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# Visualizing organic matter content in organic-rich shales through Terahertz imaging

Federico Sanjuan, Pascale Sénéchal, Iris Valido, Maria Angels Subirana, Charles Aubourg, Bertrand Fasentieux, François Baudin, Maxime Bernier

► To cite this version:

Federico Sanjuan, Pascale Sénéchal, Iris Valido, Maria Angels Subirana, Charles Aubourg, et al. Visualizing organic matter content in organic-rich shales through Terahertz imaging. Fuel, 2024, 374, pp.132430. 10.1016/j.fuel.2024.132430. hal-05093188

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# Visualizing organic matter content in organic-rich shales
through Terahertz imaging

Federico Sanjuan*<sup>a</sup>, Pascale Sénéchal<sup>b</sup>, Iris Henriquez<sup>c,d</sup>, Maria Angels

Subirana<sup>c</sup>, Charles Aubourg<sup>b</sup>, Bertrand Fasentieux<sup>e</sup>, François Bauding<sup>g</sup>,

Maxime Bernier<sup>f</sup>

<sup>a</sup>Université de Pau et des Pays de l'Adour, E2S-UPPA, CNRS, LFCR, Avenue de l'université, Pau, 64013, France

<sup>b</sup>Université de Pau et des Pays de l'Adour, E2S UPPA, CNRS, DMEX, Avenue de l'université, Pau, 64013, France

<sup>c</sup>CNRS, Université de Pau et des Pays de l'Adour, E2S UPPA, Institut des Sciences Analytiques et de Physico-Chimie pour l'Environnement et les Matériaux (IPREM), UMR 5254, Avenue de l'université, Pau, 64000, France

<sup>d</sup>GTS Research Group, Department of Chemistry, Faculty of Science, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain

<sup>e</sup>Université de Pau et des Pays de l'Adour, Géosciences, College STEE, Avenue de l'université, Pau, 64013, France

<sup>f</sup>CROMA, UMR 5130, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, Grenoble INP, Avenue de l'université, Le Bourget-du-Lac, 73370, France

<sup>g</sup>Sorbonne Université, CNRS, Institut des Sciences de la Terre, 4 place Jussieu, Paris, 75005, France

## Abstract

Fine-scale visualization and quantification of the distribution of organic matter within shaly petroleum source rocks like oil shales, offers valuable insights into their suitability for energy applications, such as unconventional energy sources. Although methods exist for imaging organic matter content in organic-rich shales, such as X-ray microtomography and acoustic wave imaging, there is currently no non-ionising, contactless, non-destructive method capable of imaging samples of several square centimetres and sensitive to nano-micrometre organic matter content. This possibility would provide new and complementary information to obtain a better characterisa-
tion of the reservoir. Thus, to address this gap, we propose a novel method that uses measurements of electromagnetic waves within the Terahertz (THz) band, acquired through a Terahertz time-domain spectrometer operating in transmission mode. It uses information about the dielectric permittivity anisotropy of the material to estimate, for the first time, the average total organic carbon (TOC) per pixel across the thickness of the rock. We therefore present, first THz TOC images of organic-rich shale samples, specifically from the Vaca Muerta Formation (Neuquen Basin, Argentina). The images show a TOC pixel variation ranging from 3 to 6 % with a relative pixel error of less than 6 %, i.e. for a TOC value of 4%, the absolute error would be ±0.24 %. The method have been validated with combined results obtained by Rock-Eval® analysis, Raman and infrared spectroscopy and X-ray microscopy. The reliability of this approach lies primarily in the consideration that the main source of dielectric permittivity anisotropy must be caused by the varying organic matter content of the different rock strata.

**Keywords:** Organic-rich shale, TOC imaging, Terahertz imaging, Oil shale

**PACS:** 0000, 1111

**2000 MSC:** 0000, 1111

# 1. Introduction

The petrophysical properties of organic-rich shales have been studied over the years for their importance especially in the field of energy. A better understanding of their properties has an impact in energy resource exploration, economics, technological advancements, environmental considerations and geological research [1].
In particular, these organic-rich sedimentary rocks, holds significant potential as an unconventional energy sources due to their abundant kerogen content (organic matter), which can be converted into shale oil and gas through various extraction processes such as pyrolysis, retorting, and in-situ conversion [1, 2].

