**3. Results and discussion**

For method development, studies on derivatization of some hydrazines with MPA had been carried out in acidic solution [15]. It was found that DNPH is a suitable reagent. Many internal standards having similar core structure which is derivatized with DNPH were also investigated by the same manner [19]. Prednisolone was found to be the most suitable ketosteroid and its derivatized solid product was prepared. It was eluted along with the MPA-DNPH and was used as I.S. throughout the experiment.

Effects of derivatization time and concentration of DNPH for MPA analysis were studied. The DNPH concentrations used were 12.5, 25, 75 and 100 mg/mL. MPA (100 ng/mL) was used to react with various concentrations of DNPH in the ratio of 1 : 1 at room temperature and their reaction times were also done up to 180 min (Fig. 3). It was found that 75 mg/mL of DNPH gave the highest peak area within 120 min derivatization time. In this study, the derivatization time of 30 min wasenough, since the signal output was not significantly different from that of 120 min derivatization.

Application of HPLC Analysis of Medroxyprogesterone Acetate in Human Plasma 223

the results for intra-day and inter-day precision were 5.05 and 11.5 %RSD, respectively. The intra-day accuracy ranged from 102.2 - 109.5%. The average recovery was found to be 102.9 + 4.4%. It was found that this electrochemical detection was considerably selective and gave a working range of 1.0 - 10.0 ng/mL with linear regression: y = (3.17 + 0.12) x – (0.67 + 0.03), r2

**Figure 4.** Effect of temperatures on the derivatization reaction of DNPH (75 mg/mL) and MPA (100

**Figure 5.** Effect of applied voltammetric potentials on the peak areas of MPA- and P-DNPH derivatives.

= 0.9985 for *n* = 5 with %RSD = 2.34 – 7.96.

ng/mL) between 5 min and 30 min.

**Figure 3.** Effects of reaction times and concentrations on the derivatization of DNPH with 100 ng/mL MPA at room temperature.

The effect of temperature for 30 min derivatization was also investigated using 100 ng/mL MPA and 75 mg/mL DNPH in acidic solution. There seemed to be no effect of temperature, except a linear increase of peak area when the reaction was performed at room temperature (Fig. 4). Thus, the reaction was rapidly completed at room temperature.

Concerning on RP-HPLC-AD analysis, the optimization conditions of the HPLC-AD for MPA-DNPH analysis were then investigated. Effects of pH (3-6) and buffer concentration (10-40 mM) on mobile phase (ACN : phosphate buffer) were carried out. It was found that the optimum buffering system was at pH 3.0 and 30 mM phosphate buffer (data not shown). In addition, the ratios of organic solvents were investigated and a good separation was obtained with the mobile phase of ACN : MeOH : 30 mM phosphate buffer (pH 3.0) at the ratio of 39 : 39 : 22 by volume.

Figure 5 shows the hydrodynamic voltammogram (HDV) of MPA- and P-DNPH derivatives. The optimum potential that gave the highest peak area was around 0.8-0.9 V for both derivatized products, and 0.85 V was used in this study.

For method validation, the method limits for detection and quantitation were found to be 0.2 and 1.0 ng/mL with lower 15% precision and 80 - 120% accuracy, respectively. In Table 1, the results for intra-day and inter-day precision were 5.05 and 11.5 %RSD, respectively. The intra-day accuracy ranged from 102.2 - 109.5%. The average recovery was found to be 102.9 + 4.4%. It was found that this electrochemical detection was considerably selective and gave a working range of 1.0 - 10.0 ng/mL with linear regression: y = (3.17 + 0.12) x – (0.67 + 0.03), r2 = 0.9985 for *n* = 5 with %RSD = 2.34 – 7.96.

222 Chromatography – The Most Versatile Method of Chemical Analysis

different from that of 120 min derivatization.

MPA at room temperature.

ratio of 39 : 39 : 22 by volume.

Effects of derivatization time and concentration of DNPH for MPA analysis were studied. The DNPH concentrations used were 12.5, 25, 75 and 100 mg/mL. MPA (100 ng/mL) was used to react with various concentrations of DNPH in the ratio of 1 : 1 at room temperature and their reaction times were also done up to 180 min (Fig. 3). It was found that 75 mg/mL of DNPH gave the highest peak area within 120 min derivatization time. In this study, the derivatization time of 30 min wasenough, since the signal output was not significantly

**Figure 3.** Effects of reaction times and concentrations on the derivatization of DNPH with 100 ng/mL

The effect of temperature for 30 min derivatization was also investigated using 100 ng/mL MPA and 75 mg/mL DNPH in acidic solution. There seemed to be no effect of temperature, except a linear increase of peak area when the reaction was performed at room temperature

Concerning on RP-HPLC-AD analysis, the optimization conditions of the HPLC-AD for MPA-DNPH analysis were then investigated. Effects of pH (3-6) and buffer concentration (10-40 mM) on mobile phase (ACN : phosphate buffer) were carried out. It was found that the optimum buffering system was at pH 3.0 and 30 mM phosphate buffer (data not shown). In addition, the ratios of organic solvents were investigated and a good separation was obtained with the mobile phase of ACN : MeOH : 30 mM phosphate buffer (pH 3.0) at the

Figure 5 shows the hydrodynamic voltammogram (HDV) of MPA- and P-DNPH derivatives. The optimum potential that gave the highest peak area was around 0.8-0.9 V for

For method validation, the method limits for detection and quantitation were found to be 0.2 and 1.0 ng/mL with lower 15% precision and 80 - 120% accuracy, respectively. In Table 1,

(Fig. 4). Thus, the reaction was rapidly completed at room temperature.

both derivatized products, and 0.85 V was used in this study.

**Figure 4.** Effect of temperatures on the derivatization reaction of DNPH (75 mg/mL) and MPA (100 ng/mL) between 5 min and 30 min.

**Figure 5.** Effect of applied voltammetric potentials on the peak areas of MPA- and P-DNPH derivatives.

Application of HPLC Analysis of Medroxyprogesterone Acetate in Human Plasma 225

**Table 1.** Intra-day and inter-day precision and method recovery of MPA-DNPH derivative. a*n* = 5

*bwithin three consecutive days cMean of three determinations RSD, relative standard deviation*

Since the method sensitivity of MPA analysis was the main objective of this study, it has been shown that the clean-up step, using the SPE method for MPA spiked in human plasma sample, was necessary probably due to both the reagent used and the sample interference matrices (Fig. 6, a-d). The solution of MPA-DNPH derivative products with excess DNPH was not clean enough, so it must be added through the Sep-pak cartridge before being separated on the HPLC-AD system. Without the clean-up step, there might be trouble for the HPLC column used, which may affect the analytical sensitivity as well. This is the first report of the use of DNPH as an electroactive labeling reagent for MPA analysis. Development and validation of the HPLC-AD of the derivatized product were then carried out. The MPA-DNPH was completely separated from the I.S. within a suitable analysis time (Fig. 6, a-d).

Intra-day RSD (%)

**Table 1.** Intra-day and inter-day precision and method recovery of MPA-DNPH derivative.

Inter-dayb RSD (%)

1.0 7.76 15.4 107.3 4.0 5.06 9.62 98.55 8.0 2.35 9.55 102.9 Mean + SD 5.05 + 2.7 11.5 + 3.4 102.9 + 4.4

Since the method sensitivity of MPA analysis was the main objective of this study, it has been shown that the clean-up step, using the SPE method for MPA spiked in human plasma sample, was necessary probably due to both the reagent used and the sample interference matrices (Fig. 6, a-d). The solution of MPA-DNPH derivative products with excess DNPH was not clean enough, so it must be added through the Sep-pak cartridge before being separated on the HPLC-AD system. Without the clean-up step, there might be trouble for the HPLC column used, which may affect the analytical sensitivity as well. This is the first report of the use of DNPH as an electroactive labeling reagent for MPA analysis. Development and validation of the HPLC-AD of the derivatized product were then carried out. The MPA-DNPH was completely separated from the I.S. within a suitable analysis time

Recoveryc (%)

Concentrationa (ng/mL)

*bwithin three consecutive days cMean of three determinations RSD, relative standard deviation*

a*n* = 5

(Fig. 6, a-d).

Application of HPLC Analysis of Medroxyprogesterone Acetate in Human Plasma 227

Table 2. It is, however, satisfactory for pharmacokinetic study of trace MPA in plasma sample, especially in the case of contraceptive administration with very low dose. Since it is rapid procedure and inexpensive, this technique is suitable for routine analysis of the MPA

A specific and sensitive method is presented for the analysis of medroxyprogesterone acetate (MPA) in human plasma using reversed phase high-performance liquid chromatography (HPLC) with amperometric detection. The blood sample spiked with trace amount of MPA was cleaned up to remove natural interfering matrices by solid-phase extraction (SPE). The MPA extract was then derivatized with 2,4-dinitrophenylhydrazine (DNPH) as an electroactive agent. The MPA-DNPH derivative was re-extracted using SPE prior to analysis by reversed phase HPLC. Quantitative analysis of the MPA-DNPH using prednisolone-DNPH as an internal standard were optimized on a Hypersil ODS column using acetonitrile : methanol : 30 mM phosphate buffer, pH 3.0 (39 : 39 : 22, v/v/v) as mobile phase at a flow-rate of 1.0 mL/min. It was found that the method was selective and gave linear calibration curve for a concentration range of 1.0 - 10.0 ng/mL for 2 mL spiked plasma samples. The relative standard deviation (RSD) of inter-day precision for a period of three validation days was 11.5 + 3.4% for all concentration used. The RSD of intra-day precision (n = 5) was 5.05 + 2.7% with accuracy (n = 5) of 102.3 + 7.4%. The average recovery was found to be 102.9 + 4.4%. The correlation coefficient of the calibration curve was 0.9985. The limits of detection and quantitation were found to be 0.2 and 1.0 ng/mL, respectively. Using DNPH as a derivatizing agent can enhance both selectivity and sensitivity of MPA in plasma and is

content in most pharmaceutical products and blood samples.

