# Laser-induced breakdown spectroscopy (LIBS) imaging for carbon quantification in archaeological ferrous alloys

Sarah Richiero, Xueshi Bai, Clothilde Comby-Zerbino, Nicolas Herreyre, Agnès Piednoir, Manon Gosselin, Philippe Dillmann, Florian Téreygeol, Nicolas Wilkie-Chancellier, Vincent Motto-Ros, et al.

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## ► To cite this version:

Sarah Richiero, Xueshi Bai, Clothilde Comby-Zerbino, Nicolas Herreyre, Agnès Piednoir, et al. Laser-induced breakdown spectroscopy (LIBS) imaging for carbon quantification in archaeological ferrous alloys. Journal of Analytical Atomic Spectrometry, 2025, 40 (7), pp.1746-1753. 10.1039/D4JA00432A. hal-05357339

HAL Id: hal-05357339

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Submitted on 23 Feb 2026

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<figure><img src="image_2.png" /></figure>
# Laser-induced breakdown spectroscopy (LIBS) imaging for carbon quantification in archaeological ferrous alloys

Sarah Richiero, Xueshi Bai, Clothilde Comby-Zerbino, Nicolas Herreyre, Agnès Piednoir, Manon Gosselin, Philippe Dillmann, Florian Téreygeol, Nicolas Wilkie-Chancellor, Vincent Motto-Ros, Vincent Detalle

## Introduction

Steel has been used since the Iron Age in the Occident. This alloy of iron and carbon has excellent mechanical properties which led it to be used in ancient societies in a wide range of products and in different spheres of public, private and military life. Nevertheless, the heterogeneous thermodynamic and kinetics conditions in ancient ironmaking processes led to the fabrication of heterogeneous products, particularly concerning the quantity and distribution of carbon (or carbides) in ancient ferrous alloys. In this context, analysing ancient iron-based artefacts to determine their nature (and their heterogeneity) provides a lot of information about skills and technologies. The study of these alloys using physico-chemical analytical techniques offers keys to understand the evolution of techniques and the nature of production processes and trade between different geographical areas in the products of ancient societies.

The carbon concentration mainly determines the metal's mechanical properties. Carbon is associated with iron during smelting of ore or heat treatment in production. Depending on the carbon content and also on the heterogeneity/homogeneity of its distribution, dramatic changes in the physical properties of steels are observed. In archaeological steels, the carbon concentration typically ranges from less than 0.02 wt% to about 1.00 wt%. In contrast to modern steels, due to the thermodynamic conditions of production furnaces, carbon in archaeological steels is usually heterogeneously distributed in the iron matrix and this distribution offers clues to manufacturing techniques and post-reduction treatments (struck or rolled steels, Damascus, welds, etc.). Therefore, the study of the carbon distribution in an ancient iron-based artefact enlightens the nature and the quality of the metal and allows archaeometallurgists to categorize iron-based metals. This is the main reason why it is crucial to analyse this ferrous alloy using spatially resolved techniques. Moreover, in addition to carbon, phosphorus and manganese distribution provides valuable information about ancient production techniques and is a key factor in archaeometallurgical studies.
To study carbon distribution in ancient metals, archaeometallurgists for decades have been using the metallographic analysis by optical microscopy after Nital etching of polished cross-sections sampled on artefacts. Carbon quantification with this method is based on visual (or by image analysis) estimation on microphotographs of the quantity of the Fe3C cementite phase. Unfortunately, this method is only effective in the case of very slow cooling near thermodynamic equilibrium conditions. With faster cooling rates, other phases can form and the carbon concentration is impossible to evaluate. These near-equilibrium conditions are not often guaranteed by old production techniques. Moreover, fast cooling could be a deliberate act of the craftsmen to harden the alloy (quenching).