In addition, organic-rich shales play a role in various geological and engineering applications as cap-rocks [3, 4]. In these cases they are key for containing fluids-gas, such as hydrocarbons or CO2 [5]. Shales’ organic matter (OM) is particularly linked to the rock’s sealing capacity, which is essential for preventing the upward migration of fluids from the reservoir as it can affect the mechanical strength and stability of the cap-rock. Moreover, OM may undergo chemical interactions with fluids in the reservoir that can impact the shales reactivity and stability over time [6].

Also, in applications that involve the injection or extraction of fluids at elevated temperatures, such as in enhanced geothermal systems or certain oil and gas recovery methods, the OM content can influence the shale’s thermal properties [7].

Hence, visualizing the distribution, composition, morphology, and structure of organic components within the organic-rich shales provides valuable insights into the reservoir’s potential. Various imaging techniques are employed for this purpose [8, 9], e.g. scanning electron microscopy, optical microscopy, transmission electron microscopy, atomic force microscopy, confocal laser scanning microscopy, cryo-electron microscopy, X-ray tomography (XCT), nuclear magnetic resonance imaging, Raman microscopy, laser-induced fluorescence, Fourier-transform infrared spectroscopy (FTIR)
and acoustic wave imaging. Usually, a combination of these imaging tech-
niques is used to obtain a comprehensive understanding of the rock's OM.

In recent years, several applications have been developed in the field of
energy using electromagnetic measurements in the Terahertz (THz) band [10,
11, 12]. In particular, in addition to established techniques, a novel method
for imaging oil shale surfaces has been developed [13]. It has the advantage of
being non-destructive, non-ionising, contactless and sensitive to OM content.
Moreover, it allows imaging of samples of several centimetres squares, which
is not possible with most of the methods mentioned above. Notably, optical
microscopy and XCT can also image larger sample sizes, but since they are
diffraction limited, the smallest OM size that can be observed is of the order of
microns. Thus, they cannot be sensitive to concentrations of smaller particles
of OM, which, in contrast, is possible in THz images. Although each THz
pixel is submillimetre in size, its sensitivity to the composition of OM ranges
from the nanoscale to the microscale, exploiting information derived from the
effective refractive index. However, it should be noticed that the proposed
THz method in [13] does not allow the quantification of the amount of OM
per pixel within the rock.

Therefore, to overcome this, we present a new method utilizing Tera-
hertz measurements in transmission mode. It uses data on the dielectric
permittivity anisotropy of the shale, which is related to its OM content
[12, 14, 15]. Consequently, the method allows the estimation of total or-
ganic carbon (TOC) per pixel over the thickness of the rock. This new in-
formation is particularly important to improve resource estimation, optimise
exploration and production and enhance geological research.
The manuscript presents the development of the method and the first THz-TOC images from two organic-rich shale samples. As the results in both cases are similar and in line with expectations, the results of one of them are presented in the supplementary material to facilitate the reading of the manuscript. The method was validated after sample characterisation by Raman spectroscopy, FTIR, Rock-Eval analysis and SCT.

## 2. Material and methods

### *Samples*

This study used two organic-rich shale samples obtained from the Vaca Muerta Formation (upper Jurassic-lower Cretaceous) in the Neuquén Basin, Argentina (courtesy of Total Energías). The samples, named A and B, come from the same core. Sampled parallel to the bedding, the stratification is perpendicular to their thickness. Both samples are cylindrical in shape, measuring 25 mm in diameter and 2.1 mm in thickness, with a parallelism error of less than 1 %. The latter is important because a larger error can cause a significant systematic error when extracting the physical THz parameters [16].

In addition, 0.5 g of powder was prepared from the same cylindrical core for subsequent Rock-Eval analyses [17]. The excess of the material compared to the quantity required (≈50 mg), gives enough material to test repeatability. The results revealed a TOC$_A$ = 3.26%. With a vitrinite reflectance of 0.7 %, the sample is within the oil window.