**4. Conclusion**

suitable for routine analysis.

ACN: Acetonitrile

HCl: Hydrochloric acid

 chemiluminescence I.S.: Internal standard

LOD: Limit of detection LOQ: Limit of quantitation

MeOH: methanol

Depot-MPA: Depot-medroxyprogesterone acetate

GC-ECD: Gas chromatography-electron capture detection GC-MS: Gas chromatography-mass spectrometry

HPLC-UV: High-performance liquid chromatography with ultraviolet detection HPLC-PO-Cl: High-performance liquid chromatography with peroxyoxalate

LC-MS/MS: Liquid chromatography-mass spectrometry/mass spectrometry

DNPH: 2,4-Dinitrophenylhydrazine

HDV: Hydrodynamic voltammogram

**5. Abbreviations**

**Figure 6.** Chromatogram obtained from (a) blank reagent. (b) standard solution of (1) MPA-DNPH (10 ng/mL) and (2) P-DNPH as I.S. (c) blank of blood plasma sample, (d) blood plasma spiked with (1) MPA-DNPH (10 ng/mL) and (2) P-DNPH as I.S. (inset, (1) MPA-DNPH, 1 ng/mL).

**Table 2.** Some analytical methods and limits of detection of MPA analysis in plasma samples.

*aelectron capture detection of MPA derivative*

*bmass spectrometric detection of MPA derivative*

*ctandem mass spectrometry*

*dultraviolet (254 nm) detection of MPA*

*e peroxyoxalate chemiluminescence (*λ*ex/*λ*em : 480/570 nm) detection of MPA derivative*

*f voltammetric (0.85 V) detection of MPA-DNPH derivative*

*LOD, limit of detection*

The plasma sample size used in this study was still rather high (2 mL) for clinical aspects when compared with other reports [11,13,14], giving LOD and LOQ of 0.2 and 1.0 ng/mL, respectively. Thus, the developed method with electrochemical detection has sufficiently high sensitivity when compared with other methods, except for the tandem MS, as shown in Table 2. It is, however, satisfactory for pharmacokinetic study of trace MPA in plasma sample, especially in the case of contraceptive administration with very low dose. Since it is rapid procedure and inexpensive, this technique is suitable for routine analysis of the MPA content in most pharmaceutical products and blood samples.


**Author details**

Hossein Behnia1\*, Azita Tehranchi2 and Farnaz Younessian3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Dentofacial Deformity Research Center, Research Institute of Dental Sciences, Department of Oral and Maxillofacial Surgery, School of Dentistry, Shahid Beheshti University of Medi‐ cal Sciences, Tehran, Iran

2 Dental Research Center, Research Institute of Dental Sciences, Department of Orthodon‐ tics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 Dentofacial Deformity Research Center, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran


**1. Introduction**

Countries of the Middle East and North Africa (MENA Region) face major challenges in terms of food supply and food security. High dependency on food imports combined with ongoing conflicts and reoccurring climate shocks make the region particularly vulnerable to food crises. Among the 10 countries with the most people affected by food insecurity (IPC/CH Phase 3 or above), three are located in the Middle East including Yemen, Afghanistan, and Syria [1]. In Syria, approximately 9.3 million people were food insecure and needed food, humanitarian, and livelihood assistance between October and December 2020 [1].


**1. Introduction**

[102] Schaefer GB, Mendelsohn NJ. Genetics evaluation for the etiologic diagnosis of autism spectrum disorders. Genetics in medicine : official journal of the American College of Medical Genetics. 2008 Jan;10(1):4-12. PubMed PMID: 18197051. Epub 2008/01/17. eng.

[103] Roesser J. Diagnostic yield of genetic testing in children diagnosed with autism spec‐ trum disorders at a regional referral center. Clinical pediatrics. 2011 Sep;50(9):834-43.

PubMed PMID: 21525079. Epub 2011/04/29. eng.

320 Recent Advances in Autism Spectrum Disorders - Volume I

Autism is a neurodevelopmental disorder characterized by impaired social interaction, and verbal and nonverbal communication as well as limited and repetitive behaviours. Although symptomatology of autism may be noticed around early months, diagnosis generally occurs around 24-36 months, however in some cases diagnosis may be delayed to adulthood [1]. Since behavioural symptoms and the degree of functional impairment are variable, the au‐ tistic disorder is described as a heterogenous symptom cluster of varying etiological and pathological basis [2]. Described as a multifactorial disorder created by interaction of neuro‐ logic, immunologic, environmental, and genetic factors, autistic disorder has no definite cause [3, 4]. In many cases in whom the etiology remains unclear are diagnosed as idiopath‐ ic autism or non-syndromic autism [5, 6]. Seventy percent of cases with idiopathic autism have basic symptoms without physical abnormalities whereas 30% have complex autism in which dysmorphic features are detected such as microcephaly and/or structural brain mal‐ formations [7]. Autism is associated with other syndromes such as Fragile X syndrome, Down Syndrome, and tuberosclerosis in 5-25% of the cases ([8, 9]. Although phenotypic het‐ erogeneity is the biggest challenge for research efforts directed to identify autism etiology [10], currently it is widely accepted that environmental and genetic factors play essential role in genesis of autistic disorder thanks to a recent advance in research techniques related to biological factors and widespread studies in this field [11, 12].


*Article* **Interaction Patterns between Wildlife and Cattle Reveal Opportunities for Mycobacteria Transmission in Farms from North-Eastern Atlantic Iberian Peninsula**

**Lucía Varela-Castro 1, \* , Iker A. Sevilla 1 , Ariane Payne 2 , Emmanuelle Gilot-Fromont 3 and Marta Barral 1**

**\*** Correspondence<EMAIL_ADDRESS>
**Simple Summary:** Mycobacteria can cause medically and socio-economically significant diseases, including several non-tuberculous infections and tuberculosis, and are considered a One Health challenge for their impact on public and animal health. These microorganisms are maintained and shared between the environment, domestic and wild animals, and humans. The aim of this research was to characterize the interactions that take place between several wild mammals and cattle through camera-trapping in order to provide insights into the dynamics of mycobacteria transmission opportunities in the environment of cattle farms located in Atlantic habitats from northern Iberian Peninsula. Camera traps were set during a one-year period in three cattle farms and visits of six wild species were modelled. We demonstrated that cross-species mycobacteria transmission, if occurring, would be mainly maintained through indirect interactions and most likely occur in pastures. In contrast to previous studies, wildlife visits were abundant but brief, and food and water resources did not attract wild animals. We suggest that badger latrines might act as aggregation points and sources of exposure to mycobacteria for badgers, wild boars, foxes, and cattle. This knowledge can contribute to designing and implementing effective measures aimed at controlling the spread of mycobacterioses in the environment–wild–domestic–human interface.

**Abstract:** Interactions taking place between sympatric wildlife and livestock may contribute to interspecies transmission of the *Mycobacterium tuberculosis* complex or non-tuberculous mycobacteria, leading to the spread of relevant mycobacterioses or to interferences with the diagnosis of tuberculosis. The aim of this study was to characterize the spatiotemporal patterns of interactions between wildlife and cattle in a low bovine tuberculosis prevalence Atlantic region. Camera traps were set during a one-year period in cattle farms with a history of tuberculosis and/or non-tuberculous mycobacterioses. The frequency and duration of wildlife visits, and the number of individuals per visit, were analysed through generalized linear mixed models. The seasons, type of place, type of point, and period of the day were the explanatory variables. A total of 1293 visits were recorded during 2741 days of camera observation. Only 23 visits showed direct contacts with cattle, suggesting that mycobacteria transmission at the wildlife–livestock interface would occur mainly through indirect interactions. Cattle pastures represented the most appropriate habitat for interspecies transmission of mycobacteria, and badgers' latrines appear to be a potential hotspot for mycobacteria circulation between badgers, wild boars, foxes, and cattle. According to both previous epidemiological information and the interaction patterns observed, wild boars, badgers, foxes, and small rodents are the species or group most often in contact with livestock, and thus may be the most involved in the epidemiology of mycobacterioses in the wildlife–livestock interface in this area.

**Keywords:** camera-traps; interactions; wildlife-livestock interface; tuberculosis; non-tuberculous mycobacteria

**Citation:** Varela-Castro, L.; Sevilla, I.A.; Payne, A.; Gilot-Fromont, E.; Barral, M. Interaction Patterns between Wildlife and Cattle Reveal Opportunities for Mycobacteria Transmission in Farms from North-Eastern Atlantic Iberian Peninsula. *Animals* **2021**, *11*, 2364. https://doi.org/10.3390/ani11082364

Academic Editors: Laila Darwich Soliva and Julie Arsenault

Received: 28 June 2021 Accepted: 5 August 2021 Published: 10 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

3 UMR 5558 LBBE, VetAgro Sup, Université de Lyon, 1 Avenue Bourgelat, F-69280 Marcy-l'Etoile, France<EMAIL_ADDRESS>
#### **1. Introduction**

Multi-host pathogens are often of wide concern because of the complexity that entails their control [1]. This control may become harder to manage when wild species are involved in their maintenance and transmission and even more difficult when poor or lacking farm biosecurity measures enable the occurrence of interactions between livestock and wildlife. In order to improve biosecurity, it is necessary to identify the places, moments, and circumstances that entail highest risk. Generally speaking, the rate of interactions between species tends to increase when scarce water or food sources are shared by domestic and wild species, such as in Mediterranean ecosystems, due to a high spatial and/or temporal overlap between them [2,3] and so does the probability of pathogen spread and transmission. Indirect transmission, more likely than direct transmission, tends to be involved across a community of host species [4]. However, when several host species are involved in the transmission of the same pathogen, it is crucial to identify the most important epidemiological connections between species and where/when these connections occur. Understanding interactions that can potentially lead to pathogen transmission at the wildlife-livestock interface is therefore a key for the implementation of appropriate disease control strategies in a multi-host system. However, this is often difficult to assess.

