening response in the Fe-rich alloy appears to diminish with extended ageing, as the difference in hardness between the alloys becomes less pronounced. While the overall effect of Fe is relatively minor, it is non-negligible, particularly during the early stages of ageing. A more detailed investigation would be needed to fully understand the influence of varying Fe levels, especially in the context of alloy recycling, where Fe levels tend to increase with greater use of scrap materials.

## 4.3. Dispersoids stoichiometry evolution

Previous studies have indicated that Si and Fe can incorporate into the L1₂ structure of Al₃Sc, likely substituting for Al. The concentrations of Si and Fe within Al₃Sc precipitates have been shown to evolve during ageing, with higher levels present in the early stages, decreasing as ageing progresses [16]. This evolution is likely due to the role of Si and Fe in initial clustering, where Si-Fe-Sc-rich clusters form and subsequently transform into a stable L1₂ structure enriched in Sc. As the precipitates grow, the presence of Si and Fe impurities becomes more dilute.

In the present study, APT measurements were performed on two samples containing 0.1 wt% Si and aged for 1 h at 350 °C. The samples differed in their Fe content, with one containing 0.01 wt% Fe and the other 0.1 wt% Fe. Both alloys exhibited precipitate Sc concentrations of 20–25 at.%, consistent with the stoichiometric composition of L1₂ Al₃Sc. This finding confirms that Fe and Si do not substitute for Sc within the precipitate structure, corroborating earlier reports [16].

Despite the similar bulk Si content (~0.1 wt%) in both samples, the Si concentration within the precipitates varied significantly. In the Fe-lean alloy, the precipitates contained 3.5 at.% Si, whereas in the Fe-
<figure><img src="image_10.png" /><figcaption>Fig. 8. Proximity histogram showing the distribution of Sc (red), Si (grey) and Fe (blue) for two samples with 0.1 wt% Si aged at 350 °C for 1 h, 0.1Fe (circles), 0.01Fe (squares). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

rich alloy, the Si content reached 10 at.%. A small difference in Fe content within the precipitates was also observed, with concentrations of 0.5 at.% and 1.5 at.% in the Fe-lean and Fe-rich samples, respectively.

These results challenge previous studies suggesting that Si content within precipitates depends solely on ageing conditions rather than bulk alloy composition. Our findings clearly demonstrate that Fe has a significant influence on Si incorporation within Al₃Sc precipitates, emphasising the synergistic effects of Si and Fe on the early stages of Al₃Sc formation.

## 4.4. Strengthening contributions

### 4.4.1. Solid solution strengthening in as-solutionised condition

The hardness across the diffusion couple was found to increase as a function of Si in the as-solutionised condition. The solid solution strengthening contribution from Si can be estimated with:

$$ \Delta\sigma_{ss} = \sum_{j} k_{j} C_{j}^{2/3} \qquad \text{Equation 1} $$

<figure><img src="image_11.png" /><figcaption>Fig. 9. Evolution of hardness as a function of Si concentration across the diffusion couple in the solution treated condition (72 h at 610 °C), black dots represent experimental values and the dashed red line is the fit of the data using Equation (1). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

where $C_j$ is the concentration of alloying element j and $k_j$ is a factor specific to each alloying element.

The solid solution strengthening is plotted as a function of Si content in Fig. 9 using the accepted literature value of $k_{Si} = 63.3 \text{ MPa/wt.}\%^{2/3}$ [34]. The very close agreement obtained confirms that the initial difference in hardness across the diffusion couple can be fully explained by the solid solution strengthening of solute Si.

### 4.4.2. Precipitation strengthening

SAXS was used to extract the evolution of radius and of the mean square fluctuation, $\bar{\eta}^2$ which is proportional to the precipitate volume fraction through the electronic contrast (Equation (A.4)). For the purpose of this section, we have assumed that the precipitates have the compositions extracted from the APT for each diffusion couple to calculate the respective electronic contrast and calculate the volume fraction which can be used for the precipitation strengthening model below (see Figure A.2). The influence of precipitate composition on electronic contrast and precipitate volume fraction is discussed further in Appendix C.

