# Toward characterization of organic matter rich in aromatic compounds by spectral-induced polarization: preliminary investigation and perspectives

Frédéric Delarue, Aida Mendieta, Alexis Maineult, Samuel Abiven, Haoliang Luo, Marco D Vásconez-Maza, Cécile Finco, Damien Jougnot, Julien Thiesson

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## To cite this version:

Frédéric Delarue, Aida Mendieta, Alexis Maineult, Samuel Abiven, Haoliang Luo, et al., Toward characterization of organic matter rich in aromatic compounds by spectral-induced polarization: preliminary investigation and perspectives. Geophysical Journal International, 2025, 241 (3), pp.2028-2033. 10.1093/gji/ggaf145. hal-05206839

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Submitted on 11 Aug 2025

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# Toward characterization of organic matter rich in aromatic compounds by spectral-induced polarization: preliminary investigation and perspectives

Frédéric Delarue<sup>1,†</sup> Aida Mendieta,<sup>1,2,†</sup> Alexis Maineult<sup>1,3</sup> Samuel Abiven,<sup>3</sup> Haoliang Luo,<sup>1</sup> Marco D. Vásconez-Maza,<sup>1,4,5</sup> Cécile Finco,<sup>6,7</sup> Damien Jougnot<sup>1</sup> and Julien Thiesson<sup>1</sup>

Accepted 2025 April 9. Received 2025 March 27; in original form 2025 January 30

## SUMMARY

Spectral induced polarization (SIP) has been suggested as a non-invasive and cost-effective tool to detect and monitor aromatic rich organic matter such as biochar. In our study, we show that SIP can track biochar concentration up to 10 per cent (wt.) in a soil with a clay content of 20 per cent. Assessment of changes in the concentration of biochar was conducted according to double Pelton parameters and the maximal phase determined at 11.7 Hz, a frequency at which a polarization peak is observed in the presence of biochar. All SIP-derived parameters were correlated with the biochar content, with the exception of the relaxation time of the polarization peak occurring at 11.7 Hz, which was related to soil water saturation in previous investigations. Among studied parameters, the phase value that we measured at 11.7 Hz may therefore consist in a simple and reliable methodology to evaluate the biochar content on SIP in our experiment. Several steps are still necessary before a widespread field application notably by considering how modifications in the chemistry of biochar with time can interact with biochar concentration and water saturation to modify polarization processes shaping SIP curves. Beyond the scope of tracking changes in the content of highly aromatic OM—such as biochar—in soils, this study suggests that the degree of aromaticity of OM can play a key role in the SIP response paving the way for wider use of SIP in soil science.

**Key words:** Electrical properties; Induced polarization; Polarization analysis.

## 1. INTRODUCTION

Aromatics are key compounds of soil organic matter. They are organic molecules containing one or several aromatic six carbon rings including three double bonds. Solid by-product of plant biomass pyrolysis, biochar is a highly aromatic material which can persist in soils and sediments from centuries to millennia owing to its chemical structure limiting biological and abiotic degradation (Kuzyakov et al. 2009). In addition to carbon sequestration, adding biochar to soils can lead to cascading consequences on soil quality notably

by increasing water holding capacity, increasing pH or reducing nutrient leaching (Jeffery et al. 2011). Although some adverse effects were also observed on soil functioning (depending on soil type/texture, biochar feedstocks; Brtnicky et al. 2021), biochar has attracted an international interest as a potential tool to mitigate climate change while enhancing crop productivity (Lehmann, Gaunt & Rondon 2006; Woolf et al. 2010). However, the effects of biochar on soil functions are mostly studied in laboratory experiments and at the short-term scale. With time, beneficial effects of adding biochar on soil functions remain an open question as biochar ageing implies modifications of its chemical properties and of its physical characteristics following fragmentation as well as changes in its concentration via lateral movement or downward migration in the soil

<footnote n="1"><sup>1</sup>Sorbonne Université, CNRS, EPHE, UMR 7619 METIS, F-75005 Paris, France. E-mail: Frederic.delarue@sorbonne-universite.fr</footnote>

<footnote n="2"><sup>2</sup>Institut de Physique du Globe de Paris, CNRS, Université de Paris Cité, 1 Rue Jussieu, F-75005 Paris, France</footnote>

<footnote n="3"><sup>3</sup>Laboratoire de Géologie, École Normale Supérieure, CNRS, PSL University, IPSL, Paris, France</footnote>

<footnote n="4"><sup>4</sup>Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena, Paseo Alfonso XIII, 52, E-30203 Cartagena, Murcia, Spain</footnote>