Animal tuberculosis (TB) is a worldwide zoonotic disease caused mainly by *Mycobacterium bovis* and other mycobacteria belonging to the *Mycobacterium tuberculosis* complex (MTC). Although cattle are considered its main and most well-studied host, *M. bovis* represents the perfect example of a multi-host pathogen with a complex and diverse spectrum of both domestic and wild hosts. In fact, a recent study has demonstrated that TB systems in some regions of Europe are dominated by non-bovine domestic and wild species [5]. *M. bovis* survival in the environment is highly variable according to environmental conditions but may last for several months [6], enhancing the likelihood of interspecies transmission within shared habitats (mainly through indirect contacts) [2,3]. On the other hand, the emerging prevalence of non-tuberculous mycobacteria (NTM) has become a matter of concern [7], even in countries reporting a low TB incidence [8]. Some of these NTM are associated with opportunistic or major mycobacterioses affecting humans and several domestic and wild species, as well as with interferences in the diagnosis of bovine TB [9]. NTM are widely distributed in a broad variety of aquatic and terrestrial environments [10]. Some species of veterinary relevance, such as *Mycobacterium avium* subsp. *paratuberculosis*, are able to persist in the environment for long periods [11].

In the Iberian Peninsula, multiple domestic and wild hosts are implicated in the epidemiology of animal TB. Among domestic species, cattle is still considered the main reservoir [12], despite the fact that other livestock can also play this epidemiological role (e.g., goats [13], sheep [14], and pigs) [15]. Although Spain is far from being considered officially TB free, the herd-level prevalence in cattle has been greatly reduced since the introduction of the National Eradication Program in 1987. In Atlantic regions in particular, this prevalence has been kept below one per cent [16] for the last twelve years. However, eradication has not been accomplished yet. In spite of the absence of a mandatory NTM surveillance program, NTM-infected cattle have also been detected in these regions during the national TB eradication campaigns among cattle showing false positive reactions to the tuberculin skin test [17,18]. The interactions between cattle and competent cohabiting wild hosts could contribute to this epidemiological picture of Atlantic Iberian Peninsula. There, the European badger (*Meles meles*) has been described as a potential wild reservoir of TB [19,20]. Furthermore, occasional TB cases have been detected in red deer (*Cervus elaphus*), and wild boar (*Sus scrofa*) seems to be implicated in the epidemiology of the disease (its role still being under debate) [17,21,22]. Besides, several species of NTM have been detected in these three wild species as well as in roe deer (*Capreolus capreolus*), wood mice (*Apodemus sylvaticus*), fox (*Vulpes vulpes*), and other carnivores such as the stone marten (*Martes foina*) and the mink (Varela-Castro, unpublished data) [17,18,23]. Before designing and implementing strategies aimed at reducing pathogens transmission between wild and domestic animals, deepening our current understanding of wild-domestic interaction

dynamics is necessary. Among the current tools available for this purpose, camera trapping is a non-invasive technique useful for the assessment of a broad variety of ecological phenomena [24–27], which can be helpful to delve into disease transmission mechanisms.

The aims of the present research were (1) to study through camera trapping the nature of interactions (direct or indirect, frequency, duration and number of animals per wildlife visit, and observed behaviours) between cattle and wild mammal species from the Basque Country, a low bovine TB prevalence Atlantic region, and (2) to investigate whether these interactions may vary according to season, period of the day, places and points sampled. The results will provide useful information for assessing the risk of transmission of mycobacteria that could help in designing potential control strategies adapted to this specific scenario.

#### **2. Materials and Methods**

#### *2.1. Study Area*

This study was carried out in the Basque Country, northern Iberian Peninsula, where the annual prevalence of TB among cattle herds has been less than one per cent for the last 17 years and kept below 0.1% since 2017 [16]. According to official censuses from 2018 [28], there are 134,611 cattle in 4703 farms. These animals graze in the pastures regardless of the management system. Even when managed under an intensive production system, enclosures are open to the field and no biosafety measures such as fencing are always implemented. Therefore, cattle may share the pastures with cohabiting wildlife. Traditional husbandry practices are still maintained by some farmers in the Basque Country, being communal pastures shared by cattle and other domestic species such as horses and sheep, during the summer. MTC infection among wild mammals from this region has been detected in wild boar (1.12%) and red deer (2.40%) [22]. Several species of NTM able to infect cattle and interfere with the diagnosis of bovine tuberculosis have been also detected in wood mice [18] and other wild species from the study area (Varela-Castro, unpublished data: wild boar, red deer, roe deer, badgers, foxes and stone martens).

Three cattle farms located in the municipalities of Kexaa, Kortezubi and Deba (named A, B and C, respectively (see Figure 1)) were selected to represent farms where TB and other mycobacterioses, mainly provoked by *M. avium* subsp. *paratuberculosis* and/or *Mycobacterium avium* subsp. *avium*, have been recently diagnosed. These farms hosted tuberculin skin test-reactor cattle that were subsequently confirmed as *M. bovis*-infected or as false positive cases. Almost half of the MTC-positive cattle of the last ten years in the Basque Country were detected in farms A and C and NTM were also detected in these farms, while in farm B only *M. avium* subsp. *avium* was detected [18]. Farms A and B are dairy farms and farm C is a fighting bull farm. All three farms follow a free-range system. Cattle from farm A can either graze in the pastures or stay indoors, since facilities are open all year long. Facilities from farm B are completely closed and cattle are kept indoors during autumn and winter. Bulls of farm C are always kept outdoors. There are no other domestic species in these farms that could be important in terms of MTC transmission.

#### *2.2. Camera Trap Survey*

During a one-year period (January to November 2017), a total of twenty-three infrared motion-triggered camera traps (CTs) (Trophy Cam HD Aggressor, Bushnell, Overland Park, KS, USA) were used for the detection of wild mammal visits in different places of the farms while cattle were present (either in the field or inside facilities connected with outdoors). The field design comprised a two-week sampling period per farm and season except for farm B, where cattle are kept in closed facilities with no contact with outdoors during autumn and winter; thus, no sampling periods were recorded for those seasons. Overall, 10 sampling periods were recorded. CTs owned movement detection up to 25 m and a response time of 0.2 s. They were programmed to work day and night, recording 10 s videos each time a movement was detected with a triggered interval of 5 s. Date and time were displayed for each video. CTs were tied on trees, spikes, fences, or walls at ≈50 cm

above the ground or up to 150–200 cm with a downward inclination, depending on the sampling point. When needed, branches that fell in the field of vision were removed. There was no overlap between the CTs' field of view.

**Figure 1.** Field sampling design. The map shows the location of the studied farms (**A**–**C**) within the Basque Country (northern Iberian Peninsula). The spatial distribution of the sampling points recorded within farms (**A**–**C**) is displayed on the satellite photographs (**A**–**C**), respectively (see legend for sampling point description). Purple lines surround the sites (1 to 13) included in the models as a random factor.

The sampled places were cattle pastures, bushy edges between pastures, farm buildings, and a pine forest. In each place CTs were set in one to several points that could be *a priori* attractive for some wild species, such as water or food sources, a badger latrine and a manure pile, as well as in points that could potentially indicate the presence of wildlife, such as wildlife paths or paths that could be used by both cattle and wild species (see Figure 1). Water sources were all located outdoors and could be either a stream, a pond or cattle troughs settled with a certain height but surrounded by a flooded ground. Food sources were located indoors and outdoors. Those located indoors could be cattle feeders settled on the ground or feed-storages with some spillage of grain, while those located outdoors were piles of straw or hay delivered on the ground or a hazelnut trees plantation. Because of husbandry practices such as the rotation of herds among different pastures, the number of cameras varied between sampling periods and some points were not recorded during all the sampling periods of each farm. Overall, we sampled 67 points located in 17 places (Table 1). Due to the unbalanced availability among sampling places and points in the study area, pastures and wildlife paths were the type of place and point of the survey with longer surveillance time (Table 1). All videos were checked for species identification. If a wild mammal was detected, the number of animals, their behaviour and the duration of the visit was also registered.

**Table 1.** Number of visits, surveillance recording hours and sessions grouped by type of place and point.

Numbers in brackets indicate the number of wild mammal visits.

#### *2.3. Variables Definition*

Since some CTs were located relatively close to each other, their observations could be non-independent. In each farm, we thus defined three to six sites, a "site" being a spatial unit corresponding to either a farm building or a pasture, including its bushy edges and the forest when present (see Figure 1). Each site (*n* = 13) was thus considered an independent area from other sites from the same farm, while the non-independence of observations within a site was accounted for in the analysis (see below). Distances between sampling points situated within a site varied among sites (range = 16 to 393 m).

We defined a "session" as a continuous period of monitoring on the same sampling point with the same camera. Although sessions were planned to last two weeks, some of them terminated earlier due to cattle moving CTs, thefts and unexpected battery depletion. For this reason, the duration (in hours) of each session was taken into account for subsequent analyses. We defined independent visits as (1) consecutive videos of individuals of different species; (2) consecutive videos of individuals of the same species more than 30 min apart; or (3) non-consecutive videos of a different or same species [29]. The number of visits per wild species, their duration (interval between the time displayed at the beginning of the first video and at the end of the last video included in the same visit, in minutes), and the number of animals per visit were the dependent variables. Animals could not always be individually identified, so the maximum number of individuals seen simultaneously in any of the videos of each visit was recorded. A direct interaction was defined as the simultaneous presence of cattle and at least one wild mammal on the same video. All other visits were considered as indirect interactions with cattle, since all points included areas used by cattle. Explanatory variables were the season (spring: April–June, summer: July–September, autumn: October–November, and winter: January–March), the period of the day (dawn, day, night and sunset, being the time slots determined according to the season where visits were observed), the place (pasture, farm building, forest, and edge) and the sampling point (water source, food source, manure, latrine, path, and wildlife path).