Therefore, an imaging technique is required for allowing access to quantitative values in a rapid and easy manner, over an area large enough to observe the distribution of these elements. The detection of carbon in steels is difficult, because of its relatively low concentration (sometimes below 0.02% wt). But the nuclear reaction analysis (NRA) showed its capacity to reach the quantitative analysis required; however, it requires a particle accelerator and the lateral resolution of the external beam of these instruments does not yet allow imaging over a large area.

Thus, we propose the micro-laser-induced breakdown spectroscopy (μ-LIBS) technique for studying the carbon distribution in archaeological steels. Our previous work has demonstrated that LIBS is able to detect carbon and its spatial distribution. However, this application should overcome two challenges: the first is that iron exhibits numerous optical emission lines, with the only non-interfered carbon line found in the far UV region at 193.1 nm. Mastery of the detection protocol and intensity extraction from this line is essential to obtain quantitative results for a large number of spectra (over one million). The second challenge relates to cross-section preparation, where various sources of contamination can affect the alloy's intrinsic quantitative information, particularly in terms of carbon emission from the use of diamond paste, lubricating and cleaning solutions, dust, etc.

To address these difficulties, in this study, the capabilities of μ-LIBS imaging are examined to define the experimental protocol and further to obtain high-definition images of the different elements in the steel sample. In addition, LIBS measurements can provide not only the carbon distribution, but also the distribution of other characteristic elements (including Mn and P). To evaluate the accuracy of μ-LIBS imaging, the C distribution map obtained on an archaeological iron base sample will be compared with those acquired through microscopic examination following the application of the Nital reagent.

## Materials and Methods

In this study, five certified standard steel samples from the National Institute of Standard
and Technology (NIST) were used to construct the carbon calibration model. Then, an
archaeological sample was used to validate the quantitative µ-LIBS imaging of carbon in a
real case study.

The carbon concentrations of standard steel samples extend from 0.015 wt% to 0.871 wt%.
This range may be representative of concentrations usually observed in archaeological iron-
based artefacts.

The archaeological sample is a cross-section taken from a plate (Ref: CL13286, CM16, US6016, Fig. 1a) dating to the 13th century and coming from the mediaeval workshop of the Castel-Minier site located in the present-day commune of Aulus-les-Bains (France). This sample has been cut and embedded in resin to get a polished cross-section (Fig. 1b). This cross-section sample has been studied by microhardness testing, observation after Nital etching and LIBS. Concerning this latter technique, the cross-section is relatively large (5.5 x 1 cm), enabling us to construct an image with a sufficient number of analysed points. The area relevant to our study is the light grey zone visible in Fig. 1b, corresponding to the unaltered metal.

The µ-LIBS experimental setup used was from the ILMTech-OPTOLYSE platform (ILM-UMR 5306, CNRS-Université Lyon1, Villeurbanne, France). Basically a Nd:YAG laser at a fundamental wavelength of 1064 nm was operated with a pulse duration of 8 ns (FHHM) and a repetition rate of 100 Hz (Centurion GRM, Lumibird, France). A powermeter was used to measure the stability of the laser energy as it leaves the cavity. A shutter controls the arrival of the laser shots on the sample. The pulse energy was set at 1 mJ using a computer-controlled attenuator (ATT1064, Quantum Composer, USA).

Behind the shutter, two mirrors reflecting IR light guide the laser beam to a beam expander X3, which controls its diameter as it enters the focusing lens. A 15× magnification objective (LMM-15X-P01, Thorlabs, Germany) was used to focus the laser pulse on the sample surface. A dichroic mirror reflects the laser and transmits visible light so that an optical monitoring camera receives the image of the sample. Samples were placed on an XYZ motorized stage system and scanned position by position with a predefined lateral interval. In all of the experiments shown below, a lateral step size of 22 µm was employed. An argon flow of 1 L per min blowing through the interaction zone was used both to reduce the surface contamination due to ablated material deposition from previous laser shots and to enhance the plasma emission. All experiments were conducted at room temperature under ambient pressure conditions.