<footnote n="5"><sup>5</sup>British Geological Survey, Keyworth, Nottinghamshire NG125GG, UK</footnote>

<footnote n="6"><sup>6</sup>Université Rouen Normandie, Université Caen Normandie, CNRS, M2C, UMR 6143, F-76000 Rouen, France</footnote>

<footnote n="7"><sup>7</sup>Cerema, ENDSUM, F-76120 Le Grand-Quevilly, France</footnote>

<footnote>†These authors contributed equally to this paper</footnote>
(Zhang et al. 2010). To date, modifications of the biochar content remain poorly documented in the field although it is a pre-requisite to characterize their spatial and temporal dynamics and assess the effect of biochar at the long-term scale on soil functions (Singh et al. 2015).

Biochar content can be assessed by numerous approaches including optical techniques such as particles counting, chemical oxidation including the so-called Benzene Polycarboxylic Acids method, thermal oxidation, spectroscopic techniques in particular quantification by mid-infrared or solid-state ¹³C nuclear magnetic resonance (Brodowski et al. 2005; Paetsch et al. 2017; Lesven et al. 2022; Aubertin et al. 2024; Xie et al. 2024). Application of these time-consuming methods requires to destroy samples before or during the analyses. Therefore, these methods are not suitable to provide a continuous monitoring of the spatial and temporal dynamics of biochar in soil. Recently, spectral induced polarization (SIP) has been suggested as a potential effective tool to detect and monitor biochar in soil (e.g. Gao et al. 2017, 2019) through the measurements of the complex electrical conductivity spectra (from 10⁻³ to 10⁴ Hz). If these studies have been conducted in laboratory conditions, it is worth noting the SIP method can also be used on the field paving the way for its future application to non-intrusively monitor the spatial and temporal dynamics of biochar in soil directly in situ. However, several intermediate steps are still required before a field application notably to test the ability of SIP to effectively quantify a wider range of biochar content application in a mineral soil. In this study, we therefore aim to test the potential of SIP to quantify a wide range of biochar content (from 0 to 10 per cent) in a mineral soil. Assessment of changes in the concentration of biochar was performed according to double Pelton parameters and the value of the phase measured at 11.7 Hz.

# 2. MATERIALS AND METHODS

## 2.1. Sample preparation

The soil was sampled in a test site located at the Cerema (Centre for Studies and Expertise on Risks, the Environment, Mobility and Urban Planning, Rouen, France). This soil contains ca. 20 ± 6 per cent clay, 70 ± 6 per cent silt and 10 ± 4 per cent sand as determined by granulometry. A cationic exchange capacity (CEC) of ca. 3.1 ± 0.4 g/100 g eq was determined by blue methylene test. Studied soil was dried at 40 °C for 10 d. Afterwards, soil and biochar (Spruce-derived biochar; carbonized at ca. 600 °C; 74.1 per cent C, 0.7 per cent N and 3.0 per cent H) were crushed separately and sieved at 2 mm. The < 2 mm fraction of soil and biochar were then manually mixed to obtain mass concentration of biochar of 0 per cent, 0.1 per cent, 1 per cent, 5 per cent and 10 per cent (three replicates for each concentration). As samples were not flushed leaving their original ions in the soil, biochar-soil mixture was then mixed with de-ionized water (gravimetric water content = 27.5 per cent) to obtain a good plasticity to fill the sample holder (see Mendieta et al. 2021, for the experimental details on the sample holder). The wet samples were then placed in a sample holder for a period of 24 hr for equilibration.

## 2.2. Spectral induced polarization

The details of the procedure for SIP measurements with the SIP-FUCHS III (Radic Research, www.radic-research.de) are described in Mendieta et al. (2021). A sinusoidal current is injected between

a pair of stainless steel current electrodes and the resulting potential difference is measured between two non-polarizable electrodes (home-made Cu-CuSO₄ electrodes). Before starting the measurements over the full frequency range (1 mHz to 20 kHz), short tests are performed to adapt the imposed input electric potential difference (1 Hz to 20 kHz). A very low potential difference yields poor quality data (low signal-to-noise ratio), and a very high potential difference could be out of the validity of Ohm's law (linearity). In this case, we chose a range of injected electrical current from 0.05 to 0.1 mA for the whole data set, yielding measurement errors below 1 per cent.