The precipitation strengthening can be estimated through the following equations [35]:

$$ \Delta\sigma_{sh} = \frac{M\pi Gk_{2}^{3}}{16} \sqrt{\frac{3f_{v}r}{\beta b}} \qquad \text{Equation 2} $$

$$ \Delta\sigma_{bp} = \frac{M\beta Gb}{r} \sqrt{\frac{3\sqrt{3}f_{v}}{\pi}} \qquad \text{Equation 3} $$

$$ \Delta\sigma_{\text{precipitation}} = \min(\Delta\sigma_{sh}, \Delta\sigma_{bp}) \qquad \text{Equation 4} $$

Where $\Delta\sigma_{sh}$ is the strengthening increment due to precipitation from shearing and $\Delta\sigma_{bp}$ for bypassing; $M=3.06$ is the Taylor Factor, $b=2.86 \AA$ is the Burgers vector of Al, $G = 25.4 \text{ GPa}$ is the shear modulus of the matrix. In this case, $r$ and $f_v$ are the mean radius and the volume fraction, respectively, obtained from SAXS experiments. Finally, $\beta = 0.22$ and $k = 0.07$ are fitting constants and were determined separately. First, $\beta$ was determined by fitting in a regime where $r$ is greater than 2.1 nm [36], which is the transition radius between bypassing and shearing. The
<figure><img src="image_12.png" /><figcaption>Fig. 10. Calculated strengthening increment based on the analytical equations (1)–(3) using the experimentally measured Fv and mean radius from SAXS for the DC without iron (left) and with iron (right) compared to the experimentally observed hardness increment.</figcaption></figure>

expression for the critical radius $r^c = \frac{2\mu b^4}{k\pi} \left(\frac{\sqrt{3}}{2}\right)^{\frac{1}{3}}$ can be used to compute $k$, assuming that $r^c = 2.1$ nm.

Since the equations calculate yield strength increments in units of MPa, a conversion factor of 3 was applied to plot them as hardness increments, allowing direct comparison with the hardness measurements [37–39]. The hardness measurements were subtracted by the solution-treated hardness to isolate the precipitation strengthening component, assuming constant solid solution strengthening. The hardness was modelled for five Si concentrations and three ageing times using the size and volume fraction results from SAXS and plotted in Fig. 10.

The modelled hardness increases with ageing time, showing a trend consistent with the effect of Si content. At low Si levels, the modelled hardness is significantly lower, but it rises and plateaus at higher Si concentrations.

The overall agreement between the modelled and measured hardness is excellent for the Fe-lean sample. However, in the Fe-rich sample, the modelled hardness consistently underestimates the measured hardness, especially at early ageing times. This discrepancy is likely due to the role of Si and Fe on the formation of Al₃Sc precipitates. At early ageing times, the Fe and Si content within Al₃Sc is likely significantly higher, particularly in the Fe-rich sample. This variation in composition, not captured in this study, affects the SAXS contrast and the derived volume fraction. As ageing progresses and Si and Fe are depleted from the precipitates, the agreement between the experimental and modelled hardness improves.

Interestingly, this discrepancy between the model and experiment serves as an indirect indicator of the impact of Fe impurities on precipitation in this system.

## 5. Conclusions

A simple and effective method to fabricate bulk Al alloys with macroscopic composition gradients in Si was validated, enabling high-

throughput analysis of precipitation kinetics. SEM, EPMA, and hardness mapping confirmed the quality and consistency of the gradients.

Two compositionally graded samples—Al-0.1Sc-0.1Zr and Al-0.1Sc-0.1Zr-0.1Fe—were fabricated and aged at 350 °C. Both presented Si gradients from ~0.04 to 0.45 wt%. This allowed to study the individual and combined effects of Si and Fe on the precipitation of Al₃Sc.

A consistent reduction in mean precipitate radius was observed with increasing Si content, with saturation beyond ~0.2 wt%. This trend was observed regardless of Fe content. In parallel, an increase in precipitate volume fraction was observed with increasing Si. A marginally lower volume fraction was measured in the Fe-rich sample, although this falls within experimental uncertainty.

APT confirmed the SAXS-based particle size and volume fraction and provided key insights into precipitate composition. In the Fe-rich sample, Si incorporation into Al₃Sc was nearly three times higher than in the Fe-lean alloy, and measurable amounts of Fe were detected within the dispersoids. These findings confirm the significant role of Fe in modulating precipitate chemistry, particularly at early ageing stages.