The diameter and depth of the ablation resulting from a single laser shot with the µ-LIBS instrument used were measured by using an atomic force microscope (AFM) and an optical profilometer. The image of the impacted zone and the corresponding profile obtained at the centre of the impact are shown in Fig. 2b and c. The depth of the ablated material was estimated at 3.83 µm per laser shot and the diameter is at around 22 µm which
approximates to 0.93 ng of metal analysed per point, as determined by AFM (AFM MFP-3D, Asylum Research, Oxford Instruments, UK) and optical profilometer (Contour GT-K 3D Optical Microscope, Bruker, UK) analyses.

Two spectrometers were used to perform the spectral acquisition. The first spectrometer used was a Czerny-Turner spectrograph (Shamrock 303i, Andor Technology, Northern Ireland) equipped with an intensified CCD camera (iStar DH340T-18F-E3, Andor Technology, Northern Ireland), synchronized to the Q-switch of the laser. The quantum efficiency curves (i.e. E3) of this camera intensifiers offer a quantum efficiency above 20% at 200 nm. The acquisitions were performed with a typical delay and gate of 1 µs and 5 µs, respectively. The spectrometer was configured with an 1800 lines per mm grating, blazed at 250 nm. In this configuration, the spectral range was from 180 to 227 nm, with a spectral resolution of around 0.1 nm. This study focused on the carbon line at 193.1 nm due to its most intense accessible transition and minimal interference from iron. To optimize the detection of the 193.1 nm carbon emission line, a specific collection system was developed (Fig. 2) using two CaF2 lenses of 1" diameter (L1 spherical and L2 cylindrical) both with 50 mm focal distance and an enhanced deep UV coating (Edmund Optics, USA) offering a reflection coefficient >90% at 190 nm. In addition to carbon detection, this range was also adequate to detect iron, phosphorus (213.62 nm) and silicon (212.41 nm).

The second spectrometer was a Czerny-Turner spectrograph (Shamrock 500, Andor Technology, Northern Ireland) equipped with an intensified CCD camera (iStar DH340T-18F-E3, Andor Technology, Northern Ireland), synchronized to the Q-switch of the laser. It was configured with a 2400 lines per mm grating that registered the spectra with a wavelength ranging from 394 to 411 nm for the purpose of detecting manganese (403.23 nm).

Protocol and data processing for LIBS imaging. To evaluate the effect of C contamination on the surface, we worked in 3D scanning mode. We defined x, y, and z axes to obtain a 3D scan with resolved values for both the surface (x and y directions) and depth (z direction). The lateral stage carrying the sample keeps moving while the laser fires with a controlled speed, so that each position corresponds to a laser shot and it can maintain a regular interval between the two laser shots as well.

The first phase of this study focused on the mapping of standard NIST steel samples in order to evaluate the imaging system (operating at 100 Hz) on carbon quantification. So, four imaging measurements of 250 (X) × 100 (Y) points on the standard samples were performed successively by rising the sample with a step of around 3 µm in the Z direction (perpendicular to the surface plan) after every scan, corresponding to the crater depth.

The archaeological sample was analysed over three scans, each scan with 1300 (X) × 550 (Y) points. For each shot, the spectrum corresponds to a pixel of the elemental image. The intensity of the selected line corresponding to the element of interest is extracted by
defining a baseline on a spectral range of a few nanometres and subtracting it from the
chosen emission line. The obtained 2D matrix contains spatial and spectral information
saved in .txt format. ImageJ software (National Institutes of Health USA) was used to apply a
false colour scale and get an elemental image. The signal from the resin was corrected in the
image display by defining a threshold corresponding to the intensity of the iron contained in
the resin and removing all the spectra with the iron line intensity below this threshold. An
elemental mask was applied to the carbon image in order to enhance its legibility and
ensure that the colour scale was more appropriate. Then the same protocol was applied to
the images of the other elements (P, Mn, Fe, ...). A threshold based on the signal from non-
metallic inclusions containing manganese was defined to isolate only the image showing the
presence of elements of interest within the metal.

