*Article* **Analysis of Oribatid Fauna of the East European Tundra with First Reported Data of Subpolar Urals**

### **Elena N. Melekhina**

Institute of Biology, Komi Science Center, Ural Branch of Russian Academy of Sciences (IB FRC Komi SC UB RAS), Kommunisticheskaya Str. 28, 167982 Syktyvkar, Russia<EMAIL_ADDRESS>
Received: 1 May 2020; Accepted: 6 June 2020; Published: 10 June 2020

**Abstract:** This study presents data on the oribatid mite fauna of the Subpolar Urals for the first time. Observations were made in the Lembekoyu River valley and 35 species of oribatid mites from 24 genera and 21 families were found. The analysis of taxonomic diversity and distribution of East European tundra oribatid mite species is presented based on available literature and the author's own research findings. The taxonomic list includes 163 species from 81 genera and 45 families. Ceratozetidae (15 species), Crotoniidae (14 species), Oppiidae (12 species), Suctobelbidae (12 species), Damaeidae (9 species), Brachychthoniidae (8 species), Phthiracaridae (5 species), Humerobatidae (5 species), Achipteriidae (5 species), Punctoribatidae (5 species), and Galumnidae (5 species) are the leading families, comprising more than 58% of all species. The zoogeographical structure of the fauna is dominated by widely distributed Holarctic, cosmopolitan, and semi-cosmopolitan species. The share of Palaearctic species is 23%. The specificity of the fauna of East European tundra manifests itself in the small group of Arctic species, both in the mainland tundra and on the Arctic islands. A complex of arctic-boreal species, widely distributed in the Eurasian sector of the Arctic, is distinguished.

**Keywords:** Arctic; Oribatida; faunistics; taxonomic diversity; distribution; checklist; arctic species; arctic-boreal species

### **1. Introduction**

Interest in Arctic invertebrates has greatly increased in recent decades [1–14]. Coulson et al. [11] summarises data on the taxonomic composition of invertebrates from three archipelagos in the Barents Sea: Spitsbergen, Franz Josef Land, and Novaya Zemlya. One of the directions of modern research is to learn how invertebrates spread and what contributes to their faunistic diversity on the islands and archipelagos in the Arctic [15–18]. Makarova et al. [19] focused on 'eastern elements' in the invertebrate fauna of East European tundra, the so-called 'Siberian' species. An integrated assessment of biological diversity, including soil invertebrates, has been carried out in the large Arctic region, Nenets Autonomous District [20].

Attention is being paid to the study of taxonomic diversity of invertebrates, including oribatid mites, in anthropogenic altered soils in the Arctic and Sub-Arctic conditions and the application of this group of animals in bioindication [13,21–23]. Inventory of modern biodiversity and study of geographic trends of soil fauna diversity is important for biodiagnostics of natural communities under conditions of anthropogenic impact and forecasting changes in these communities in the long-term. In addition, the study of modern biodiversity is important for assessing the changes that take place in response to global warming.

In the European North-East, studies on oribatid mite fauna have been conducted both in the mainland tundra (plain and mountain tundra) and on the Arctic islands. The first data on Vaigach Island oribatid mites were presented in the form of a monograph by Koch [24], who processed the data collected by Adolf Nordenskiöld during his Arctic expedition of 1875. Koch named eight species of oribatids found on the Vaigach Island, four of which he described as new to science. Subsequently, Trägårdh [25] identified three species from Nordenskiöld's collection, which were already named by Koch. Krivolutsky et al. [26] provided a list of oribatid species in the Russian Arctic sector known at the time of publication, which included data on the Vaigach and Kolguev Islands. Melekhina et al. [27] studied oribatid fauna in the nests of Lapland Bunting *Calcarius lapponicus* and reported nine species, nine genera, and six families of oribatids new to Vaigach Island. In total, 25 species of oribatid mites from 24 genera and 21 families were found in this study. The authors compiled a list of Vaigach oribatid mites (43 species, 34 genera, 25 families) and analysed the zoogeographic structure of the fauna and species distribution [27].

Melekhina and Zinovyeva [28] collected samples in the north-western part of the Yugor Peninsula, on the Pai-Khoi Ridge. Oribatida from seven types of habitats of mainland tundra and mountain tundra, as well as one intrazonal plant community (motley grass meadow on the bank of the stream) were examined.

Data on the Bolshezemelskaya tundra have been collected mainly from around Vorkuta [29–31]. Melekhina and Krivolutsky [30] presented a list of oribatids of the Bolshezemelskaya tundra (33 species, 24 genera, 20 families). Peculiarities of the vertical distribution of oribatid mites in moss-lichen tundra with permafrost soil and in the tundra of dwarf birch with non-frost soil have also been reported [31]. Zubrii et al. [32] studied the plant communities near the thermal springs of Pym-Va-Shor located in the Bolshezemelskaya tundra in the Polar Pre-Urals. Samples from melted soil plots and watered mossy turfs were collected during the winter.

Research was also carried out in the Polar Urals in the area of Labytnangi [33], on the Rai-Iz Ridge [34]. Melekhina [35,36] obtained data in the vicinity of Lake Paga-Ty. Six types of mountain tundra habitats were surveyed: shrub-moss-lichen tundra, shrub-moss tundra, shrub-green-moss birch, grassy willow, horsetail moss willow, and motley grass in the hollow of the drain.