The precipitation kinetics data, combined with a strengthening model, matched experimental hardening data well for the Fe-lean alloy. For the Fe-rich sample, the model underpredicted hardness at short ageing times, likely due to unaccounted differences in precipitate composition affecting SAXS contrast and inferred volume fraction. This discrepancy highlights the subtle but important influence of Fe on precipitation behaviour.

Overall, Si was found to have a more pronounced and consistent influence on Al₃Sc precipitation than Fe. An “ideal” Si content of ~0.1 wt% was identified for optimal strengthening within practical ageing times. However, kinetic benefits continue to increase up to ~0.35 wt% Si, even if the peak hardness does not.

These results challenge the prevailing view that Si impurities must be strictly limited in Al-Sc alloys. Our findings suggest that, under suitable homogenisation conditions, Si can be beneficial—even up to ~0.5 wt %—without triggering the formation of deleterious phases.
The interaction between Fe and Si during the earliest stages of pre-
cipitation appears to promote solute clustering and accelerate Al₃Sc
formation, but more work is needed to decouple these effects.

CRediT authorship contribution statement

**A. Techereş:** Writing – original draft, Visualization, Validation, Project administration, Investigation, Formal analysis, Data curation. **M. Perez:** Writing – review & editing, Supervision, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **A. Deschamps:** Writing – review & editing, Validation, Supervision, Formal analysis, Data curation. **S. Seibt:** Visualization, Validation, Resources, Methodology, Investigation, Formal analysis, Data curation. **N. Kirby:** Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation. **L. Jiang:** Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation. **T. Dorin:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgements

This work was supported by a Discovery Early Career Award (project number DE190100614) funded by the Australian Research Council. Deakin University's Advanced Characterisation Facility is acknowledged for use of the CAMECA LEAP 5000 XR APT and JEOL 7800F SEM instruments. The SAXS experiments were undertaken on the SAXS/WAXS beamline at the Australian Synchrotron, part of ANSTO (Beam-time 17487). Special thanks to Graham Hutchinson from University of Melbourne who assisted with running the EPMA measurements on a JEOL FE-EPMA instrument and for data analysis and interpretation.

Appendix A

SAXS experiment and data analysis

The heating stage, used for the SAXS experiments, is equipped with fine adjustment screws that enable precise displacement of a sample along two axes through a clamp. The spring plate ensures enough pressure to keep the sample in contact with the heated part of the stage. The whole set-up was modified and automated to enable remote and precise control of the displacement of diffusion couples across the heated stage. Figure A.1 illustrates these modifications made to the furnace. This includes the addition of a step motor to control the movement screw and a sketch of an adaptive aluminium plate designed to hold the sample in place during the stage movement. These modifications allowed us to align the diffusion couple with the hole in the heated plate, enabling the measurement of scattering signals at various points along the macroscopic composition gradient during the in-situ ageing treatment.

<figure><img src="image_13.png" /><figcaption>Fig. A.1. Motorised Linkam stage (left) and Schematic of the adapting plate (right).</figcaption></figure>

The captured SAXS images were processed using the PyFAI package [40,41]. Detector related corrections have been applied on site. Normalisation was performed based on a measurement done with nothing in the path of the beam at the respective ageing temperatures, and absolute intensity calibration was achieved using a standardised glassy carbon sample. Later corrections for variation of the intensity of incoming beam and thickness of sample were performed using two intensity sensors placed before and after the sample following the procedures described in a review article [42].

Our experiment is equivalent to a series of in-situ experiments performed at the same location on the sample with a 193 s timestep between two measurements. One can, therefore, subtract the first measurement from each location from all the subsequent measurements from that respective location, as is commonly done for in-situ experiments, so that the obtained scattered signal only consists of the contribution of newly formed precipitates.
The model fit approach was used to extract relevant information from the resulting I(q) signal plots. The fitting approach is explained in the PhD work and in work by other authors [43]. A short summary of the equations used is presented below. In this study, the implementation was done using Matlab™.