To reveal the microstructure of the metal and deduce the distribution of the carburized
zones, etching with the Nital reagent (4% mass) was carried out. The steel cross-section
was etched on it for 15 seconds followed by rinsing with water and then ethanol. Then the
heterogeneity of steel phase grains was observed with an optical microscope. So, by
referring to the phase diagram and visually estimating the proportion of one microstructure
to another, an estimate of the carbon concentration is possible.

Results and Discussion

The objective of our study is to obtain a quantitative image of carbon in the archaeological
steel sample using the µ-LIBS imaging technique. The NIST standard reference samples
were first investigated to validate the measurement protocol and to establish the
calibration curve. Then the approved experimental protocol was applied to examine the
archaeological piece in order to have quantitative information on carbon distribution.

With the help of our specifically designed experimental set-up, the selected wavelength
range and spectral resolution enable the emission lines corresponding to phosphorus,
chromium, manganese, and carbon to be identified. Fig. 3 shows the average spectra for the
five NIST standard samples. The atomic emission line of carbon at 193.0 nm varies in
intensity according to the carbon concentration. It is worth noting that defining the
continuum threshold is challenging due to the carbon emission line being positioned
between iron lines within a spectral region near the detection limit. To obtain comparable
results and increase accuracy, the same spectral region was selected across all spectra for
each sample. The same spectral region was defined to precisely extract the intensity of the
carbon line and the iron line for the five NIST samples to build a calibration curve.

Fig. 4 presents the images of emission intensity obtained for the carbon atoms at 193.0 nm and the iron ions at 216.6 nm. The images are arranged from the highest carbon content on the left side (NIST 1264a) to the lowest carbon content on the right side (NIST 1766). For
each sample, from the top to the bottom, the four intensity images are displayed from the surface (Scan 1) to the fourth layer (Scan 4) with about 12 µm in whole depth. A stable iron emission intensity indicates that the experimental measurement conditions are relatively consistent for each sample over each scan since iron is the major element. As for the carbon emission image, a stronger emission from the first layer (the red colour) and a more heterogeneous distribution in this layer compared to the subsequent layers may be the results of CO2 adsorption on the surface, C deposition due to preparation (diamond discs, solvents, etc.) and the onset of alteration. Therefore, the first layer needs to be ignored for the quantification of carbon in the sample. From the second layer (Scan 2), the sample exhibits the homogeneity of carbon distribution as expected, because the carbon emission intensity is as stable as that of iron. Laser ablation involves the utilisation of a pulsed laser to vaporize a minute quantity of material from the sample. This method enables the analysis of the material beneath the surface contamination layer. Consequently, from the second pass onwards, the contamination is eliminated to a depth of approximately 3 µm beneath the initial surface.

The laser ablation in our experimental configuration can be considered a stoichiometric one. During the interval of time when the emission spectrum was recorded, the emission intensity from one species (atoms or ions) is proportional to the number density in the plasma. So the conservation of the stoichiometry between plasma and the target allows building a calibration curve for carbon content with its emission intensity. Therefore, the calibration curve was established by linearly fitting the experimental measured intensity of the C I 193.1 nm line normalized by the intensity of the iron ionic line at 204.06 nm, as an internal standard. Then averaging the normalized carbon intensity from 20 000 analysed points taken at the centre of the second scan for each sample was carried out in order to avoid the possible variation from the frontier during recording the mapping and the surface contamination.

The calibration curve is shown in Fig. 5, whose linear determination coefficient (R²) obtained for this curve is 0.996 (Fig. 5). Furthermore, the line does not pass through the origin, which may be due to the complexity of the iron spectrum and possible interference in the region where carbon emits, as observed in the simulated spectra. This interference would not affect the quantitative results as long as the protocol is rigorously applied to all standard and test samples.