Sidorchuk [37] investigated the distribution of oribatid mites along with the vertical profile of the Malyy Paypudynskiy Ridge, from the floodplain to the belt of stony tundra. Samples were collected from the lower part of the Malaya Paypudyna River valley (settlement Polyarny, Labytnang district, YNAD), on the slope of the north-eastern exposure. Sidorchuk [37] found 82 species of oribatid mites in four types of plant communities, of which 46 were recorded for the first time in the region. The article provides a generalised list of oribatid mites of the Polar Urals (106 species, 61 genera, 34 families) taking into account all published data.

Melekhina conducted research at the tundra landscapes of the complex reserve 'Khrebtovy', located on the south-eastern slope of the Yenganepe Ridge (Polar Urals) [38]. Oribatida from seven types of habitats were examined: birch forest, larch sparse woodland, shrubby moss-lichen tundra, large bog, rocky outcrops, shrubby lichen tundra, and meadow complex.

Data on the mountain tundra oribatid fauna of the Northern Urals are also available. Melekhina [39] surveyed four types of plant communities (spruce, boggy grass-marsh, shrubby lichen, and stony lichen tundra) on the Yany-Pupu-Nyor mountain in the Pechora-Ilychsky Reserve. Melekhina [40] summarised her own research findings and those available in the literature on the taxonomic diversity of oribatids in the European North of Russia (in the taiga and tundra zones) and analysed the dynamics of diversity along the latitudinal gradient. In the Subpolar Urals, no research has been conducted on the oribatid mite fauna so far.

The purpose of this paper is to summarise the findings of the available literature and those of the author's own new research on the taxonomic diversity and distribution of oribatid mites in the East European tundra and to identify specific features of this fauna.

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

Observations were made in the Lembekoyu River valley (65◦1604600 N, 60◦4 05100 E), in zonal (lichen-moss tundra, moss-marsh tundra) and intrazonal (stream floodplain) communities. Sampling was conducted in five plant communities: lichen-moss tundra 1, lichen-moss tundra 2, moss and dwarf birch tundra 1, moss and dwarf birch tundra 2 and grassy community in a creek valley. Field material was collected according to generally accepted methods [41]. Ten soil samples, each with dimensions 10 × 10 cm by 10 cm deep, were taken from all sites in June 2018. A total of 50 soil samples were collected. The soil samples were transported to the Institute of Biology (IB Komi SC UB RAS), Syktyvkar, and placed into Tullgren soil extractors. The microarthropod fauna was extracted under 40 Watt bulbs into 96% alcohol for seven-ten days until the soil was completely dry. The Oribatida were identified to species by morphological taxonomic characters [42]. A total of 2500 specimens of adult oribatid mites were identified up to the species level.

In this manuscript, the author summarises the results of her own research conducted earlier in the tundra zone of the European North-East and presented in publications [22,27–32,35,36,38,39], new data obtained in the Subpolar Urals, as well as all available literature information (Table 1, Figure A1). Taxonomies of oribatid mites and types of global distribution of the species follow Subías [43]. For the analysis of the geographical distribution of species, literary sources were used [1,7,11,30,44–50] and others. The term local fauna was used as understood by A.G. Tatarinov [51].

**Table 1.** Number of taxa of oribatid mites in the local fauna of East European tundra.

Synonyms follow Subías [43]. Synonyms of species were given when the author of a publication mentioned the species using a different name. For example, the species *Eupelops plicatus* (Koch, 1835) (=*Pelops auritus* Koch, 1839) was noted in Bolshezemelskaya tundra [30] and was named *Eupelops auritus* Koch, 1839. In some cases, a synonym was given if the authors of several publications cited synonyms.

### **3. Results and Discussion**

### *3.1. Taxonomic Diversity*

### 3.1.1. Subpolar Urals

This is the first report of oribatid mite fauna of the Subpolar Urals. In total, 35 species, 24 genera, and 21 families of oribatid mites were found (Table 2, Appendix B). The largest number of species was recorded in the families Crotoniidae, Oppiidae and Suctobelbidae. In the studied five plant communities, 11 to 18 species were found. *Heminothrus (H.) longisetosus, Nanhermannia (N.) sellnicki, Tectocepheus velatus* and *Melanozetes sellnicki* were common in different types of communities. The species *Malaconothrus (M.) monodactylus, Heminothrus (P.) peltifer, Hypochthonius rufulus, Atropacarus (A.) striculus* and *Neoribates (N.) aurantiacus* were recorded only in the floodplain of the stream. The first two species are known to prefer high humidity habitats.

**Table 2.** The species composition of oribatid mites in the plant communities of the Subpolar Urals.

Notes. L-m 1 lichen-moss tundra 1; L-m 2 lichen-moss tundra 2; M-d 1 moss and dwarf birch tundra 1; M-d 2 moss and dwarf birch tundra 2; Gras. com. grassy community in a creek valley; + and – The presence or absence of a species.