High frequency diverging trend was removed following the correction proposed by Florsch, Revil & Camerlynck (2014). An optimization procedure was conducted (after Maineult 2016) to determine the Pelton parameters including the direct current resistivity ρ₀ (in Ω m, or its inverse σ₀ in S m⁻¹), the chargeability m (V/V), the Cole-Cole exponent c (no units) and the relaxation time τ (in s). ρ₀ informs about the resistance of a material to an electrical current at the low frequency end, m about the capacity of a material to store electrical charges reversely, τ determines the characteristic time of this charge storage at which the chargeability of the material is maximum electrically discharges itself and c is a constant that informs about the distribution of relaxation times of the material (for more information see for instance Tarasov & Titov 2013). In this work, a double Pelton model (i.e. two polarization peaks) was used as one peak describing the polarization of biochar at lower frequencies and one describing the high frequency behaviour were observed. The eq. (1) that describes the double-Pelton model is:

$$ \rho^*(\omega) = \rho_0 \left[ 1 - m_1 \left( 1 - \frac{1}{1 + (i\omega\tau_1)^{c_1}} \right) - m_2 \left( 1 - \frac{1}{1 + (i\omega\tau_2)^{c_2}} \right) \right], \quad (1) $$

where ρ*(ω) is the complex and frequency-dependent electrical resistivity (in Ω m). Note that the subscripts 1 and 2 correspond to the Pelton model for the low and high frequency polarizations, respectively. Finally, for the high-frequency trend corrected and uncorrected SIP spectra, we determined the maximal phase φₚₘₐₗ (mrad) at 11.7 Hz as a simple descriptor of the polarization peak of biochar. The complex conductivity was expressed following the real σ' (S m⁻¹) and imaginary components σ'' (S m⁻¹).

# 3. RESULTS AND DISCUSSION

Up until now, the SIP signature of biochars has been determined in sand, that is, a simple and clean synthetic material (Gao et al. 2017, 2019). However, natural soils present a complex texture, including sand but also silt and clay (i.e. enhancing the CEC). While the SIP response of sand can be considered as negligible, clay (and sometimes silt) present a significant SIP signature (Mendieta et al. 2021). Indeed, when a mineral is in contact with an electrolyte, an electrical charge builds up in its surface, creating an electrical double layer (EDL). In the case of clay minerals, their very specific surface area yield a relatively important EDL (i.e. interface properties start to play a major role). When subjecting a sample where the EDL is significantly present (e.g. typically a natural soil) to an external electrical field, the EDL polarizes (see Revil et al. 2012). This so-called electrochemical polarization implies a SIP phase 'peak' typically around 0.1 to 10 Hz (Fig. 1; see Leroy & Revil 2009). Such polarization peak associated to biochar has also been observed in Gao et al. (2017; 2019).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Average (a) uncorrected amplitude of the resistivity, (b) uncorrected phase (ratio between $\sigma''$ and $\sigma'$), (c) corrected amplitude of the resistivity, (d) corrected phase, (e) real and (f) imaginary conductivity components from the soil-biochar mixtures ($n = 3$).</figcaption></figure>

In our study, the high level of correlation between most of double-Pelton model-derived parameters and the biochar content suggests that the presence of clay (20 per cent) in the studied soil did not inhibit the possibility to track changes in biochar concentration with SIP. Among all studied parameters, the amplitude of the peak phase $\varphi_{\text{peak}}$ (determined in high-frequency uncorrected and corrected spectra to be at 11.7 Hz) constitutes the best descriptor of the biochar content ($p$-value = 0.98; Table 2). First, this results evidence that high-frequency corrections do not have any effect on the determination of $\varphi_{\text{peak}}$. Hence, simple processing of SIP spectra can already be conducted in the lab to track changes in biochar concentration by SIP non-specialist, a pre-requisite to open its use to communities dealing with soil science. Secondly, significant differences in the $\varphi_{\text{peak}}$ as a function of biochar content are well observed above 1 per cent (Table 1). This suggests that modification in the biochar content can be assessed by SIP from 1 to 10 per cent, an usual range for biochar application in soils (Schmidt et al. 2021).

However, it is worth mentioning that the low frequency relaxation time $\tau_1$ constitutes the sole Pelton parameter, which was not correlated with the biochar content (Table 2). In saturated conditions, various authors discussed the relationship between particle or inclusion sizes and $\tau_1$ (Tartrat et al. 2019); however, this point has not been investigated in this study. Also, as the water content was kept constant throughout the experiment, this result is complementary to those from Gao et al. (2019), who previously showed that the relaxation time of the biochar polarization was as a function of the water saturation in sand/biochar mixtures. This implies that the shift in frequency can be used in conjunction with $\varphi_{11.7}$ to disentangle the effects of water saturation and of the biochar content on SIP signatures, respectively.