After subtracting the first measurement from the following ones, performed at the same position on the sample, a signal I(q) is left that is strictly related to the change in the volume and shape of scatterers. This procedure exempts us from further background corrections and corrections associated with the asymptotic q⁴ component.

In the case of this study, the Al₃Sc precipitates are known to be spherical; therefore, the precipitate contribution I<sub>precipitate</sub>(q) can be expressed as follows:

$$I(q) = \int_{0}^{\infty} f(R)I_{1}(q, R)dR \qquad \text{Equation A.1}$$

where f(R) is a distribution function, in this case a log normal distribution, while I₁(q,R) is the contribution to I(q) of precipitate radius R

$$I_{1}(q, R) = (\rho_{p} - \rho_{m})^{2} V_{p}^{2} \left( 3 \frac{\sin(qR) - qR * \cos(qR)}{(qR)^{3}} \right)^{2} \qquad \text{Equation A.2}$$

$\rho_m$ and $\rho_p$ are the electron densities of the matrix and precipitate, and $V_p$ is the volume of a precipitate of radius R

$$f(R) = \frac{1}{\sqrt{2\pi\sigma R}} \exp\left(-\frac{1}{2}\left(\frac{\ln\frac{R_m}{R}}{\sigma}\right)^2\right) \qquad \text{Equation A.3}$$

$R_m$ represents the mean radius of the precipitates and $\sigma = 0.2$ is the width of the distribution.

Furthermore, we also mention that the mean square fluctuation, $\bar{\eta}^2$ as defined in the SAXS formalism explained in the literature [44], is proportional to the square of the electronic contrast and volume fraction of scatterers, only if $f_v \ll 1$:

$$\bar{\eta}^2 \approx \Delta\rho^2 f_v \qquad \text{Equation A.4}$$

Accurate evaluation of the scattering length densities of the precipitate and matrix is required to quantitatively assess the precipitate volume fraction. This requires the knowledge of the exact composition of scatterers at each time step.

The composition of precipitates at specific ageing times was measured using APT. For the APT experiments, the bulk samples (composition presented in Table 1) were aged and later prepared into needles via mechanical polishing to thin films, cutting into rod-like shapes and final electrolytical polishing for sharpening [45].

The resulting volume fraction heat maps are presented in Figure A.2. SAXS experimentally measured volume fraction was observed to be about 40 % smaller than the volume fractions identified via APT. SAXS results are susceptible to many sources of error, like normalisation, material thickness, and intensity correction, azimuthal integration binning, contrast factor assumptions, etc. Moreover, APT results confirm that the concentration of Sc is 0.1 wt% (0.06 at.%) in the alloy, supporting the notion that very little Sc was lost to coarse primary particles. The role of the contrast factor is discussed in Appendix C.

<figure><img src="image_14.png" /><figcaption>Fig. A.2. Heat map of the volume fraction as a function of time and Si concentration measured during in-situ SAXS ageing at 350 °C of (a) the low Fe diffusion couple and (b) the high Fe diffusion couple assuming the precipitate contrast presented in Table C.1.</figcaption></figure>
## Appendix B

### APT peak decomposition

<caption>**Table B. 1**</caption>

Decomposition of APT the mass spectrum for two alloys after isothermal ageing at 350 °C for 1 h.