### 3.1.2. Total Number of Taxa at Different Levels

In total, 163 species of oribatid mites from 81 genera and 45 families were found in East European tundra (Table 1, Appendix B). Oribatid species found in the East European tundra are grouped into 23 superfamilies, with the majority of the species coming under six superfamilies. The leading superfamily is Ceratozetoidea, with 28 species from four families, followed by Crotonioidea (22 species, four families), Oppioidea (16 species, four families), Trizetoidea (12 species, one family), Oripodoidea (12 species, six families), and Damaeoidea (10 species, two families) (Figure 1). Eight species represent the superfamilies Brachychthonioidea (one family), Gustavioidea (three families), and Achipterioidea (two families). A similar distribution of species among superfamilies has been observed, for example, in Svalbard [7]. *Diversity* **2020**, *12*, x FOR PEER REVIEW 5 of 19 *Diversity* **2020**, *12*, x FOR PEER REVIEW 5 of 19

**Figure 1.** Distribution of species richness among different superfamilies of oribatid mites in the East European tundra (taxa are in a systematic order). **Figure 1.** Distribution of species richness among different superfamilies of oribatid mites in the East European tundra (taxa are in a systematic order). **Figure 1.** Distribution of species richness among different superfamilies of oribatid mites in the East European tundra (taxa are in a systematic order).

Leading families in the mite fauna are: Ceratozetidae (15 species), Crotoniidae (14 species), Oppiidae (12 species), Suctobelbidae (12 species), Damaeidae (nine species), Brachychthoniidae (eight species), Phthiracaridae (five species), Humerobatidae (five species), Achipteriidae (five species), Punctoribatidae (five species), and Galumnidae (five species). These 11 families comprise 58.6% of all species (95 species). Most families are represented by a small number of species (one to three) (Figure 2). Leading families in the mite fauna are: Ceratozetidae (15 species), Crotoniidae (14 species), Oppiidae (12 species), Suctobelbidae (12 species), Damaeidae (nine species), Brachychthoniidae (eight species), Phthiracaridae (five species), Humerobatidae (five species), Achipteriidae (five species), Punctoribatidae (five species), and Galumnidae (five species). These 11 families comprise 58.6% of all species (95 species). Most families are represented by a small number of species (one to three) (Figure 2). Leading families in the mite fauna are: Ceratozetidae (15 species), Crotoniidae (14 species), Oppiidae (12 species), Suctobelbidae (12 species), Damaeidae (nine species), Brachychthoniidae (eight species), Phthiracaridae (five species), Humerobatidae (five species), Achipteriidae (five species), Punctoribatidae (five species), and Galumnidae (five species). These 11 families comprise 58.6% of all species (95 species). Most families are represented by a small number of species (one to three) (Figure 2).

BrachychthoniidaeHaplozetidaeParakalummidaeGalumnidaeFamilies **Figure 2.** Distribution of species richness among different families of oribatid mites in the East European tundra (taxa are in a systematic order). **Figure 2.** Distribution of species richness among different families of oribatid mites in the East European tundra (taxa are in a systematic order).

It is possible to distinguish species characteristics of different families for the East European tundra. From Crotoniidae, *Heminothrus (Platynothrus) punctatus* and *Camisia (C.) horrida* are

3.1.3. Characteristic Families Typical of Tundra Zone Species

3.1.3. Characteristic Families Typical of Tundra Zone Species

### 3.1.3. Characteristic Families Typical of Tundra Zone Species

It is possible to distinguish species characteristics of different families for the East European tundra. From Crotoniidae, *Heminothrus (Platynothrus) punctatus* and *Camisia (C.) horrida* are consistently found in the local tundra fauna. Both species are often found in the Eurasian sector of the Arctic [30]. *Camisia (C.) biverrucata*, *C. (C.) invenusta*, and *C. (Ensicamisia) lapponica*, are associated with high latitude. *Hermannia reticulata* and *H. scabra* Hermanniidae and *Ceratoppia bipilis* and *C. sphaerica* Ceratoppiidae are also characteristic of high latitudes. In the tundra zone, the holarctic species *Nothrus borussicus* Nothridae was often observed.

From Carabodidae, the most common species in the tundra are the circumpolar *Carabodes labyrinthicus* and the Palearctic *C. subarcticus* and *C. marginatus*. For high latitudes, there are Holarctic species *Oppiella (M.) neerlandica, O. (O.) splendens,* and *Moritzoppia unicarinata unicarinata* Oppiidae, and *Suctobelbella acutidens acutidens* Suctobelbidae. From Ceratozetidae, *Edwardzetes edwardsi* and *Ceratozetella sellnicki* are common at high latitudes. *Diapterobates notatus* and *Svalbardia paludicola* Humerobatidae are widely distributed in the Eurasian sector of the Arctic; they are found on the Arctic islands and archipelagos [11–13,17]. The first of these species has been noted as the most abundant in various plant communities in Svalbard [11–13].

This complex of species is characteristic of local fauna of the East European tundra, as well as of the tundra zone of Eurasia as a whole. The Holarctic species *Oribatula (O.) tibialis* and *O. (Z.) exilis*, the cosmopolitan *Tectocepheus velatus* and *Oppiella nova*, and the semi-cosmopolitan *Quadroppia quadricarinata* and *Scheloribates laevigatus laevigatus* also are widely distributed in the Arctic.