#### *2.4. Statistical Analysis*

The observed behaviours were classified focusing on those that could represent a risk of mycobacteria acquisition or excretion (Table 2). When none of these behaviours was observed, animals were considered as "moving through". If more than one behaviour were detected during a visit, either by one or more individuals, they were all recorded, except for "moving through" [30]. The percentage of occurrence of the different behaviours was

calculated for each species. Then, the frequencies of wildlife visits were described, for each species, in terms of means and standard errors (SEs) by computing the number of visits per month, based on the observations obtained for each session. The means and SEs were also computed for the duration of the visits and the number of individuals per visit. Afterwards, a description of the direct interactions between each species and cattle was performed.

**Table 2.** Description of behaviours observed among wild species.

Then, generalized linear mixed models (GLMMs) were used to analyse how the number of visits per each species, their duration and the number of individuals varied among seasons, periods of the day, type of place and type of point, using the farms (A, B and C) and the sites (1–13) within each farm as random effects in order to take into account the likely dependence of wildlife visits within farms and each site of every farm. For the number of visits, a model adjusted to a Poisson distribution was used and, in order to consider the sampling effort of the sessions, the logarithm of the number of surveillance hours per session was included in the model as an offset. For the number of animals and the duration of visits, models adjusted to Poisson and Gamma distributions were used respectively. A total of 18 models were initially fit, one per response variable and species. For each one, a maximal model including all the variables was first created. Hereafter, the dredge function of R software was used to generate a selection table of models with combinations of the fixed variables originally included in the maximal model. The selection of the best combination was made following the parsimony principle [31]: among models that had similar AIC values (delta < 2), the one with fewest parameters was selected.

Finally, we used the overdisp.glmer function of R in order to check whether overdispersion was still present in the residuals of the selected Poisson models [32]. Nakagawa and Schielzeth R-squared were used to determine the variability explained by the fixed and random parts of the selected models (using the r.squaredGLMM function of R software). All of the statistical analyses were performed using the R 4.0.0 software [33]. The data sets employed for the statistical analyses are submitted as Supplementary Material: Tables S1 and S2.

#### **3. Results**

#### *3.1. Data Collected from the Field Samplings*

Data were recorded during 2741 camera days (i.e., data obtained from a given camera over a given day) distributed into 180 sessions (mean duration ± standard error: 271.76 h ± 7.73). A total of 127,091 videos were recorded. Among them, 48,976 involved only cattle, 1329 other domestic species (cats, dogs, and horses), 4942 birds, 2320 wild mammals, and 4 reptiles. In 71 videos, it was not possible to identify the species. Wild mammal videos involved wild boar, roe deer, badger, fox, other carnivores (hereafter OC group, which includes genet (*Genetta genetta*), stone martens and pine martens (*Martes martes*)), small rodents (mouse-like), hedgehogs (*Erinaceus europaeus*), squirrels (*Sciurus vulgaris*), and bats. After excluding those species without previous epidemiological data on mycobacterial infection in the study area (hedgehogs, squirrels and bats), 2182 videos of wild mammals were retained for the analyses.

A total of 1293 visits by wild species of interest were registered, each visit being recorded by 1 to 33 videos. All species visited the farms during all seasons. Pastures and wildlife paths received the highest number of visits (Table 1). Since the observed species were mainly nocturnal, most of the visits (85%), including direct contacts with cattle, took place at night. Visits occurred in 64 out of the 67 sampling points. The three points that did not receive any visits were food sources located inside a farm building (2 points) and in a pasture (1 point). Wild boar, fox and small rodents were the only visitors of farm buildings (Table 3).

**Table 3.** Description of wild mammal visits. Mean ± SE and range are shown for frequency of visits per month, visit duration, and number of individuals per visit.

Numbers in brackets indicate the total number of visits.

#### *3.2. Frequency and Characterization of Visits Per Species*

Figure 2 shows the proportion of occurrence of the behaviours exhibited per species. The most frequent behaviour was moving through (60% of the visits), followed by sniffing (31%), being both behaviours displayed by all species. Table 3 describes wild mammal visits in terms of frequency, number of individuals per visit, and duration of visits. The frequency of visits was highest for foxes, followed by badgers, wild boar, roe deer, small rodents, and the OC group. Small rodents were the group that showed longest visits on average (5.12 min ± 1.68), while the OC group showed the shortest on average (0.47 min ± 0.13). The species that showed up in more numerous groups was the wild boar (2.57 individuals ± 0.10, up to 10 individuals), while the rest of the species showed mainly solitary incursions (82% of visits performed by a single individual) or appeared, punctually,

in small groups (up to four badgers, up to three roe deer, foxes, and small rodents). Even though visits longer than half an hour occurred sporadically (1% of the visits) except for the OC group, short visits (less than 5 min) were predominant (93% of the visits). Twenty-three direct contacts with cattle were recorded (see Table 4). Thus, the other 1270 visits were considered as indirect interactions. The fox was the species which showed most direct contacts with cattle (eight), followed by small rodents (six) and wild boar (six), badger (two) and roe deer (one). No direct interaction was recorded for the OC group. More than half of these direct contacts took place during autumn (13/23) and within pastures (17/23), being more frequent in wildlife paths (9/23). Those that took place in farm buildings were mostly between small rodents and cattle (4/5). Even though the most common behaviour recorded during the visits was moving through, when a direct interaction took place, wild animals showed other behaviours such as sniffing, scent marking or foraging, except for small rodents (Table 4).

**Figure 2.** Percentage of the occurrence of each behaviour exhibited per species.

**Table 4.** Description of direct contacts between wild mammals and cattle.

Numbers in brackets indicate the number of direct contacts.

Table 5 shows the outputs of the models selected to explain the frequency of visits, the number of individuals per visit, and the duration of visits for each wild mammal. Models related to the number of individuals could only be fit for wild boar data, due to the quasi-absence of variability for other species. The overdispersion of residuals was limited for all Poisson models, ranging from 0.6 to 3.03. The random effects (farms and sites within farms) accounted for 0 to 91.5% of the variations.

*Animals***2021**, *11*, 2364

**Table 5.** Models selected per wild species and response variable. For each model, the table gives the percentage of variation explained by fixed and random parts of the model, and the OR, estimate and *p*-value of Wald test for each contrast between the reference level and the given level. The number of individuals was analyzed for wild boar only due to the quasi-absence of variability for other species.

**Table 5.** *Cont.*

**Table 5.** *Cont.*

V.E = Variation explained. ref = reference level of the fixed effect. \*:*p*-value≤0.05. \*\*:*p*-value≤0.01. \*\*\*:*p*-value≤0.001.

91

#### 3.2.1. Badger

No badger visit was recorded during the day, inside farm buildings, or in food sources. The two direct interactions with cattle were both observed in the latrine (Table 4). Pastures were the place where badger visits were most frequent and longest. The frequency of visits was also significantly higher in winter than in summer (OR = 2.49, 95% CI: 1.75–3.54). Moreover, visits were significantly more frequent in the badger latrine than in wildlife paths (OR = 3.81, 95% CI: 1.97–7.35), but significantly less frequent in paths (OR = 0.46, 95% CI: 0.31–0.68) or in the manure pile (OR = 0.04, 95% CI: 0.01–0.31) than in wildlife paths. The duration of visits was also significantly shorter in paths (OR = 0.41, 95% CI: 0.25–0.70) compared to wildlife paths (Table 5).

#### 3.2.2. Wild Boar

No diurnal visit was recorded. Apart from one case that was recorded in a farm building, direct interactions with cattle took place in pastures (5/6) (Table 4), including one in the latrine. The frequency of visits was significantly higher in autumn (OR = 1.57, 95% CI: 1.13–2.18) and spring (OR = 1.38, 95% CI: 1.02–1.88) compared to summer, while it was significantly lower during winter (OR = 0.39, 95% CI: 0.23–0.67). The same tendency was also observed for the number of animals per visit, being significantly higher in autumn compared to summer (OR = 1.52, 95% CI: 1.20–1.94). However, the visits were significantly longer in winter (OR = 3.41, 95% CI: 1.54–7.57). Wild boar visits were significantly more frequent in the badger's latrine (OR = 9.07, 95% CI: 4.47–18.42) and significantly less frequent in food sources (OR = 0.34, 95% CI: 0.13–0.94). Lastly, wild boars were significantly less numerous during the dawn than at night (OR = 0.59, 95% CI: 0.38–0.92) (Table 5).

#### 3.2.3. Roe Deer

No visits were recorded in farm buildings, in the manure pile or in the latrine. The only direct interaction with cattle recorded took place in summer, during the day and in a wildlife path located in pastures (Table 4). Visits were significantly less frequent during spring (OR = 0.30, 95% CI: 0.21–0.44) and shorter during spring (OR = 0.10, 95% CI: 0.06– 0.16) and autumn (OR = 0.28, 95% CI: 0.10–0.81), if compared with summer. Visits were significantly longer in food sources than in wildlife paths (OR = 22.15, 95% CI: 4.46–110.05) (Table 5).

#### 3.2.4. Fox

Foxes were seen at all periods of the day, type of places and type of points. All direct interactions with cattle but two that were recorded in the latrine and in a path took place in wildlife paths (6/8) (Table 4). Compared to wildlife paths, visits were significantly more frequent in badger's latrine (OR = 2.79, 95% CI: 1.42–5.49) and less frequent in food (OR = 0.15, 95% CI: 0.05–0.42) or water sources (OR = 0.62, 95% CI: 0.42–0.93). Water sources received shorter visits (OR = 0.32, 95% CI: 0.13–0.83) than wildlife paths did. In comparison to summer, these were significantly longer in spring (OR = 2.02, 95% CI: 1.10–3.68) and shorter in autumn (OR = 0.44, 95% CI: 0.20–0.97). Moreover, they were significantly longer during the day than during the night (OR = 3.76, 95% CI: 1.61–8.78), and shorter in edges than in pastures (OR = 0.38, 95% CI: 0.15–0.97) (Table 5).