A thin section (30 µm thick) was prepared with a sample obtained from the same cylindrical core. The microfacies was examined by natural and po-
larised light microscopy and using UV light (fluorescence) microscopy. The images are shown in figure 1. The microfacies is a calcareous mudstone without visible lamination. The detrital fraction is dominated by altered quartz and micas (mainly muscovite) with some traces of glauconite. OM is abundant and associated with clays. Remains of higher plants (leaf debris) can be large (≈5 mm long) with a fluorescent part that probably corresponds to cuticle remnant. However, small black plants (infra mm) dominate particulate OM. Pyrite is abundant, either in sub-automorphic grains or more commonly in the form of framboids. The main bioclasts are calcitized radiolarians, fine bivalves and rare foraminifera. The sedimentary fabric shows two slightly different microfacies: one where radiolarians and organic matter (fluorescent) are more abundant, and the other where OM is less abundant (not very fluorescent) with few radiolarians and more faecal pellets. Further information on the samples can be found in the supplementary material.

## Terahertz imaging of organic matter

The commercial system TeraPulse LX from the English company Tera-view has been used with its "chamber" module in this study. Images have been obtained by raster scanning the sample in the XY-plane with a spatial step of 200µm.

The proposed method exploits the findings from the articles [14, 15], where the authors found a linear relationship between the OM content (TOC) and the anisotropy (birefringence) of the THz dielectric permittivity in oil shales. In particular, the anisotropy evidenced by the variation of the refractive index.

To obtain the THz-TOC images, we performed the following steps:
<figure><img src="image_3.png" /></figure>

Figure 1: Microphotographs of the sedimentary fabric. a) natural light and b) same view under fluorescence mode (UV light) showing a medium fluorescence (pale yellow to green in colour) of OM associated to clay minerals and intense fluorescence (yellow) of hydrogen-rich organic particles. Non fluorescent organic particles are woody debris.

1- Given the uncertainty regarding the position of the optical axes of the sample, our initial step was to precisely locate them. For this, a series of measurements were performed by rotating the sample every 10°. With each result, the refractive index per angle was obtained with the method described in [18], showing for one selected frequency an ellipsoidal variation as shown in the articles of Miao et al. [14] and Sanjuan et al. [19]. Finally, the location of the optical axes has been at the angles for which the refractive index was maximum and minimum.

2- Two THz images were obtained. One, irradiating the sample with the polarisation of the electromagnetic (EM) wave parallel to one optical axis (fast axis) and then another irradiating with the polarisation parallel to the other one (slow axis). In each pixel/spot irradiated a time signal was
acquired. Each of them was processed in the frequency domain to obtain the
refractive index as described in [18]. In order to work with the maximum
spatial resolution to obtain a more detailed image, the refractive index values
were selected at the maximum frequency where there is still a good signal-to-
noise ratio (>20 dB). In our case, it was at 1 THz. These values were then
represented with a colour scale to create the coloured pixels of the images.

3- In order to put in evidence the sample anisotropy (Δn), the difference
of the pixel values from the same position of the refractive index images
obtained in each optical axis was made.

4- As mentioned, Miao et al. [14] have found a linear relationship between
the refractive index anisotropy (Δn) and OM content. Thus, this relationship
was applied for each refractive index anisotropy pixel value, to obtain the
TOC image per pixel.

Fourier-transform infrared (FTIR) and Raman spectroscopy

FTIR and Raman spectroscopy have been applied to sample A in or-
der to observe whether there are changes either in composition or in OM's
concentration.

Raman spectra was measured by backscattering Raman spectroscopy sys-
tem using an HREvo microspectrometer (Horiba Instrument) coupled to a
confocal microscope. The laser had a wavelength of 355 nm and a maximum
power of 1 mW. The system gave a spectral range of 100-2000 cm⁻¹ with a
resolution of 5 cm⁻¹. The laser spot diameter over the sample was 5 µm.

Infrared spectra were recorded using PerkinElmer's Spotlight 400 FT-IR
microscope. The instrument used a MCT/A detector and KBr separator. It
had a spectral range of 750-4000 cm⁻¹, with a spectral resolution of 4 cm⁻¹
and a spot size over the sample of 100 µm diameter.

In both methods the signals have been processed by peak fitting, as described by the authors in [20], using OriginLab software (OriginLab Corp., Northampton, MA) to perform a more detailed analysis of the absorption peaks linked to the OM. Particularly, in Raman spectroscopy these peaks are in the G (≈ 1575-1598 cm⁻¹) and D (≈ 1330-1351 cm⁻¹) bands [20] and in FTIR the selected peaks are associated to the asymmetric stretching of CH2 (≈ 2925 cm⁻¹) and CH3 (≈ 2955 cm⁻¹) [21].