For 11 species in the list, Subías [43] indicates boreo-Alpine distribution. These are Holarctic species *C. (E.) lapponica, C. (C.) borealis, H. (P.) peltifer, H. (P.) humicola, E. edwardsi, Melanozetes mollicomus, Moritzoppia unicarinata clavigera, D. notatus, S. paludicola, Mycobates (Calyptozetes) sarekensis*, and *Oromurcia lucens*. For the Holarctic species *C. sphaerica, Melanozetes sellnicki, Ameronothrus lineatus, A. nigrofemoratus, Diapterobates variabilis*, and *Sphaerozetes arcticus*, boreal distribution is indicated. In conclusion, all these species are typical of the high latitudes of Eurasia.

### *3.2. Types of Longitudinal Distribution*

In this section, the Holarctic, Palearctic, cosmopolitan and semi-cosmopolitan species of oribatid fauna are distinguished by their longitudinal distribution. Holarctic species predominate the fauna (Figure 3). For comparison, in Spitsbergen, Holarctic species make up 50% of all species [7,11]. It has previously been noted that the European sector of the Arctic is characterised by an increase in the proportion of Holarctic oribatid species in the latitudinal gradient, from the taiga to mainland tundra to the Arctic islands and archipelagos [40]. Thus, in the taiga zone of the European North-East, the share of Holarctic species (41.5%) is the least [40].

The share of Palearctic species (34 species) in the local fauna was much smaller (23.4%) than that of Holarctic species. For comparison, in the taiga zone of the European North-East, Palearctic species make up 37.5% of the faunal list [44]. The highest share of Palearctic species is observed in the fauna of the Polar Urals and Bolshezemelskaya tundra (Figure 3).

The composition of the Palearctic species was specific to each local fauna. In total, five Palearctic species from Damaeidae were recorded, four of which were found only in the Polar Urals. Only one species, *Damaeus (E.) bituberculatus*, was distributed in three local faunas: in the Bolshezemelskaya tundra, the Polar Urals and the Northern Urals. This species is widely distributed in the taiga zone of the European North [44].

Some Palearctic species were noted only in the Polar Urals: *Liacarus (D.) neonominatus* (Liacaridae), *Eueremaeus oblongus silvestris* (Eremaeidae), *Exochocepheus laticuspis* (Scutoverticidae), *Bipassalozetes (B.) intermedius* (Passalozetidae), and *Suctobelbella (S.) subcornigera vera* (Suctobelbidae). In turn, other palearctic species, *Scutovertex neonominatus* (Scutoverticidae), *Berniniella (B.) bicarinata* and *Lauroppia falcata* (Oppiidae), and *Suctobelbella (S.) singularis* (Suctobelbidae), were recorded only in the Bolshezemelskaya tundra.

*3.2.* Types of L*ongitudinal Distribution*

consistently found in the local tundra fauna. Both species are often found in the Eurasian sector of the Arctic [30]. *Camisia (C.) biverrucata*, *C. (C.) invenusta*, and *C. (Ensicamisia) lapponica*, are associated with high latitude. *Hermannia reticulata* and *H. scabra* Hermanniidae and *Ceratoppia bipilis* and *C. sphaerica* Ceratoppiidae are also characteristic of high latitudes. In the tundra zone, the holarctic

From Carabodidae, the most common species in the tundra are the circumpolar *Carabodes labyrinthicus* and the Palearctic *C. subarcticus* and *C. marginatus*. For high latitudes, there are Holarctic species *Oppiella (M.) neerlandica, O. (O.) splendens,* and *Moritzoppia unicarinata unicarinata* Oppiidae, and *Suctobelbella acutidens acutidens* Suctobelbidae. From Ceratozetidae, *Edwardzetes edwardsi* and *Ceratozetella sellnicki* are common at high latitudes. *Diapterobates notatus* and *Svalbardia paludicola*  Humerobatidae are widely distributed in the Eurasian sector of the Arctic; they are found on the Arctic islands and archipelagos [11–13,17]. The first of these species has been noted as the most

This complex of species is characteristic of local fauna of the East European tundra, as well as of the tundra zone of Eurasia as a whole. The Holarctic species *Oribatula (O.) tibialis* and *O. (Z.) exilis*, the cosmopolitan *Tectocepheus velatus* and *Oppiella nova*, and the semi-cosmopolitan *Quadroppia*

For 11 species in the list, Subías [43] indicates boreo-Alpine distribution. These are Holarctic species *C. (E.) lapponica, C. (C.) borealis, H. (P.) peltifer, H. (P.) humicola, E. edwardsi, Melanozetes mollicomus, Moritzoppia unicarinata clavigera, D. notatus, S. paludicola, Mycobates (Calyptozetes) sarekensis,* and *Oromurcia lucens*. For the Holarctic species *C. sphaerica, Melanozetes sellnicki, Ameronothrus lineatus, A. nigrofemoratus, Diapterobates variabilis,* and *Sphaerozetes arcticus*, boreal distribution is indicated. In conclusion, all these species are typical of the high latitudes of Eurasia.

In this section, the Holarctic, Palearctic, cosmopolitan and semi-cosmopolitan species of oribatid fauna are distinguished by their longitudinal distribution. Holarctic species predominate the fauna (Figure 3). For comparison, in Spitsbergen, Holarctic species make up 50% of all species [7,11]. It has previously been noted that the European sector of the Arctic is characterised by an increase in the proportion of Holarctic oribatid species in the latitudinal gradient, from the taiga to mainland tundra

*quadricarinata* and *Scheloribates laevigatus laevigatus* also are widely distributed in the Arctic.

species *Nothrus borussicus* Nothridae was often observed.

abundant in various plant communities in Svalbard [11–13].