#### 3.2.5. Other Carnivores

Most of these visits were performed by genets (33/38), even though stone martens (3/38) and martens (2/38) could be sporadically observed. All visits by these species were recorded in the pastures and their edges. No visit was recorded in food sources, in the latrine, or in the manure pile. Visits were significantly less frequent (OR = 0.19, 95% CI: 0.06–0.60) and shorter (OR = 0.36, 95% CI: 0.20–0.66) in pastures than in edges, and less frequent in water sources than in paths (OR = 0.20, 95% CI: 0.06–0.67) (Table 5).

#### 3.2.6. Small Rodents

No visit was recorded during sunset, in the forest, in the manure pile, or in the latrine. Unlike the rest of the species, direct interactions between cattle and small rodents were more frequent inside farm buildings (4/6) (Table 4). The selected models showed that frequency and duration of visits were significantly lower in summer (OR = 0.03, 95% CI: 0.01–0.09), winter (OR = 0.11, 95% CI: 0.05–0.23) and spring (OR = 0.15, 95% CI: 0.08–0.29) compared to autumn. Their frequency was also lower in edges compared to pastures (OR = 0.33, 95% CI: 0.15–0.72) and in paths compared to wildlife paths (OR = 0.53, 95% CI: 0.31–0.92) (Table 5).

#### **4. Discussion**

#### *4.1. Methodology*

Camera trapping has proved to be a useful tool for studying interactions with a minimal disturbance to animals. However, failures in the detection due to intrinsic characteristics of the CTs (distance detection, response time), the position angle when hanging the devices, camera malfunctioning, and loss of battery power or adverse weather conditions may have led to an underestimation of the number and duration of visits and the number of individuals. Hence, our observations correspond to a minimum of what is actually occurring in these farms. On the other hand, due to husbandry practices and organization issues, there was an imbalance in the field sampling, since all points were not sampled at all seasons. This could have limited our ability to detect seasonal variations. Besides, the small sample size of some types of places and points (e.g., forest, latrine; see Table 1) has narrowed the information obtained from them and may be underrepresented.

#### *4.2. Spatiotemporal Patterns of Wildlife-Cattle Interactions*

Our observations confirmed that as well as in other regions [2,3,30,34], indirect interactions between wildlife and cattle can be considered more frequent than direct interactions. Different spatial and temporal patterns were observed depending on the species surveyed.

Winter was the most favourable season for badger visits to occur. This season is a period of food scarcity for this species, whose diet in the Basque Country is mainly based on earthworms and garden fruits [35]. However, badgers did not approach farm buildings or food sources, probably due to the fact that cattle resources (e.g., silage or hay) lack attractiveness for them. These findings are consistent with those previously reported in a medium density population area, where badgers clearly avoided farmyards [36] but differ with some British [37,38] and French studies [30], where high rates of building use were described. Resource availability and badger population density might account for these differences. In agreement with previous reports [39], pastures were the preferred place for badgers in our study area. Earthworm intake might explain the attractiveness of pastures, since their soft ground is suitable for foraging. The most attractive point for this mustelid was the badger latrine. Moreover, the few direct interactions recorded between cattle and badgers always took place in the latrine. For these reasons, this point might be considered as a potential hotspot for both indirect or direct interactions between these two species. However, in the absence of other latrines, it is not clear whether this high frequency of visits was due to the latrine itself or to another specific feature of this particular point.

The seasonal differences observed in the frequency and duration of wild boar visits, as well as in the number of individuals per visit, may be due to different factors. In winter, wild boar density is at its lowest and the duration of incursions may be longer when searching for food. However, farm food sources were the less attractive point for this species. This could be related to a higher availability of natural resources in the study area, at least compared to areas from southern Spain, where baited points turned out to be very attractive to wild boar [2]. As well as with badgers, wild boar visits were more frequent in the badger latrine, possibly due to an attraction effect of its characteristic scent. Thus, the latrine could also represent a significant point for wild boar to interact with other species.

Roe deer visits were most frequent and longest in summer, coinciding with the mating period of this species. Some of the activities during this period, such as the defence of the territory, the avoidance of dangerous fights and the chasing of females by bucks [40] might make them more visible. Actually, the only direct contact recorded between roe deer and cattle took place in pastures during summer and by day. Longer visits were observed in food sources than in other sampling points. Roe deer is mainly a browser, not a grazer [41], so they are not expected to use the same food resources as cattle. Indeed, all roe deer visits to food sources were recorded in the hazelnut trees plantation, which represents the only resource not interesting for cattle.

As for badgers and wild boar, the badger latrine was the most visited point by foxes and also the scene of a direct interaction with cattle, which supports considering the badger latrine a potential hotspot for intra and interspecies interactions in our study area. Despite visiting all types of points, foxes showed less interest for food and water sources. This may reflect their interest in other food supplies, such as small mammals to prey on. Conversely to other species, fox visits were longer during the day because they spent a long time resting on the pastures. Lastly, visits were shorter in autumn and longer in spring compared to the summer. These findings could be also related to their rest times, which were longer during the warmest seasons.

OC group species turned out to be less often seen and always alone. Visits were most frequent and longest in the edges, which is consistent with their search for protection from predators and unfavourable weather conditions [42]. The majority of visits were performed by genets, and almost half of them were recorded in one specific path within the edge of one pasture. Genets spend most of their time resting in the same place [42]. Thus, the potential existence of a resting site close to this path might explain the output of the models.

Although small rodent species could not be determined, in a previous study conducted in the same farms the wood mouse was the species most frequently captured [18]. Small rodents visits occurred more often and were longer in autumn than during other seasons, probably since this is a period when most rodent populations, such as the widely distributed wood mouse population, are at their maximal abundance [43]. These wild rodents typically move along field margins of farmlands and are known to be common in hedgerows [44], which might explain their preferences for pastures and wildlife paths from the study area.

#### *4.3. Opportunities of Mycobacteria Transmission*

Since indirect interactions were much more common than direct interactions, mycobacteria transmission at the wildlife-livestock interface, if occurring, would be mainly held through indirect interactions. In general, pastures represent the most appropriate place for interspecies transmission of mycobacteria in the study area. Our results suggest that badger latrines can be suitable places for both indirect and direct contacts at least between badgers, wild boar, foxes and cattle. During the visits to the latrine, individuals of these three wild species showed behaviours related to possible excretion of or exposure to pathogens such as sniffing or scent marking, where cattle was also seen sniffing or grazing. Consequently, these points could be considered potential hotspots for mycobacteria circulation in this habitat. However, a single latrine was found and recorded, and therefore, further studies are needed to confirm this hypothesis.

Some remarkable differences have been identified between this study and previous reports. In our study, whatever the wild species considered, the average duration of the visits was shorter (<5 min) than in studies from France [29,30]. For instance, the average duration of wild boar visits was significantly shorter in our study area (1.64 min) than in a bovine TB-infected area in France (14.5 min) [30]. The most common behaviour in our study area was "moving through". Wild species mainly move around shared habitats with cattle, but resources such as water or food supplies do not act as aggregation points, conversely to the results of previous studies [2,29,30,45]. A higher availability of natural resources throughout the whole year may account for these differences. These findings, together with the absence of MTC-infected individuals among wildlife, except for wild boar, and the

low TB prevalence reported for this wild ungulate and cattle from the Basque Country [22], suggest that the risk of MTC transmission between wild animals and cattle would be, overall, low. On the contrary, we suspect that a risk of indirect NTM transmission could be more feasible in the study area, since this group of mycobacteria have been detected in all species and the prevalence observed in some of them was significant.

Since interaction patterns and infection figures differed among wild species, some of them might be more involved than others in the epidemiology of mycobacterioses in the Basque Country. Thus, depending on the species and the situation, different control strategies could be implemented to maximize effectiveness. Foxes, badgers and wild boars were the species observed most frequently. Even though not considered as a TB reservoir in the Atlantic Iberian Peninsula, wild boar is the only species observed in this study and found to be infected with MTC in the study area, since red deer distribution is limited to a few settings that do not encompass these farms. Wild boar has shown an unexpectedly high MTC seroprevalence of 17% in this region [21] and, despite the low prevalence detected by culture (<2%) and the absence of animals with disseminated lesions/infection, a potential geographical link was found between spoligotypes identified in cattle and wild boar [22]. Furthermore, culture methods revealed a 9% prevalence of NTM in this species. Considering this information, the high frequency of visits and the high proportion of individuals per visit, wild boar could contribute to the dynamics of mycobacteria transmission in the Basque Country. Conversely, the badger is already considered a potential reservoir of TB in neighbouring Atlantic regions [19,20] but no infected individual was found in our study area [22]. However, a high prevalence of NTM infection (17%) was detected in this mustelid, as well as in other regions of northern Iberian Peninsula [23]. Since the ability of badger to transmit MTC is already confirmed, either as a TB maintenance host or as a bridge between other species through its latrines [46], the potential role of this carnivore in the epidemiology of mycobacterioses in the Basque Country should not be ruled out. MTC-infected foxes have been sporadically found in Spain [47] but not in the Basque Country, where 46 individuals were analysed throughout a 10-year survey [22]. The fox is currently considered a spill-over host of TB in Europe [48] (i.e., populations cannot maintain infection on the long-term, but may transmit it to other species), even though the prevalence reported in foxes ranged from 9% in four TB endemic areas of France [49] to 26% in Portugal [50,51]. In addition, the prevalence of NTM in foxes from the Basque Country (4.3%) was lower compared to badgers and wild boar. Nevertheless, the fox was the species most often observed and for which most direct contacts with cattle were recorded, so its behaviour could counteract its apparent irrelevance in the epidemiology of mycobacterioses. A study carried out earlier in the same three farms proved that small rodents such as *A. sylvaticus* can carry potentially pathogenic NTM with the ability to cross-react with TB diagnosis in cattle, reporting an overall prevalence of 6.5% [18]. However, no species belonging to the MTC were detected. The scarce literature available on the epidemiology of natural *M*. *bovis* infection in small rodents suggests that these animals could be dead-end hosts (i.e., not able to transmit infection to other species [52–54]). However, the field vole (*Microtus agrestis*) is considered as a natural maintenance host for *M*. *microti*, a role that other small rodents like the wood mouse might play, maintaining the infection and spreading the bacteria through wounds inflicted to their predators or by indirect transmission through sputum, saliva or skin crusts [55,56]. These routes should not be ruled out for NTM transmission. Although most of the visits in our study were recorded in pastures and wildlife paths, small rodents were also observed inside the farm buildings and, conversely to the rest of the studied species, most of their direct contacts with cattle took place inside the enclosures. Roe deer visits were on average more frequent than those of small rodents and longer compared to badgers, wild boars and foxes. In the Basque Country, no cases of TB were detected [22] and the prevalence of NTM in roe deer was 4.70%. Like the fox, the roe deer has been considered a spill-over host, particularly in endemic areas [57]. However, TB cases in roe deer are reported even more sporadically [58]. The behaviour of this species during this study (mostly solitary, not