### X-ray microtomography

The tomograph used to perform the measurements is a Zeiss Xradia Versa 510 (DMEX, Pau, France) and features a combination of optical and geometric magnification. It is equipped with a 10 W X-ray source with a tunable voltage level between 30 keV and 160 keV, and a 16 bit cooled CCD detector capable of acquiring images of 2048 x 2048 pixels. In this study, X-ray radiographies were performed at 40 keV tube voltage and 3 W target power, using an exposure time of 50 s and a pixel size of 5.75 µm in binning 2 with an objective X4. The data sets were visualized and interpreted with Dragonfly software (ORS, Version 2022.2).

## 3. Results

The THz refractive index images obtained after following the steps one and two of the proposed method, as described in section 2, are presented in figure 2. Absorption images are also shown. Although they will not be used to perform the THz-TOC images, they give information linked to the OM
166 content [13]. Specially, figures 2a,b show that the zone of lower refractive index coincides with the zone of higher absorption presented in figure 2d,e.
167

<figure><img src="image_4.png" /><figcaption>Figure 2: (a) THz refractive index image obtained with THz polarization of EM wave parallel to sample' optical fast axis. (b) THz refractive index image obtained with THz polarization of EM wave parallel to samples' optical slow axis. (c) 3D representation of image from (b). (d-f) Idem (a-c) but presenting the absorption results.</figcaption></figure>

168
169

Figure 2c and 2f show the 3D representation of figure 2b and 2e respectively. It is possible to see a gradient behavior over the refractive index and
170 absorption.

<figure><img src="image_5.png" /><figcaption>Figure 3: (a) Refractive index anisotropy image. (b) 3D representation of (a).(c) Absorption anisotropy image and (d) its corresponding 3D representation.</figcaption></figure>

171 When applying step 3 of the method, i.e. making the difference of the
172 pixel values from the same position of the images obtained in each optical axis
173 from figure 2 (2b-2a=Δn and 2d-2e=Δα), we obtained the anisotropy images
174 presented in figures 3a and 3c. Figures 3b and 3d show the 3D representation
175 of the previous images.

176 The spectra obtained by performing FTIR and Raman spectroscopy on
177 sample A are shown in figures 4a and 4d. The corresponding irradiated spots
178 on the TOC THz image are presented in figures 4b and 4e. These are shown
179 by black circles in the images. Although the spectra in figures 4a and 4d
180 have differences in amplitude, all the minerals found are present in all the
181 irradiated points. Thus, the signal changes are due to variations in mineral
182 concentration.

183 Figures 4c and 4f shows the curve fitting and spectral reconstruction for
184 one case in each method over the absorption peaks related to the organic
185 matter.

186 Figures 4d and 4g present contour plots generated with the selected
187 peaks' amplitudes, i.e. G and D for Raman spectroscopy and the asym-
188 metric stretching of CH2 and CH3 for FTIR, obtained after making signal
189 deconvolution. It can be seen that the same trend as that observed in the
190 THz images is not visible. In addition, since the contour plots generated
191 with the FWHM and the area of the signals show no discernible patterns, we
192 have included them in the supplementary material to ease the readability of
193 the manuscript.

194 The radiograph obtained by X-ray tomography, as described in section 2,
195 show in each pixel the information of all the matter traversed through the
196 thickness of the sample, as is also the case in the THz images. Thus, in figure
197 5, a comparison of the results is made.

198 Depending on the attenuation, the radiograph shows clearly four main
199 phases which are segmented and presented on figure 5b.
<figure><img src="image_6.png" /><figcaption>Figure 4: (a) Raman spectra of the irradiated spots located with black circles in the THz TOC-image shown in (b). (c) Signal deconvolution. (d) Contour plots of amplitudes peaks of band G and D after deconvolution. (e-h) Idem a-d but for FTIR results.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 5: (a) THz refractive index image (fast axis) (b) Four-phase attenuation-coloured X-ray radiograph. (c) Histogram of the four phases per line of the image presented in (b).</figcaption></figure>

The phase with higher attenuation (red colour) mainly reflects the presence of iron-rich pyrite, the phase with lower attenuation (light blue colour) reveals a higher content of OM and/or air inside the pores, and/or micro-nanofractures. However, since the samples are organic-rich shales, this is generally associated with the presence of OM [22].