**Figure 3.** Relative species richness according to the longitudinal distribution of oribatid mites in the local fauna of East European tundra. Kolguev I. – Kolguev Island, Vaygach I. – Vaygach Island, Yugor **Figure 3.** Relative species richness according to the longitudinal distribution of oribatid mites in the local fauna of East European tundra. Kolguev I.—Kolguev Island, Vaygach I.—Vaygach Island, Yugor Penin.—Yugor Peninsula, BZ tundra—Bolshezemelskaya tundra, P Urals—Polar Urals, SP Urals—Subpolar Urals, N Urals—North Urals.

Only a few Palearctic species were common to several local faunas. In addition to *D. (E.) bituberculatus* mentioned above, the species *Carabodes (C.) marginatus* was recorded in four mainland faunas: Polar Urals, Subpolar Urals, Yugor Peninsula, and Bolshezemelskaya tundra. The generalisate Palaearctic species for two faunas: the Yugor Peninsula and the Vaigach Island was *Minunthozetes (M.) pseudofusiger.*

The share of cosmopolitan and semi-cosmopolitan species in the total fauna structure is 14.5%. In local faunas, they account for 13.9% to 18.2%. This is much higher than their share in the taiga zone, where they account for 9.5% of the total species [40].

It can thus be concluded that the findings of previous research [40] regarding the decrease in the proportion of Palearctic species and the increase in the proportion of Holarctic, cosmopolitan, and semi-cosmopolitan species in the oribatid fauna along the latitudinal gradient (from the taiga to mainland tundra to the Arctic islands and archipelagos) holds true.

A substantial number of species (20 species, 62.5%) are distributed circumpolar; they occur in high latitudes of both the Palearctic and Nearctic regions- in Alaska, Yukon, and Greenland [10,47,49]. These include *Liochthonius lapponicus*, *N. borussicus, Camisia horrida, C. biurus*, *H. (P.) punctatus*, *H. reticulata, C. bipilis, C. sphaerica, Moritzoppia neerlandica, M. unicarinata clavigera, S. acutidens acutidens*, *S. hammeri, O. tibialis*, *M. mollicomus, D. notatus, S. paludicola,* as well as cosmopolitan species *T. velatus*, *O. nova, C. gracilis* and semi-cosmopolitan species *Q. quadricarinata*.

### *3.3. Types of Latitudinal Distribution*

### 3.3.1. Arctic Species

In the composition of the oribatid fauna of the East European tundra, species with arctic, arctic-boreal, temperate, and polyzonal types of latitudinal distribution have been documented. For the East European tundra, as well as for the mainland tundra of the European part of Russia as a whole, only a single Arctic oribatid species is known until now – *S. paludicola.* In addition to the Eastern European sector, *S. paludicola* was found in the Kola Peninsula [50]. In the Arctic archipelagos of the Palearctic region, it was found in Spitsbergen [7,11] and Novaya Zemlya [1,11]; in northern Siberia, in Yamal and Taimyr [34,45]; and in the northern Far East, in Chukotka [48]. The species has a circumpolar distribution and is found in Greenland [49], Alaska, and Yukon [47]. The data obtained confirm earlier

conclusion about the small number of Arctic species in the Eastern European sector of the Arctic, both in the island and mainland regions [28,40].

The oribatid species *Sphaerozetes arcticus* Hammer, 1952, noted in the Polar Urals [35], should be referred to as an arctic-boreal species. In the European part of Russia, this species is rarely found. In addition to the Polar Urals, the species was recorded in the taiga zone, in the Arkhangelsk region [44]. In northern Siberia, it is more widespread, with its distribution covering the arctic-boreal zone, but most findings are in the tundra zone [45]. The species is found in Chukotka. Thus, it can be concluded that *S. arcticus* is characteristic of high latitudes. Subías [43] indicates a boreal distribution of this species.

The number of Arctic species is increasing on remote islands and archipelagos of the European Arctic sector. For example, on the Svalbard archipelago, where 81 species of oribatid mites are known [7,11], six Arctic species have been recorded. These are: *Autogneta (A.) kaisilai* Karppinen, 1967, *Sphaerozetes setiger* (Trägårdh, 1910), *Iugoribates gracilis* Sellnick, 1944, *Svalbardia paludicola, Ceratozetes (C.) spitsbergensis* Thor, 1934, and *Oribatella (O.) arctica arctica* Thor, 1930. The first of these species is noted only on Svalbard [43] and can be called conditional endemic to Svalbard.

For comparison, two Arctic species, *S. paludicola* and *Oribatella (O.) arctica arctica,* have been registered in the fauna of Novaya Zemlya. *Oribatella (O.) arctica arctica,* was observed in northern Siberia [34] and Chukotka [45], in addition to Spitsbergen. Therefore, it cannot be ruled out that this species may be found in the East European tundra in the future.