observed close to farm buildings, preference for food sources disregarded by other species and almost no direct contacts with cattle) suggests that roe deer is unlikely to play any role in the epidemiology of TB in this low prevalence area. Accordingly, its relevance in the epidemiology of other mycobacterial infections seems to be limited. Finally, OC group could be considered the least threatening in terms of mycobacteria transmission risk in the study area. If we focus on the species observed in this study, *M. avium* was detected in one stone marten out of 18 specimens analysed in the Basque Country. Besides, to the best of our knowledge the only cases of MTC infection reported in Europe belonged to one stone marten and two genets from Portugal [51]. These epidemiological features, as well as the behaviour observed (lowest frequency and duration of visits that are always performed by one individual, no direct interaction with cattle and preference of edges over pastures) support our statement.

The findings of this study together with previous results in wildlife from the Basque Country and the low TB infection prevalence observed in cattle do not show a strong justification for intervention to reduce the risk of mycobacteria transmission at the wildlife– livestock interface. However, monitoring has proved to be an essential tool for defining the most appropriate measures if the situation changes. To reduce wild visits to farms, combined strategies rather than a single one would be more effective [59]. This study suggests that biosafety should particularly concern pastures, for example by activating electric fencing at night and at different heights, taking into account the anatomy of the species of interest. Furthermore, according to our results the presence of badger latrines inside pastures should be at least identified and hereafter reduced or cattle access to them should be avoided. These measures may reduce indirectly the presence of wild boar and foxes in these particular points as well. Because measures directed at minimizing contacts between cattle and small rodents are difficult to implement, rodent population control strategies may indirectly help to reduce these interactions. Notwithstanding, MTC and *M. leprae* put aside and in spite of a few recognized pathogens such as *M. avium* subsp. *paratuberculosis* and *M. avium* subsp. *avium* [60,61], the vast majority of NTM are generally regarded as environmental and ubiquitous or opportunistic pathogens at the most [62,63]. This fact makes NTM control even more challenging. Strategies to avoid exposure to these mycobacteria may rely more strongly on hygiene than on preventing contact between animals. Possible measures include avoidance of animal-driven farm environmental contamination, water contamination, biofilm formation, and surface spreading [63].

#### **5. Conclusions**

The results of the present study combined with the information derived from our previous epidemiological surveys suggest that four wild species or groups might be most involved in the epidemiology of mycobacterioses in the Basque Country: wild boar, badgers, foxes, and small rodents. Cattle pastures were the most frequently visited habitat and indirect interactions represent the most likely route for the potential transmission of mycobacteria between cattle and wild species. Conversely to previous studies, food and water sources did not attract wild species in this region, while badger latrines could have acted as aggregation points and as a source of mycobacteria exposure for badgers, wild boars, foxes and cattle. Further studies are first needed to confirm that interactions between wild species and cattle in pastures occur preferentially on the latrines. If so, their identification and management would be a key to avoid inter-species transmission on pastures in this area. Moreover, analysing interactions among these three wild species around latrines and in other interfaces (elsewhere than in farm environment) will be needed to completely understand mycobacteria transmission dynamics. In agreement with the low TB prevalence of the study area, the risk of MTC transmission at the wildlife-livestock interface is expected to be low. However, the risk of NTM interspecies transmission in the Basque Country is more likely than that of MTC, which could result in the maintenance and spread of potentially pathogenic mycobacteria that could also affect the tuberculin test specificity in cattle. The current quantification and qualification of connections among

several hosts provides a valuable insight into the dynamics of transmission within the wildlife-livestock interface.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/ani11082364/s1, Table S1: Sessions database; Table S2: Visits database.

**Author Contributions:** Conceptualization, M.B.; methodology, M.B., L.V.-C., E.G.-F. and A.P.; formal analysis, L.V.-C.; investigation, M.B., I.A.S. and L.V.-C.; resources, M.B., E.G.-F. and A.P.; data curation, L.V.-C., E.G.-F. and A.P.; writing—original draft preparation, L.V.-C.; writing—review and editing, M.B., L.V.-C., I.A.S., E.G.-F. and A.P.; visualization, L.V.-C.; supervision, M.B., I.A.S. and E.G.-F.; project administration, M.B. and I.A.S.; funding acquisition, M.B. and I.A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Institute for Agricultural and Food Research and Technology (INIA) (Research Project RTA2014-00002-C02-02), the "Departamento de Desarrollo Económico, Sostenibilidad y Medio Ambiente" of the Basque Government and the Interreg-POCTEFA EFA357-19-INNOTUB Project (FEDER co-funded). LV holds a pre-doctoral fellowship from the National Institute for Agricultural and Food Research and Technology (INIA), grant number CPD2016-0006.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, since camera trapping allows for collection of information on wild animals without capturing or handling them. Permissions for the field work were obtained from the competent authorities (corresponding approval numbers and dates: 6387/2917 in December 2017, 1907 in March 2017 and 183 in February 2017).

**Data Availability Statement:** The data presented in this study are contained within the Supplementary Materials Tables S1 and S2.

**Acknowledgments:** We want to thank the Provincial Councils of Araba, Bizkaia, and Gipuzkoa for giving us the permissions needed for camera trapping activities. A sincere appreciation goes to Diana Paniagua, Andrés Illana and Jorge Echegaray, members of GADEN, for their implication in the field work design and for the substantial work they performed on the identification of the species appearing in all the videos. Special thanks go to Vega Álvarez, Xeider Gerrikagoitia, Olalla Torrontegi and Miriam Martinez de Egidua for their collaboration with the field work, as well as to José Luis Lavín for helping to build the initial database in a record time. We also want to thank the laboratory UMR 5558 Laboratoire de Biométrie et Biologie Évolutive for receiving Lucía Varela-Castro for a training on the statistical methods used in this work. We are grateful to the farmers because this study would not have been possible without their collaboration.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Alkali Activated Cements**

**Chapter 14**

**Provisional chapter**

**Clay-Based Materials in Geopolymer Technology**

**Clay-Based Materials in Geopolymer Technology**

DOI: 10.5772/intechopen.74438

The term "geopolymer" was introduced by Davidovits in the 1970s. The prefix "geo" was selected to symbolize the constitutive relationship of the binders to geological materials, natural stone and/or minerals. Geopolymer is mineral polymers of inorganic polymer glasses with structure resembling natural zeolitic materials. Previously, geopolymer formation used source materials such as clay (e.g. kaolin and calcined kaolin) or industrial by-product (e.g. slag and fly ash). The precursor material plays an important role in the formation of geopolymer. The source material provides silicon (Si) and aluminum (Al) for reaction by an alkali activator solution. The Si and Al contents in the source materials dissolve in the alkaline activator solution and then polymerize to form a polymeric Si-O-Al-O framework which becomes the binder. Geopolymeric materials are attractive because of their excellent mechanical properties; durability and thermal stability can also be achieved. Owing to their low calcium content, they are more resistant to acid attack than materials based on Portland cement. In addition, they are of great interest because of the reduced energy requirement for their manufacture and the higher sustainability. Recently the search for alternative low cost and easily available materials led among others to Clay. Clay generally consists of a mixture of different clay minerals and associated minerals, which are strongly affected by the nature of the parent rocks. These materials are extensively distributed over the surface of the world and may show certain reactivity after a thermal activation process shows a great potential to be utilized in geopolymer technology. This article presents the potential of different types of clay as the source materials for geopolymerization reaction in terms of morphological properties. Moreover, the mechanical and microstructural properties of geopolymer made with vari-

ous kinds of clay and its potential application are also presented.