As FTIR-Raman spectroscopy indicates no change in the mineral composition of the samples studied, the differentiation of the orange and grey phases is due to a variation in density.

Thus, we interpret each pixel of the orange phase as mainly representing the average attenuation given by a mixture of the rock matrix and OM. The latter would impact the measurement by giving lower density to the material. On the other hand, we interpret the grey phase as given mostly by the rock
matrix.

Figure 5c presents the number of pixel per line for each phase. From right to left, the figure shows an increase of OM content (light blue and orange color) and a decrease of the more dense rock matrix (grey color). These observations are in agreement with the THz refractive index image presented in fig. 5a. In this context, lower refractive index indicates lower density of the material.

## 4. Discussion

We obtained in figure 2 that the THz images show an area of lower refractive index (lower density), corresponding to an area of higher absorption. We initially associated this with a higher OM content, as mentioned in [13], rather than a mineralogical change. Thanks to the Raman and FTIR spectroscopy measurements, which only indicated a change in mineral concentration, we were able to validate this in our samples. Moreover, this information permitted us to conclude that the phases with lower attenuation in the X-ray radiograph reflected density differences primarily due to changes in OM content, rather than mineralogical variations, which was in agreement with our initial assumptions based on the THz images. This was further confirmed by the observation that the same area exhibited greater anisotropy, as shown in figure 3. This is consistent with the findings reported by Miao et al. [14] and the obtained X-ray radiograph, validating that higher dielectric permittivity anisotropy corresponds to higher organic matter content.

However, since the TOC values of our samples determined by the Rock-Eval method were not within the TOC-$\Delta n$ relationship found in Miao et al.
249

[14], we have added them to the ones they obtained, in order to re-estimate
the linear relationship with more points. This is presented in figure 6. The
values of Δn of our samples are given by the average of all pixels obtained
from the refractive index anisotropy images. Both fittings are presented in
the figure.

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251

252

253

254

The new linear TOC-Δn relationship is applied for each pixel of figure
3a (step 4 of the THz method presented in section 2), to obtain the TOC
image per pixel presented in figure 7a. A 3D projection can be seen in figure
7b and an histogram of the TOC values of the image is presented in figure
7c. By averaging the values of all pixels in the image, we got a total organic
carbon (TOC) value of 4.17 %. Applying the Rock-Eval method, the TOC
obtained was 3.26 %.

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261

To estimate the TOC error of each pixel, propagation of the uncertainty of
the new linear TOC-Δn relationship was performed. The error given by Δn
was estimated by considering twice the absolute error given by the refractive
index estimation. The latter was calculated by taking into account the error
in sample thickness and signal phase [23]. This gave a relative error of about
6 %, which means that the total TOC obtained was 4.17 ± 0.25 %.

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267

In particular, the TOC image reveals an OM distribution similar to that
observed in the X-ray radiograph. However, this distribution is not seen in
the FTIR and Raman spectroscopy results.

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270

In the last case, it may be due to the fact that the system used irradiated
the sample with a laser spot of 5 µm diameter, which is much smaller than the
THz spot (≈ 300 µm diameter). Hence, the signal obtained is not irradiating
the same area/quantity of minerals. Over 5 µm spot diameter it is possible to

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273
<figure><img src="image_8.png" /><figcaption>Figure 6: Relationship between organic matter content and refractive index anisotropy.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 7: (a) TOC-THz image. (b) 3D representation of image from (a). (c) TOC histogram of pixels values from image in (a).</figcaption></figure>
irradiate mainly one mineral, e.g. pyrite, which does not necessarily represent the average OM content over 300 µm spot diameter. On the other hand, in the FTIR system, the laser spot had a diameter of 100 µm, which is closer to the TOC THz image spatial resolution, but it didn't give a similar result either. This could be because FTIR, like Raman spectroscopy, provides information on the surface and not on the OM within the sample thickness, as it is the case with THz imaging and X-ray radiography. Furthermore, it has to be taken into account that the preparation of the samples (cut and polishing) has an impact over the sample's surface by changing and/or affecting the mineralogical, as well as the OM distribution. Another factor to consider in these studies is that the OM in the samples is around 3-4 %. Thus, it may be that both FTIR and Raman spectroscopy are not sensitive enough to estimate changes of less than 1 % from measuring just the surface [24].