### 3.3.2. Arctic-Boreal Species

The tundra oribatid fauna includes a complex of arctic-boreal species occupying the Arctic islands and archipelagos, the continental part of the tundra zone, and the taiga zone of Eurasia. The base of this complex is represented by species that constitute the majority of tundra fauna, both in the European sector of the Arctic (mainland part and island part: Vaigach Island, Novaya Zemlya, and Spitsbergen), and in Siberia and the Far East, and, accordingly, contribute to the higher percentage of tundra fauna similarity in Eurasia. These species are *H. punctatus, C. sphaerica, H. reticulata,* and *D. notatus,* with a circumpolar distribution. On Vaigach Island, this list further includes *Ameronothrus lineatus, A. nigrofemoratus,* and *Oromurcia lucens*. Species *A. lineatus* in the European sector of the Arctic is also distributed in Spitsbergen and Kola tundra. Like *A. nigrofemoratus*, it is also a boreal species, while *Oromurcia lucens* (L. Koch, 1879), also found on Spitsbergen, is a boreal-alpine species, according to Subías [43].

In the biocoenoses of the Pai-Khoi Ridge (Yugor peninsula)*,* the complex of arctic-boreal species is complemented by *Moritzoppia unicarinata clavigera, Pyroppia lanceolata,* and *Banksinoma setosa*. For the first of these species, Subías [43] indicates a boreal-alpine distribution. The species *B. setosa,* in addition to the Yugor peninsula, is noted in the North Urals. It is mainly located in the Siberian and Far Eastern sectors. The share of arctic-boreal species in different local faunas is not high, making up 7.7% to 18.6% of the total fauna. In the overall structure of the fauna, their contribution is 7.4%.

Some arctic-boreal species are also present in the Palaearctic mountain ranges, such as *D. notatus* in the Altai [46], *C. sphaerica* in the Tien Shan [46], and *M. unicarinata clavigera* and *P. lanceolata* in the Caucasus [41]. Arctic-boreal species *H. punctatus, D. notatus, Hermannia scabra, H. reticulata, A. lineatus,* and *C. sphaerica* are also found in the Western European tundra sector (Kola Peninsula) [50]. It can be thus concluded that the arctic-boreal species which are widely distributed along the longitudinal gradient of the European sector of the Arctic are all common. No arctic-boreal species specific to the Eastern European sector have been identified. In the European North-East, the complex of arctic-boreal species also includes *M. unicarinata clavigera, P. lanceolata, B. setosa, Sphaerozetes arcticus,* and *Peloribates pilosus.*

### 3.3.3. Temperate and Polyzonal Species

The largest number of species recorded in the East European tundra have a temperate or polyzonal type of latitudinal distribution. Temperate species typical of East European tundra are *Liochthonius* *lapponicus, N. borussicus, O. (M.) neerlandica, C. labyrinthicus, C. marginatus. C. subarcticus, E. edwardsi,* and *M. mollicomus*.

Polyzonal species *C. horrida, C. biurus, N. borussicus, S. acutidens acutidens, S. acutidens duplex, C. bipilis, O. (O.) tibialis,* and *O. (Z.) exilis* were often found in local faunas of the tundra zone. Cosmopolitan species *T. velatus* and *O. nova* and semi-cosmopolitan species *Q. quadricarinata* and *S. laevigatus*, which in the latitudinal aspect are also distributed polyzonal [52], are common in the tundra. These polyzonal and temperate species that are common in the tundra zone, as well as the Arctic and arctic-boreal species, mentioned above, we call 'species of northern complex'.

### 3.3.4. 'Southern' Elements within the Oribatid Fauna

Attention is also drawn to species which are mainly found in the lower latitudes. These species can be called 'conditionally southern'. For example, *Hydrozetes thienemanni*, with a temperate type of distribution, was found only at Yugor Peninsula in the European sector of the tundra zone [28]. In the European part of Russia, it is distributed in taiga and coniferous/broad-leaved forests [41,44,52]. In the tundra zone of Eurasia, *H. thienemanni* was previously found only in Chukotka by Grishina [34].

Holarctic species *Malaconothrus (Trimalaconothrus) tardus*, according to Subías [43], is absent in the northern Palaearctic region. It is found in the Polar Urals [35]. In the European part of Russia, it is found in the northernmost regions. This species was observed mainly in broad-leaved forests and steppe zone [41,44].

Palaearctic species *Eueremaeus oblongus silvestris* found in the Polar Urals [37] is not characteristic of the tundra zone. In the European part of Russia, it is mainly found in the zone of broad-leaved and coniferous/broad-leaved forests [41,52]. In Siberia, the species was observed in taiga forests and the Altai [45].

### **4. Summary**

This publication presents a generalised taxonomic list of oribatid mites of East European tundra, based on available literature and new data. The checklist of East European tundra oribatid mites includes 163 species, 81 genera, and 45 families. This study presents data on the oribatid mite fauna of the Subpolar Urals for the first time. To date, 35 species, 24 genera, and 21 families of oribatids have been registered from this region.

The leading families in the fauna structure are Crotoniidae, Ceratozetidae, Oppiidae, Suctobelbidae, Damaeidae, Brachychthoniidae, Phthiracaridae, Humerobatidae, Achipteriidae, Punctoribatidae, and Galumnidae. The greatest number of species is Holarctic. Circumpolar distributed species make 12.3% of the total. The share of Palaearctic species is low (23.4%), which distinguishes the fauna of the tundra zone from the taiga zone.

The specificity of the oribatid fauna of East European tundra manifests itself in the small group of Arctic species, both in the mainland tundra and on the Arctic islands. In the majority of local fauna, there is only one Arctic species, *Svalbardia paludicola*. *Sphaerozetes arcticus*, noted in the Polar Urals, that has been classified as an arctic-boreal species. It could also be called 'conditionally arctic', as it sometimes penetrates into the taiga zone.