<table><thead><tr><th></th><th></th><th>Decomposed abundance</th><th>Expected abundance</th><th>% of Range</th></tr></thead><tbody><tr><td rowspan="3">Al-0.1Sc-0.1Zr-0.1Si-0.1Fe</td><td><strong>28 Da</strong></td><td></td><td></td><td></td></tr><tr><td>AlH<sup>+</sup></td><td>99.984</td><td>99.984</td><td>80.716</td></tr><tr><td>Si<sup>+</sup></td><td>0</td><td>92.23</td><td>0</td></tr><tr><td></td><td>Fe<sup>2+</sup></td><td>91.754</td><td>91.754</td><td>19.284</td></tr><tr><td rowspan="6">Al-0.1Sc-0.1Zr-0.1Si-0.01Fe</td><td><strong>29 Da</strong></td><td></td><td></td><td></td></tr><tr><td>AlH<sup>2+</sup></td><td>100</td><td>99.365</td><td>99.926</td></tr><tr><td>Si<sup>+</sup></td><td>0</td><td>4.683</td><td>0</td></tr><tr><td>Fe<sup>2+</sup></td><td>0.282</td><td>0.282</td><td>0.062</td></tr><tr><td><strong>28 Da</strong></td><td></td><td></td><td></td></tr><tr><td>AlH<sup>+</sup></td><td>99.984</td><td>99.984</td><td>56.549</td></tr><tr><td></td><td>Si<sup>+</sup></td><td>92.23</td><td>92.23</td><td>43.451</td></tr><tr><td></td><td>Fe<sup>2+</sup></td><td>0</td><td>91.754</td><td>0</td></tr><tr><td rowspan="3">Al-0.1Sc-0.1Zr-0.1Si-0.01Fe</td><td><strong>29 Da</strong></td><td></td><td></td><td></td></tr><tr><td>AlH<sup>2+</sup></td><td>99.969</td><td>99.365</td><td>94.352</td></tr><tr><td>Si<sup>+</sup></td><td>4.683</td><td>4.683</td><td>5.629</td></tr><tr><td></td><td>Fe<sup>2+</sup></td><td>0</td><td>0.282</td><td>0</td></tr></tbody></table>

It can be observed from the peak deconvolution that in the high Fe sample, the Fe is present in precipitates early in the precipitation process. This is indicated by the decomposed abundance of $Fe^{2+}$ at 28 and 29 Da. In the low Fe sample, the decomposed abundance is 0 compared to theoretical expected abundances, while in the case of the high Fe sample the decomposed abundance is closely related to the expected abundance.

## Appendix C

### Impact of precipitate composition on electronic contrast

Considering the results discussed before and visible in Fig. 8, it was decided to assign a different electronic contrast between the two samples in order to determine precipitate volume fraction from the SAXS data. Table C.1 presents the electronic contrast used under the assumed precipitate stoichiometry, which is based on the APT reconstructions.

<caption>**Table C. 1**</caption>

The electronic contrast calculated based on the assumed precipitate stoichiometry resulting from the precipitate composition measured via APT.

<table><thead><tr><th></th><th>Assumed precipitate stoichiometry</th><th>Electron contrast c<sup>2</sup></th></tr></thead><tbody><tr><td>Stoichiometric precipitates</td><td>Al<sub>3</sub>Sc</td><td>0.0081</td></tr><tr><td>Fe lean alloy</td><td>Al<sub>2.84</sub>Fe<sub>0.02</sub>Si<sub>0.14</sub>Sc</td><td>0.00835</td></tr><tr><td>Fe rich alloy</td><td>Al<sub>2.54</sub>Fe<sub>0.06</sub>Si<sub>0.4</sub>Sc</td><td>0.01054</td></tr></tbody></table>

The electronic contrast measures the difference in electron density between the matrix and the dispersoids. In Table C.1, we can observe that the contrast squared between Al and Al<sub>3</sub>Sc is the smallest. This minor difference is due to the higher number of electrons introduced when replacing an Al atom with Sc and, the minor difference in the unit cell between α-Al and L<sub>1</sub><sub>2</sub> Al<sub>3</sub>Sc.

In the case of the Fe-lean alloy, replacing 3.5 at.% of Al with Si leads to a minor change in the contrast, due to Si atoms having only one more electron than Al. However, when the replacement rates are higher, as seen in the Fe-rich alloy, close to 10 at.% then the change in the electron contrast is significant, leading to an increase of approx. 25 %

Fe has an atomic number that is considerably larger than Al's, having the potential to significantly modify the contrast factor. However, in both samples we identified less than 2 at. % of Fe replacing Al, therefore, its impact is rather limited.

From Table C.1 we observe that the evolution of the contrast factor as a function of stoichiometry, when adjusting for Si and Fe replacing Al in the precipitates, dictates the inverse proportionality between SAXS volume fraction and contrast factor squared. Therefore, any increase in contrast factor leads to a reduction in volume fraction. This explains why the volume fraction observed in the Fe-rich samples, via SAXS, appear slightly smaller than in the Fe-lean samples, even though the mean squared fluctuation intensity was similar.

## Data availability

Data will be made available on request.

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