**Keywords:** geopolymer, inorganic polymer, clay

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Mohd Mustafa Al Bakri Abdullah, Liew Yun Ming,

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Heah Cheng Yong and

**Abstract**

Muhammad Faheem Mohd Tahir

Muhammad Faheem Mohd Tahir

Mohd Mustafa Al Bakri Abdullah,

Liew Yun Ming, Heah Cheng Yong and

http://dx.doi.org/10.5772/intechopen.74438

#### **Clay-Based Materials in Geopolymer Technology Clay-Based Materials in Geopolymer Technology**

DOI: 10.5772/intechopen.74438

Mohd Mustafa Al Bakri Abdullah, Liew Yun Ming, Heah Cheng Yong and Muhammad Faheem Mohd Tahir Mohd Mustafa Al Bakri Abdullah, Liew Yun Ming, Heah Cheng Yong and Muhammad Faheem Mohd Tahir

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.74438

#### **Abstract**

The term "geopolymer" was introduced by Davidovits in the 1970s. The prefix "geo" was selected to symbolize the constitutive relationship of the binders to geological materials, natural stone and/or minerals. Geopolymer is mineral polymers of inorganic polymer glasses with structure resembling natural zeolitic materials. Previously, geopolymer formation used source materials such as clay (e.g. kaolin and calcined kaolin) or industrial by-product (e.g. slag and fly ash). The precursor material plays an important role in the formation of geopolymer. The source material provides silicon (Si) and aluminum (Al) for reaction by an alkali activator solution. The Si and Al contents in the source materials dissolve in the alkaline activator solution and then polymerize to form a polymeric Si-O-Al-O framework which becomes the binder. Geopolymeric materials are attractive because of their excellent mechanical properties; durability and thermal stability can also be achieved. Owing to their low calcium content, they are more resistant to acid attack than materials based on Portland cement. In addition, they are of great interest because of the reduced energy requirement for their manufacture and the higher sustainability. Recently the search for alternative low cost and easily available materials led among others to Clay. Clay generally consists of a mixture of different clay minerals and associated minerals, which are strongly affected by the nature of the parent rocks. These materials are extensively distributed over the surface of the world and may show certain reactivity after a thermal activation process shows a great potential to be utilized in geopolymer technology. This article presents the potential of different types of clay as the source materials for geopolymerization reaction in terms of morphological properties. Moreover, the mechanical and microstructural properties of geopolymer made with various kinds of clay and its potential application are also presented.

**Keywords:** geopolymer, inorganic polymer, clay

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **1. Introduction**

In 1978, the word "geopolymer" was introduced by Davidovits [1]. In general, geopolymer is an inorganic polymeric material formed through the reaction between aluminosilicate sources and highly alkaline silicate solution, followed by curing at ambient or slightly higher temperature [2]. The formation process is termed as geopolymerization reaction.

Geopolymer has an empirical formula of:

$$M\_{\text{\tiny\text{\tiny\text{\tiny\text{\tiny\text{\tiny\text{\tiny\text{\tiny\text{\text}}}}}}} \left\{ - \left( \text{SiO}\_{2} \right)\_{\text{\tiny\text{\textgreater}}} - \text{AlO}\_{2} \right\} \cdot w \text{ H}\_{\text{\tiny\text{\textquotedblleft}}} \text{O} \tag{1}$$

**2. Aluminosilicates**

**3. Kaolin/kaolinite**

and SiO2

The aluminosilicate sources are materials rich in alumina and silica content (e.g. ashes [11–14], clays [15, 16] or slag [17, 18]). Some other natural and artificial silicoaluminates such as zeolite [19] and magnesium-contained minerals [20] have also been used as an important source of Si4+ and Al4+ ions in the geopolymer binding system. Normally, the total composition of Al2

Kaolinite is the most common clay mineral used in geopolymer synthesis. It has 1:1 uncharged

(gibbsite) sheet linked by sharing oxygen atoms. The layers are held together by weak van der

ter, the utilization of clay or clay minerals in geopolymer formation is discussed.

dioctahedral layer structure (**Figure 2a**) whereby the layers are (Si2

**Figure 2.** Structure of kaolinite (above) and microstructure of kaolinite (below) [22].

waals and hydrogen bonds leading to the layered structure (**Figure 2b**).

is more than 70%, preferable in reactive amorphous phase [3, 21]. In this book chap-

O5 )*n*

2− sheet and the Al(OH)3

Clay-Based Materials in Geopolymer Technology http://dx.doi.org/10.5772/intechopen.74438

O3

241

where M is cation such as K+ , Na+ or Ca2+; *n* is the degree of polycondensation; *z* is 1, 2, 3 and *w* is the amount of binding water. It has three-dimensional Si-O-Al polymeric networks ranging from amorphous to semi-crystalline. Tetrahedral SiO4 and AlO4 are linked alternately by sharing oxygen atom as shown in **Figure 1**. As refer to **Figure 1**, the terminology of geopolymers can be categorized into three forms which are poly(sialate), poly (sialate-siloxo) and poly (sialate-disiloxo). The Al is in IV-fold coordination [3, 4]. This leaves a negative charge in the IV-fold coordinated Al that is charge-balanced by cations (Na+ , K+ , Li+ , Ca2+, Ba2+, NH4+ and H3 O+ ). The charge-balancing by cations is important in determining the structural integrity and fragility of geopolymers [5].

The cations is usually contributed by alkaline silicate solution which is a mixture of alkali hydroxides (NaOH or/and KOH) and silicate solution (Na2 SiO3 or/and K2 SiO3 ) [6–8]. The alkali hydroxide is required for the dissolution of aluminosilicates while alkali silicate acts as binder, alkali activator and dispersant or plasticizer [9]. The alkali silicate solution contributes certain amount of SiO2 for the geopolymerization reaction [10].

**Figure 1.** Geopolymer systems based number of siloxo Si-O units [2].


**1. Introduction**

358 Solar Cells – Thin-Film Technologies

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*Conference, 2005*, pp. 1464-1467

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*Solar Cells*. Elsevier B. V., ISBN<PHONE_NUMBER>445

*Congress 2007 (Vol. I – Vol. V)*, pp. 1220-1223

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1545, Philadelphia, PA

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Kluwer Academic Publishers, ISBN<PHONE_NUMBER>178 Boston

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The microcrystalline silicon material is reported to be a quite complex material consisting of an amorphous matrix with embedded crystallites plus grain boundaries. Although this material has a complex microstructure, its optical properties have a marked crystalline characteristic: an optical gap at 1.12 eV like c-Si. This implies the spectral absorption of µc-Si:H covers a much larger range than a-Si:H which posses an optical gap between 1.6 and 1.75eVi . Compared to a-Si:H that absorbs light up to 800 nm, µc-Si:H absorbs light coming from a wider spectral range, extending up to 1100 nm . On the other hand, within its range of absorption, the absorption of a-Si:H is higher than that of µc-Si:H –due to the indirect gap of the latter. Therefore, the optical combination of these two materials takes advantage of a larger part of the solar spectrum (compared to a single-junction cell) and the conversion efficiency of the incident light into electricity can be consequently improved. Furthermore, the µc-Si:H solar cell is reported to be largely stable against light induced degradation and enhanced carrier mobility in contrast to amorphous silicon films counterpart. Consequently hydrogenated microcrystalline silicon is one of the promising materials for application to thin-film silicon solar cells.


*Edited by Cornel Iancu*

Surgeons from various domains have become fascinated by endoscopy with its very low complications rates, high diagnostic yields and the possibility to perform a large variety of therapeutic procedures. Therefore during the last 30 years, the number and diversity of surgical endoscopic procedures has advanced with many new methods for both diagnoses and treatment, and these achievements are presented in this book. Contributing to the development of endoscopic surgery from all over the world, this is a modern, educational, and engrossing publication precisely presenting the most recent development in the field. New technologies are described in detail and all aspects of both standard and advanced endoscopic maneuvers applied in gastroenterology, urogynecology, otorhinolaryngology, pediatrics and neurology are presented. The intended audience for this book includes surgeons from various specialities, radiologists, internists, and subspecialists.

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Photo by zilli / iStock


**2. Historical perspective of allergen-based diagnostic methodologies**

### **2.1. Skin prick test (SPT)**

Skin prick testing is an essential clinical test to confirm sensitization in IgE-mediated allergic diseases. Historically, we can found an early report in 1850, in a textbook of Henry Salter, a physician from London´s Charing Cross Hospital who described the formation of wheals following scratches in patients with asthma and exposed to cats [3]. In 1907, Clemens von Pirquet reported a modification of Koch´s subcutaneous procedure based in abrasion of the skin to evaluate tuberculin response [4]. This procedure was rapidly adopted by others as a prototype for prick-puncture testing, and in 1909, the first case of anaphylactic response after scarification and exposition to an allergen was reported [5]. Practical application of a standardized procedure was suggested by Schloss [6] who described a correlation of time with clinical signs, reporting 5–15 min of erythematous reaction after abrasion of the skin in a child with rhinitis, asthma, and eczema. Since then, several techniques to evaluate allergenic sensitization have been described, e.g., intracutaneous test, [7] conjunctival test, [8] intracutaneous test by serial dilutions [9]. Nowadays the best technique to evaluate with safety allergenic sensitization is the SPT.

The standardized method of prick testing includes the appropriate selection of allergens, i.e., allergens tested are according to the country, the geographic location inside the same country, and even with seasons [10, 11]. SPT is based on the presence of sensitized cells, mainly mast cells in the skin, and the resulted cutaneous reactivity is used by the clinician as a surrogate biomarker for sensitization in eyes, nose, lung, gut, and skin. During the test, positive and negative controls must be included, a positive result is defined with a wheal ≥3 mm diameter after 15–20 min; reproducible results are obtained with standardized mixtures [12, 13]. In the early years of use, skin prick testing did not have with the entire approbation of the medical community, and their clinical relevance was questioned [14]. That vision has changed, and in the last years, it has been recognized a concordance between the clinical manifestations and allergen-specific wheal size [15]. Thus, skin prick test is considered as a fundamental technique to explore allergen sensitization in patients, but if it is true, why we need other methods to study sensitization in allergic/hypersensitivity conditions? In the following paragraphs, we will explain applications of the most common laboratory assays used to evaluate IgE specificity and the information obtained in functional allergen-based tests.