The fauna of the East European tundra is characterised by a complex of arctic-boreal species, based on circumpolar species common with the Western European sector of the Arctic, as well as with the Siberian and Far Eastern sectors, such as *Heminothrus punctatus, Ceratoppia sphaerica, Hermannia reticulata,* and *Diapterobates notatus.* In different local faunas, this complex is complemented by species *Ameronothrus lineatus, A. nigrofemoratus, Banksinoma setosa, Pyroppia lanceolata, Moritzoppia unicarinata clavigera, Peloribates pilosus,* and *Oromurcia lucens*.

The largest number of species in the East European tundra is polyzonal. No species specific to the East European sector of the tundra zone was identified.

**Funding:** This work was supported by the RFBR No. 18-29-05028 mk and State Assignment AAAA-A17 -117112850235-2.

**Acknowledgments:** The author thanks Leonid N. Rybin from the Institute of Biology, Komi Science Center, Ural Branch of RAS, Syktyvkar (Russia) for map.

*Diversity* **2020**, *12*, x FOR PEER REVIEW 10 of 19

**Conflicts of Interest:** The author declares no conflict of interest.

### **Appendix A**

**Figure A1.** Localities within the region under consideration (see Table 1 for explanations). 1 – Kolguev Island (Krivolutsky et al., 2003), 2 – Vaigach Island, Yugorskiy Shar (Koch, 1879), 3 – Vaigach Island, Cape Greben' (Koch, 1879), 4 – Vaigach Island (Trägårdh, 1904), 5 and 6 – Vaigach Island (Krivolutsky et al., 2003), 7 – Vaigach Island, Bolvansky mountain (Melekhina et al., 2019), 8 – Vaigach Island, Old Polar Station (Melekhina et al., 2019), 9 – Yugor Peninsula, Pai-Khoi Ridge (Melekhina and Zinovyeva, 2012), 10 – Bolshezemelskaya tundra, Vorgashor village (Melekhina, 1997), 11 – Bolshezemelskaya **Figure A1.** Localities within the region under consideration (see Table 1 for explanations). 1—Kolguev Island (Krivolutsky et al., 2003), 2—Vaigach Island, Yugorskiy Shar (Koch, 1879), 3—Vaigach Island, Cape Greben' (Koch, 1879), 4—Vaigach Island (Trägårdh, 1904), 5 and 6—Vaigach Island (Krivolutsky et al., 2003), 7—Vaigach Island, Bolvansky mountain (Melekhina et al., 2019), 8—Vaigach Island, Old Polar Station (Melekhina et al., 2019), 9—Yugor Peninsula, Pai-Khoi Ridge (Melekhina and Zinovyeva, 2012), 10—Bolshezemelskaya tundra, Vorgashor village (Melekhina, 1997), 11—Bolshezemelskaya tundra, Vorkuta (Melekhina and Krivolutsky, 1999), 12—Bolshezemelskaya tundra, Vorkuta (Goryachkin et al., 2011), 13—Bolshezemelskaya tundra, Pym-Va-Shor (Zubrii et al., 2012), 14—Polar Urals, complex reserve 'Khrebtovy', Yenganepe Ridge (The biological . . . , 2010), 15—Polar Urals, Polyarny village (Sidorchuk, 2009), 16—Polar Urals, Rai-Iz Ridge (Grishina, 1985), 17—Polar Urals, Labytnangi village (Karpova et al., 1987), 18—Polar Urals, Paga-Ty Lake (Biodiversity . . . , 2007; Melekhina, 2008), 19—Subpolar Urals, Lembekoyu River (Melekhina and Selivanova, unpublished), 20—Northern Urals, Yany-Pupu-Nyor mountain (Melekhina, 2005).


**3. Characteristics of MGs with respect to their use in EV application**

Two of the previously presented MG variants will be investigated here: the first one is an integrated motor-gear variant having an in-wheel configuration for the electric motor, and the second MG will be a high-speed variant with buried permanent magnets. Evaluation of the performances of these two topologies in terms of iron loss and torque (or output power) will be employed numerically, by means of finite element method (FEM) and the Flux2D software.

#### **3.1. Integrated in-wheel motor magnetic gear topology**

The considered in-wheel motor gear topology is the one presented in **Figure 8**. The motor is a fractional slot permanent magnet synchronous machine (FS-PMSM)—17 pairs of poles and 39 slots. Above the outer rotor of the FS-PMSM and attached to it, we have an inner rotor of the MG, having the same number of pairs of poles (*pin*=17), and a reduced number of pairs of poles for the outer rotor of the MG (*pout*=5). It means that the magnetic transmission will produce a higher speed, at a lower torque: it is a multiplier, or reversed gear [40]. Numerically computed results are shown in **Figures 10**–**12**.

**Figure 10.** Integrated motor-gear analyzed variant, based on in-wheel motor: field lines (left) and flux density distribu‐ tion (right) within the active parts.