The detection limit requirements are based on those of the standard method for measuring carbon in iron—microscopic analysis after Nital etching. Although this technique does not allow for precise estimation of measurement uncertainty, a precision of at least 0.1% is required, as this represents the lowest carbon concentrations typically found in archaeometallurgical samples. For NIST 1264a, the highest carbon concentration, a 95% confidence interval yields an uncertainty of ±0.1% C wt. For NIST 1762a, located at the centre of the curve, the uncertainty of the estimated C concentration is about ±0.08% wt. A change of just 0.1% by weight in carbon content can lead to significant differences in the
thermomechanical behaviour of steels. Consequently, archaeometallurgists require this
level of precision in quantification to classify different types of Fe-C alloys and distinguish
the nature of various materials. This μ-LIBS calibration method meets these requirements,
in contrast to metallography, which lacks comparable precision.

The µ-LIBS analysis enabled the construction of a multielemental distribution image (Fig. 6). The archaeological sample was analysed over three scans, with each scan comprising 1300 × 500 analysed points, resulting in the images in Fig. 6, that correspond to Scan 2, being composed of 650 000 pixels. The iron image confirms the stability of the signal, so the variations observed for the other chemical elements correspond to actual changes in the composition.

The carbon image demonstrates that µ-LIBS imaging can detect variations in the concentration, as it reveals areas of variation consistent with those observed in the metallographic image of the sample etched with Nital acid. This representation is promising for quantifying this element. The presence of phosphorus and manganese was identified in the metal, where an anti-correlation appears to exist among the concentrations of these three elements. Specifically, it seems that as the carbon concentration increases, the phosphorus concentration decreases. In contrast, the relationship between manganese and the other elements is less evident. Variations in manganese seem to follow those of carbon, and certain areas where carbon appears very intense are located at the periphery of the nonmetallic inclusions removed during the image processing. The distribution of these elements may reflect the production process and raises new questions for further archaeometallurgical studies. The intensities shown in Fig. 6 provide a qualitative indication of the distribution of each element; they are not directly comparable among themselves, as the intensity scale has been adjusted independently to clearly visualize the distribution of each element.

By applying the calibration curve to the normalized carbon intensity of the archaeological
sample (Ref: CL13286, CM16, and US6016), the quantified distribution is obtained and
shown in Fig. 7b. The estimation error for this quantification, shown in Fig. 7d, was
calculated as the distance between the upper and lower limits of the 95% confidence band
for each point. When comparing with the quantitative results obtained through
metallography (Fig. 7c), the distributions of carbon and concentration variations are
consistent with the metallographic analyses.

However, the areas identified as the most carburized are estimated to be significantly
higher in LIBS compared to the values obtained from metallography. Metallography
estimates a concentration of approximately 0.8% by weight for the most cemented region,
while µ-LIBS estimates are around 1% for this area. On the one hand, the identification by
OM after Nital etching of the microstructure corresponding to a eutectoid concentration on
the Fe-C diagram can be questioned. On the other hand, the concentration range
represented by the reference steels reaches 0.871% by weight; this value is likely below the
maximum concentration that is findable in the studied archaeological sample. Extending the concentration ranges represented by the calibration curve could allow for measuring larger concentration variations. A more localized carbon concentration could reach the level of crystalline phases as cementite (Fe3C), containing 6.7 wt% of carbon and exceed the typical carbon content found in conventional steels. For the less carburized areas, the LIBS results agree with those obtained by quantitative metallography, as well as with the intermediate values. This allows for the validation of the quantitative results obtained.