Organic matter (OM) has been shown several times to act as a key controlling factor of SIP properties (see works of Schwartz & Furman 2014; Gao et al. 2017; Gao et al. 2019; Strobel et al. 2023a). In most cases, CEC has been identified as a main controlling factor of SIP properties (see McLachlan et al. 2024, for a discussion on the role of CEC on IP signals). However, additional controlling factor can play a key role in SIP applied to biochar. Indeed, Gao et al. (2017) suggested that the more the biochar is formed at a high temperature, the more the electronic polarization of biochar particles is

(possibly generating a polarization similar to disseminated metallic particles, e.g. Revil, Florsch & Mao 2015). Such an evolution can only be explained by the rise in the degree of aromaticity occurring with carbonization leading to the loss of functional groups (e.g. CH$_2$, CH$_3$, CO, ...) and heteroatoms (atoms such as O and N except C and H) and the initiation of the stacking of polyaromatic clusters according condensation (Delarue et al. 2016). These physical (condensation) and chemical (aromatization) modifications act together to increase electronic conductance (Bartoli et al. 2022). This increase in electronic conductivity can also be seen clearly in the real part of the electrical conductivity, which behaves like metallic particles: that is, an increase of biochar content and an increase of metallic particles reflects similarly on the real part of the electrical conductivity (e.g. Revil, Florsch & Mao 2015). By comparing $\sigma'''$ and total organic carbon (TOC) values determined at ca. 1 Hz in samples enriched in peat (Strobel et al. 2023b) and in biochar (Gao et al. 2017; 2019 and our study); we indirectly test the effect of aromaticity on SIP signatures (Fig. 2). According to Fig. 2, two end-members of OM composition are characterized: (i) peat composed of small aromatic ring clusters and of aliphatic moieties and (ii) biochar composed of polyaromatic ring clusters and depleted in functional groups. These two end members are classically represented in Van Krevelen diagram used to evaluate the degree of OM microbial and thermal degradation through the progressive reduction of the H/C and O/C atomic ratios observed with an increased aromaticity. First, in all studies, TOC is significantly correlated to $\sigma'''$. This may be possibly explained by enhanced CEC with enhanced OM content. However, several studies suggest that increasing the amount of OM (and of CEC) does not systematically lead to a rise in polarization (Ponziani et al. 2012; Schwartz & Furman 2014). Secondly, although systematically correlated in each type of OM, the linear relationships between $\sigma'''$ and TOC are distinguished by their slope (Fig. 2). This slope is higher when TOC is higher too. As increased aromaticity leads to an increased TOC for a pure organic material (reduction of O and H content and consequently, a relative rise in C), this suggests that the degree of aromaticity may play a key role in the response of SIP to OM. Nonetheless, such a conclusion remains to be consolidated by additional experiments including different types of organic matter
<table><caption>**Table 1.** Mean and standard deviation values (mean value ± standard deviation; *n* = 3) for ρ₀, m₁, c₁, τ₁ and -φₚₖₐₗ determined in soil enriched in biochar from 0 to 10 per cent. Different letters denote significant differences at *P*-value ≤ 0.05 (i.e. ‘a’ is significantly different from ‘b’ but not of ‘ab’).</caption><thead><tr><th>Biochar content (per cent)</th><th>ρ<sub>0</sub> (ohm m)</th><th>m<sub>1</sub> (mV/V)</th><th>c<sub>1</sub></th><th>τ<sub>1</sub> (s)</th><th>- φ<sub>peak</sub> (mrad)</th></tr></thead><tbody><tr><td>0</td><td>70.2 ± 1.7a</td><td>0.042 ± 0.006a</td><td>0.752 ± 0.037a</td><td>2.4 ± 0.2a</td><td>6.5 ± 1.7a</td></tr><tr><td>0.1</td><td>69.2 ± 1.9a</td><td>0.043 ± 0.003a</td><td>0.747 ± 0.007ab</td><td>2.5 ± 0.2a</td><td>8 ± 0.9a</td></tr><tr><td>1</td><td>64.4 ± 5.8a</td><td>0.055 ± 0.013ab</td><td>0.681 ± 0.046ab</td><td>2.1 ± 0.1a</td><td>9.4 ± 0.7a</td></tr><tr><td>5</td><td>46.8 ± 1.2b</td><td>0.065 ± 0.013ab</td><td>0.618 ± 0.057b</td><td>2.6 ± 0.4a</td><td>15.5 ± 0.8b</td></tr><tr><td>10</td><td>43.6 ± 3.2b</td><td>0.075 ± 0.010b</td><td>0.583 ± 0.046b</td><td>2.7 ± 0.3a</td><td>22.3 ± 0.5c</td></tr></tbody></table>