It should also be mentioned that the proposed method is valid as long as the main source of dielectric anisotropy is caused by the varying organic matter content of the individual rock strata. As discussed, in Sanjuan et al. [19], there are many sources of anisotropy, e.g. µfractures, fluids, pores. Hence, a combination of these sources of anisotropy could lead to an overestimation of the TOC of the rock.

Specially, this may be one of the reasons why the TOC results obtained by averaging the pixel values of the TOC-THz images is higher (up to 20% more in relative value, considering errors) than that obtained with the Rock-Eval method. It can also be given by the TOC-Δn relationship used for the TOC image estimation, which needs more cases to establish a better cor-
relation that might not be linear. Furthermore, the sample volume studied by Rock-Eval analysis is not the same volume studied in THz imaging. In particular, looking at the histogram presented in figure 7c, it is possible to see areas where the pixels values are actually around 3.26 %. In any case, the similar results obtained from both methods served to validate the accuracy and reliability of our proposed approach. However, to mitigate the possible overestimation of total organic carbon derived from other sources of anisotropy, the complementary support of X-ray microtomography and/or microscopy imaging can provide valuable assistance. It is worth mentioning that the results are reproducible, as we obtained consistent outcomes in different measurement campaigns on two different samples. The results for one of these samples are included in the supplementary material.

Concerning the absorption anisotropy images depicted in figure 3b, they have not been used to estimate the TOC. This is because there is currently no correlation between these parameters. However, as expected, a more pronounced anisotropy can be observed where there is a higher content of OM [14].

Finally, we would like to mention that working with the anisotropy of the sample, and not with the absolute values of the dielectric permittivity to establish the correlation with the content of OM [13], allows the proposed method to be more robust to changes of minerals, e.g. pyrite, as well as porosity that different samples may have as long as they do not affect the sample dielectric permittivity anisotropy in a consistent way.
## 5. Conclusion

The manuscript introduces and validates a novel method for fine-scale imaging of OM content in organic-rich shale samples using THz electromagnetic wave measurements in transmission mode. This innovative approach enables imaging at a new spatial scale, with the capability to produce square-centimetre images with submillimetre pixel resolution, sensitive to nano- and micro-scale OM content.

The method relies on the assumption that dielectric permittivity anisotropy primarily results from variations in OM content within the rock strata. Although the TOC-refractive index anisotropy relationship has been recalculated with additional cases, further data could enhance this correlation. Moreover, absorption anisotropy data should be analysed in conjunction with TOC values from multiple samples to investigate potential correlations.

Finally, we conclude that although the method has the limitations mentioned above, the method provides complementary insights to existing imaging techniques, offering a valuable tool for the detailed analysis of OM in organic-rich shale samples.

## 6. Data Availability

The Terahertz temporal signal measurements used for making the TOC images are in the free online OSF repository: https://osf.io/km2wn/.

## 7. Acknowledgements

This work was possible thanks to the financing given by the “TELLUS CNRS” project. The authors thank F. Bejina from the IRAP Laboratory
(Toulouse, France) for cutting the samples. The authors also thanks Marcela Villar Benvenuto for her advice during this work and TotalEnergies for providing us the samples. Terahertz measurements has been performed in the THz platform PLATERA co-financed by the Auvergne-Rhône Alpes region (France).

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Highlights

Visualizing organic matter content in organic-rich shales through
Terahertz imaging

Federico Sanjuan*, Pascale Sénéchal, Iris Henriquez, Maria Angels Subirana, Charles Aubourg, Bertrand Fasentieux, François Baudin, Maxime Bernier

* Imaging the organic content of organic-rich shales using Terahertz elec-
tromagnetic waves

* Terahertz dielectric permitivity anisotropy of organic-rich shales allows TOC imaging

* New submillimetre scale in organic matter images of organic-rich shales via Terahertz spectroscopy
Graphical Abstract

Visualizing organic matter content in organic-rich shales through
Terahertz imaging

Federico Sanjuan*, Pascale Sénéchal, Iris Henriquez, Maria Angels Subirana, Charles Aubourg, Bertrand Fasentieux, François Baudin, Maxime Bernier

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