#### **2.2. IgE and allergy**

The discovery of the reaginic activity in the IgE antibody by Ishizaka in 1967 [16] developed a revolution in the knowledge of allergy affecting not only in basic research but also in applied research resulting in innovative diagnostic tools. It is well known that patients with allergy have a tendency to produce high levels of IgE antibodies due to its atopic condition. Usually, the concentration of total IgE in serum from healthy individuals ranges below 1 µg/mL. It is worthy of note that this is a very low concentration of protein so many laboratories rather use IU/mL or kU/L instead of µg/mL to report IgE levels, but understanding that 1 kU/L equals to 2.4 ng/mL [17]. Total IgE does not correlate with clinical manifestations, and is preferably to measure specific IgE (sIgE) [18, 19]. Total IgE concentration is the addition of all the specific IgE (sIgE) to the different allergens the individual has been exposed to; in non-allergic subjects, sIgE levels are below the limits of detection (0.35 kU/L) [20]. Thus, to identify the triggering antigen of allergic manifestations, one of the most common laboratory test requirement is the determination of sIgE concentration in serum. The quantification of sIgE can be performed through several methods based on antigen-antibody reaction, e.g., radio allergo sorbent test (RAST), enzyme-linked immunosorbent assay (ELISA) and fluorescence enzyme immune assay (FEIA).


*3.2. Thermal Behaviour Analysis*

The TG-DTA analysis was used in this study to analyse the thermal behaviour of five types of geopolymers. Accordingly, it was observed that the addition of another aluminosilicate source influenced the metakaolin or coal ash-based geopolymers. By evaluating the heat flux and the mass evolution during the heating of the sample, the volatile compounds were eliminated, while the transition of different phases were observed. Moreover, by overlapping the TGA with the DTA curve, the mass loss or gain at specific temperatures could be correlated with the heat flux, to confirm the presence of a specific phase and its amount.

The samples analysed in this study showed multiple peaks on the DTA curves, which have been correlated with the water evaporation and oxidation processes. As can be seen in Figure 6, the samples without mine tailings exhibited similar behaviour, except for the samples CAMK which showed an endothermic peak around 220–260 ◦C. Moreover, the samples with mine tailings exhibited important oxidation reactions above 400 ◦C.

**Figure 6.** DTA plots of the studied geopolymers.

Moreover, as can be seen in Figure 6, the acid-activated geopolymers exhibit similar behaviour to the alkali-activated ones. Accordingly, the DTA curve of the coal ash-based geopolymer (CA–geo) shows multiple peaks, while the most significant one is the endothermic peak around 131 ◦C. This peak is correlated to the removal of the water molecules, which can exist as a free or chemical bond with the components from the geopolymers' structure. Therefore, the use of phosphoric acid as an activator will lead to the development of a porous structure with zeolitic channels, which keep the water at temperatures much higher than evaporation. Firstly, the hygroscopic water is removed until 120 ◦C, this exists in the structure of the geopolymers due to their hygroscopicity. Secondly, up to 300 ◦C, the physically strong bond molecules of water are removed as follows: (i) up to 200 ◦C the crystallization water bounded in the structure during the crystal formation from the aqueous solution is created by mixing the aluminosilicate source with the activator; (ii) during heating between the 180 and 300 ◦C temperature range, the molecules from intracrystalline type or network type hydrogels are removed. In approximately the same temperature range, zeolitic water will be removed from the channels. The behaviour is similar also for MK-geo and CAMK, the peak temperature being changed in accordance with the amount of water and the size of the sample. Moreover, the metakaolin-based sample showed the largest peak as it has a higher amount of gel pores and zeolitic channels compared with the coal ash-based ones. Accordingly, the minimum value of the first peak was moved to higher temperatures, close to 167 ◦C. The blended (mix of two aluminosilicate sources,

coal ash and metakaolin) geopolymer, CAMK sample, showed a broader peak into the water removal temperature range. This change could be correlated with the influence of the thermal behaviour on water removal at a low temperature from large pores specific to coal ash-based geopolymers overlapping next to water removal from small pores specific to metakaolin-based geopolymers.

By comparing the broadening of the first peak, it can be stated that the metakaolinbased geopolymers contain a higher quantity of water in small pores compared with those based on coal ash. These results are fully in line with the pore size distribution in geopolymers evaluated by NMR in previous studies, where the authors from [33] discovered a large amount of pores around 2.5 nm in metakaolin-based geopolymers, while in [34], the experiments on coal ash geopolymers showed that the first peak, on the relative pore size distribution, was positioned at higher relaxation time, i.e., the pores have a higher diameter.

As the samples are heated up above 300 ◦C, the chemically bound water will be removed. Accordingly, the fluctuations from the DTA curves correspond to the decomposition of acids, basics and neutral groups, which are formed between a metal and the OH groups. Considering the chemical composition of the raw materials, those can be Fe(III), Ti(IV), Si(IV), Na, K, P, Mg(II), Ca(II) and Al(III).

In the case of the blended geopolymer, the Na and Ca addition from the coal ash significantly affected the amount of water retained in Ca– or Al–silicate–hydrate channels and pores. This phenomenon can be due to the high concentration of Al brought into the system by the metakaolin. In other words, by mixing these two raw materials, the addition of these three elements (Na, Ca and Al) will have a significant impact on the condensation of C–A–S–H, C–S–H and N–A–S–H and, consequently, on the three-dimensional aluminosilicate network [35]. Accordingly, in the case of the CAMK sample, the water evaporation reaction from sodium–aluminosilicate hydrogel (N–A–S–H gel) is much more dominant in the hydrogels range (close to 185 ◦C), while the endothermic peak related to water removal from C–S–H and C–A–S–H structures can be observed around 240 ◦C. Moreover, in the case of acid-activated geopolymer, structures such as –Si–O–Al–O–P– gel will lose the water from the network in the same temperature range [10].

Above this temperature, no significant peak can be identified in the case of coal ashor metakaolin-based geopolymers. However, when mine tailings are involved in the mixture, the thermal behaviour is significantly changed. Consequently, the separation of the endothermic peaks in the range of water evaporation is much clearer. Accordingly, the MTCA curve shows two minimum points, the first being close to 125 ◦C, and the second being around 155 ◦C. However, in the case of the metakaolin-mine tailings blended geopolymer, the DTA curve shows only one peak in this temperature range, which has the minimum point at 180 ◦C. Therefore, the MK presence contributed to a zeolite-like structure formation. Another significant difference between the samples with and without mine tailings is the appearance of the endothermic peaks above 400 ◦C.

By comparing the DSC curves of the studied samples with those of alkali-activated geopolymers presented in [36], it can be observed that in the 20–300 ◦C temperature range, the thermal behaviour is almost the same. Both types of materials exhibit water evaporation from large pores and zeolitic channels, followed by its removal from hydrogels.

The first exothermic peak with the maximum point around 460 ◦C for the MTMK sample, and around 480 ◦C for the MKCA sample, is the conversion of magnetite to hematite [37]. Considering the provenience of the mine tailings (dams), in the same temperature range, the exothermic reactions can be associated with humic acid disintegration [38]. Moreover, by heating sulfide ores such as pyrite in air atmospheres, the following chemical reaction can occur:

$$\text{24FeS}\_2\text{(s)} + \text{11O}\_2\text{(g)} \rightarrow \text{2Fe}\_2\text{O}\_3\text{(s)} + \text{8SO}\_2\text{(g)}\tag{2}$$

The second exothermic peak appears due to the recrystallization of precipitated sulphate apatite at a lower temperature as a result of the transition of sulphate ions [39].

The weight loss during heating is related to the evaporation of water from the highly porous structure, which includes C–S–H and C–A–S–H formations that retain the activator in a liquid state even after multiple days of ageing. At higher temperatures, the weight loss is due to the decomposition of portlandite and other phases. Accordingly, the DTA curves exhibit endothermic peaks at corresponding temperatures, except for the samples with mine tailings addition which show high exothermic peaks in the range of 420–520 ◦C and 560–590 ◦C.

According to the TGA plots (Figure 7), up to 300 ◦C, the mass loss of the CA–geo sample was 12.7 wt.%, the mass loss of the CAMK sample was 16.2 wt.%, the mass loss of MK–geo was 18.7 wt.%, the mass loss of MTCA was 10.5 wt.% and the mass loss of MTMK was 14.4 wt.%. The mass change behaviour had the same trend as the first peak from the DTA curves, i.e., the samples with high water content showed a broader peak. Moreover, by correlating the type of raw material with the mass loss, it can be stated that the raw materials influence the water retention in the following order: metakaolin < coal ash < mine tailings. Therefore, this confirms that the samples with metakaolin have lower pores and channels which retain water at high temperatures.

**Figure 7.** TGA plots of the studied geopolymers.

In the 300–600 ◦C temperature range, the mass loss was very low for the CA–geo and MTCA samples which showed an extra decrease of 0.2 wt.%. However, in the same temperature range, the MTMK sample showed an extra mass loss of 2.1 wt.%, CAMK showed a loss of 2.3 wt.% and MK–geo showed a loss of 2.8 wt.%. Starting around 420 ◦C, with a maximum peak around 460 ◦C for the MTMK sample and 480 ◦C for MKCA, an exothermic reaction occurred. The weight gain corresponding to this oxidizing reaction was close to 0.5 wt.% for MTMK and 0 wt.% for MTCA. Accordingly, due to oxidation, only a small amount of oxygen remained in the sample, while other chemical elements evacuated.

#### **4. Conclusions**

This study examined the thermal behaviour and mineralogical composition of acidactivated geopolymers. Coal ash, metakaolin, mine tailings and different mixtures of these three aluminosilicate sources were used to synthesize silico-aluminophosphate geopolymers. Based on the experimental results reported, the following conclusions are drawn:

• In the 20–300 ◦C temperature range, the geopolymers obtained with H3PO4 acid exhibited similar thermal behaviour to those activated with a mix of NaOH and Na2SiO3,

**Author Contributions:** Conceptualization, writing original draft and investigation, D.D.B.N.; writing original draft, formal analysis, C.B.; project administration and scientific supervision, P.V.; data curation, validation and writing—reviewing and editing, A.V.S.; methodology, resources, formal analysis and investigation, D.P.B.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the publications grant of the Gheorghe Asachi Technical University of Ia¸si—TUIASI—Romania, project number GI/P4/2021.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The study did not report any data.

**Conflicts of Interest:** The authors declare no conflict of interest.