<table><caption>**Table 2.** Pearson correlation coefficient (*r*) evaluating the relationship between SIP-derived parameters and biochar content. Significant correlation (*P*-value < 0.05) is indicated by the use of bold numbers.</caption><thead><tr><th>Variables</th><th>Biochar content (per cent)</th><th>ρ<sub>0</sub> (ohm m)</th><th>m<sub>1</sub> (mV/V)</th><th>c<sub>1</sub></th><th>τ<sub>1</sub> (s)</th><th>- φ<sub>peak</sub> (mrad)</th></tr></thead><tbody><tr><td>Biochar content</td><td>-</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ρ<sub>0</sub></td><td><strong>-0.919</strong></td><td>-</td><td></td><td></td><td></td><td></td></tr><tr><td>m<sub>1</sub></td><td><strong>0.809</strong></td><td><strong>-0.768</strong></td><td>-</td><td></td><td></td><td></td></tr><tr><td>c<sub>1</sub></td><td><strong>-0.829</strong></td><td><strong>0.835</strong></td><td><strong>-0.987</strong></td><td>-</td><td></td><td></td></tr><tr><td>τ<sub>1</sub></td><td>0.502</td><td>-0.463</td><td>0.109</td><td>-0.117</td><td></td><td></td></tr><tr><td>- φ<sub>peak</sub></td><td><strong>0.985</strong></td><td><strong>-0.917</strong></td><td><strong>0.852</strong></td><td><strong>-0.872</strong></td><td>0.473</td><td>-</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Data from this study and from the literature illustrating the relationship (all relationships are significant; *P* < 0.05) between σ'' (determined at 1 Hz) and TOC (< 10 per cent).</figcaption></figure>

(testing for instance, a correlation between H/C ratio and the slope between σ'' and TOC) and by avoiding differences related to the procedure used to measure SIP in the literature (e.g. electrolyte versus deionized water can have different effects on σ' for instance).

Although promising to track changes in the biochar content of soil, it is necessary to emphasize that all investigations were conducted on fresh biochar. Complementary investigations are still required to constrain the significance and evolution of SIP response as a function of their production condition (e.g. effects of feedstock and carbonization temperature) and of their modifications once incorporated into soils. Indeed, in soils, biochar is subjected to ageing implying fragmentation as well as chemical alteration through oxidation (Wiedner *et al.* 2015; Wang *et al.* 2020). Fragmentation can cause an increase in the frequency at which the biochar polarization

peak occurs (Gao *et al.* 2017). Implying a relative enrichment in carboxylic and phenolic groups at the surface of biochar (Wiedner *et al.* 2015), oxidation also affects surface conduction. With the rise in O-functional groups, it is expected that oxidation lead to a reduction in biochar conductivity with time (Kane *et al.* 2021). As oxidation also implies a relative reduction of the aromaticity of biochar, a decrease in σ'' can be expected with biochar ageing as suggested by Fig. 2. All these hypotheses highlight that biochar ageing is likely modifying the initial SIP spectra of fresh biochar. Provided an interdisciplinary research involving geophysics and geochemistry, our study suggests that SIP has the potential to provide a new and integrative proxy of the physical and chemical state of biochar in soils, the cornerstone to assess the long-term effect of biochar on soil functioning.
## 4. CONCLUSION

In this study, we evidence that SIP can be used to track biochar concentration up to 10 per cent owing to the occurrence of a polarization peak at low frequencies. Interestingly, our results indicate that the maximal phase at low frequency (here, 11.7 Hz) was the best descriptor of the biochar content compared to other widely used SIP-derived parameters. These experimental findings are then compared to other previously published results showing that clear relationships exist between the imaginary conductivity and the Total Organic Carbon. Moreover, these linear relationship seems to be strongly related to the aromaticity of the organic matter. Therefore, we believe that this rather exploratory experiment is paving the way to a wider use of SIP in soil science. However, before establishing a field SIP application to track the fate of biochar or OM in general, the next steps will be to conduct several additional and multiparametric investigations such as the effect of biochar ageing as well as interaction between soil water content and biochar itself on SIP spectra.

## ACKNOWLEDGMENTS

The author(s) acknowledge(s) the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-23-CE22-0013 (project OPTISOIL). The authors thank Fayçal Rejiba for providing CEC and granulometry results of the soil used in this study. MV thanks the support from the Margarita Salas (Universidad Politécnica de Cartagena) postdoctoral grants.

## CONFLICT OF 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.

## DATA AVAILABILITY

The data will be made available after acceptance of the manuscript in the zenodo repository with https://doi.org/10.5281/zenodo.8090792.

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