Furthermore, in the quantitative LIBS image, the distinguished areas correspond closely to the image obtained by metallography. This indicates that the LIBS technique is capable of detecting fine spatial variations in the concentration. These results can be directly exploited by calculating the average concentration to obtain a global representation of the carbon content of the object, as well as by more specifically measuring a precise area, thanks to the spatial resolution of 22 µm. However, spatial resolution plays a key role in interpreting the results. The calibration curve is based on an average surface of about 10 mm² by 20 000 laser shot scanning, which is much larger than the size of the laser spot (10 µm). This average value allows for a result that is more representative of the global area, while the resolution of 22 µm, with a laser impact of 10 µm, reveals the microscopic heterogeneity of the steel, which could give a new vision for understanding the steel producing process. Thus, the size of the laser spot becomes a decisive point for performing quantitative analysis. As demonstrated in Fig. 8, the microscopic heterogeneity of the steel can be illustrated to show the impact of the laser spot size on the quantitative analysis of a homogeneous sample on a large scale and heterogeneous distribution of the element of interest on the microscale.

The red circles represent the diameters of the different laser beams that can be employed. With a resolution of 22 µm obtained with a 10 µm laser beam (Fig. 8c), the results are sensitive to the heterogeneity of the microstructure. This means that high resolution allows for a clear observation of the distribution of carbon in a quantitative way.

Reducing the size of the laser beam makes it possible to provide access to readings of the microstructure; however, the carbon concentration usually obtained by the bulk analysis of the standard samples needs a certain number of microanalytical accumulations over the same area that can compensate for the microstructure effects. Indeed, the shot-to-shot variation in carbon intensity is primarily due to the heterogeneity of the microstructure. Depending on the study's objectives, it is necessary to estimate the optimal laser beam size for effective LIBS imaging.

## Conclusions

This work demonstrates the capability of producing quantitative carbon images for the
study of ancient archaeological ferrous alloys using μ-LIBS. The method also retains the ability to measure and generate simultaneous images of other characteristic elements, such as phosphorus and manganese, enabling researchers to explore new issues, such as the spatial (anti)correlation of characteristic elements. These elements can provide complementary information about the provenance of the metal and the chemical signature of different alloy production techniques.

In addition, these results suggest that μ-LIBS imaging is a complementary technique that effectively addresses the limitations of metallographic methods in carbon quantification. μ-LIBS imaging provides sufficient detection sensitivity to offer a rapid and comprehensive view of the distribution and quantity of chemical elements. For now, the technique is calibrated for average carbon concentrations ranging from 0.015% to 0.871% by weight, but the range could be extended to measure higher concentrations. Therefore, quantitative μ-LIBS imaging holds significant promise and could emerge as a key technique in archaeometallurgy. For future work, selecting an appropriate laser spot size is crucial to accommodate diverse μ-LIBS imaging requirements and ensure accurate quantitative analysis. Additionally, extending the calibration curve to cover a wider concentration range and additional elements beyond carbon such as phosphorus is essential to enhancing the μ-LIBS reliability and broadening its applicability. Improving or adding internal standards and even developing calibration-free methods are also critical to achieving more accurate and precise quantitative results in future applications.
<figure><img src="image_3.png" /><figcaption>Figure 1: (a) Archaeological sample (Ref: CL13286, CM16, and US6016) studied in this work; the yellow line denotes the cross-section from cutting and the * represents the same point on the sample and the optical image of the cross-section (b).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2: (a) µ-LIBS instrumentation set-up; (b) 3D AFM image of the crater; (c) line profile taken from the AFM image along the black line and the Lorentzian profile.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 3: Average raw spectra obtained for each reference sample.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4: Carbon and iron signal images across four mapped layers.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 5</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 6: Multi-elemental µ-LIBS images of a Castel-Minier sample (Fe, C, P, and Mn) and the</figcaption></figure>
optical microscopy image from metallography after Nital etching.

<figure><img src="image_9.png" /><figcaption>Figure 7: (a) The optical microscope image after Nital etching of the cross section of the archaeological sample; (b) carbon quantitative µ-LIBS image; (c) the obtained carbon content distribution by the metallographic method; (d) Estimation error for the quantitative µ-LIBS mapping.</figcaption></figure>
<figure><img src="image_10.png" /></figure>

Figure 8: The optical microscopy image after Nital etching of the NIST 1286 sample with different laser spot sizes.

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