State of the Art of Magnetic Gears, their Design, and Characteristics with Respect to EV Application 1385 http://dx.doi.org/10.5772/64174

**Figure 11.** Air-gap flux density in the integrated motor-gear studied variant [40].

*2.3.3. Analytical design of MGs through vector potential algorithm*

8412 Modeling and Simulation for Electric Vehicle Applications

previous references; thus, the reader can investigate this topic if needed.)

the influence of certain materials used in the construction of MGs.

**3.1. Integrated in-wheel motor magnetic gear topology**

results are shown in **Figures 10**–**12**.

tion (right) within the active parts.

The vector potential expressions, in both air gaps of the MG, have been detailed for radial and axial flux topologies in [28, 41]. Since the mathematical modeling for such an approach is quite laborious and goes beyond the scope of this state-of-the-art presentation of MGs, it will not be presented here. (It's worth mentioning that even the integral coefficients are detailed in the

With this, we have concluded a short presentation on the available analytical models used in the design of process of MGs. Next, we will consider two of the available configurations to evaluate their performances (in terms of iron loss and torque) and we will rapidly investigate

**3. Characteristics of MGs with respect to their use in EV application**

Two of the previously presented MG variants will be investigated here: the first one is an integrated motor-gear variant having an in-wheel configuration for the electric motor, and the second MG will be a high-speed variant with buried permanent magnets. Evaluation of the performances of these two topologies in terms of iron loss and torque (or output power) will be employed numerically, by means of finite element method (FEM) and the Flux2D software.

The considered in-wheel motor gear topology is the one presented in **Figure 8**. The motor is a fractional slot permanent magnet synchronous machine (FS-PMSM)—17 pairs of poles and 39 slots. Above the outer rotor of the FS-PMSM and attached to it, we have an inner rotor of the MG, having the same number of pairs of poles (*pin*=17), and a reduced number of pairs of poles for the outer rotor of the MG (*pout*=5). It means that the magnetic transmission will produce a higher speed, at a lower torque: it is a multiplier, or reversed gear [40]. Numerically computed

**Figure 10.** Integrated motor-gear analyzed variant, based on in-wheel motor: field lines (left) and flux density distribu‐

**Figure 12.** Performances of the integrated motor-gear studied variant: the speed-torque-power on the low speed rotor of the MG (a), for the high-speed outer rotor (b), and the iron loss in the active parts.

A first result of this analyzed integrated motor-gear variant is shown in **Figure 10**, where the field lines and flux density distribution within the active parts of the topology are presented. The reader can observe the influence of modulated teeth in the transfer of the flux (torque) from the inner to outer rotor. In the air gap layers of this machine-gear topology, the FEM software computed the following air gap flux densities, plotted in **Figure 11**: here, Gap1 refers to the in-wheel motor's air gap, Gap2 stands for the inner rotor air gap of the MG and Gap3 refers to its outer rotor air gap. More results are plotted in **Figure 12**.

The delivered torque and power, as well as the iron loss within the active parts of the integrated motor-gear studied variant are plotted in **Figure 11**. Some torque ripples can be identified, both on the inner and on the outer rotors of the integrated motor-gear variant. Since the major loss component within an MG is the iron loss, it is obvoius that the efficiency of the transmission is very low. One could sum the iron loss components (for the inner and outer rotor, as well as for the static part poles or teeth—SPP) and of the in-wheel motor to get 130 W. These results are not optimized. Based on the image of the flux density distribution (**Figure 10** right), we could consider to reshape the teeth's geometry and the outer rotor yoke, in the perspective of increasing the efficiency.

In order to prove the important advantage of MGs against classic mechanical gear, that is the possibility to obtain a very high transmission ratio for high-speed applications, and to get a very compact magnetic traction system with very good power density, the reader's attention will be oriented toward a high-speed MG.

## **3.2. MG with buried permanent magnets for high-speed applications**

The second analyzed MG is with high-speed inner rotor, running at 26,000 r/min (with *pin*=1), and outer rotor for low-speed rotor (with *pout*=15) running at approximately 1500 r/min. The number of static part iron teeth is *Ns*=16. The magnets are buried in order to avoid the surfacemounted pieces which need a consolidating ring. A preliminary check on the mechanical resistance of the inner rotor steel needs to be employed numerically, to check if the iron bridges have not been damaged during MG's operation. A cross section of the high-speed MG is shown in **Figure 13**.

**Figure 13.** The high-speed MG studied variant.

This MG will be evaluated for different steel material characteristics (M335, M400, and Vacoflux48), and permanent magnet types (Nd-Fe-B, Ferrite, Alnico). As a reference variant, the MG with Nd-Fe-B and M400 steel is considered. The first result of this configuration is shown in **Figure 14**, where the field lines and flux density distribution are shown. An obvious saturation is found on the inner rotor core, on the iron's bridge, which is normal behavior since the flux needs to pass the air gap.

State of the Art of Magnetic Gears, their Design, and Characteristics with Respect to EV Application 1587 http://dx.doi.org/10.5772/64174

**Figure 14.** The field lines and flux density distribution within the active parts of the high-speed MG.

The delivered torque and power, as well as the iron loss within the active parts of the integrated motor-gear studied variant are plotted in **Figure 11**. Some torque ripples can be identified, both on the inner and on the outer rotors of the integrated motor-gear variant. Since the major loss component within an MG is the iron loss, it is obvoius that the efficiency of the transmission is very low. One could sum the iron loss components (for the inner and outer rotor, as well as for the static part poles or teeth—SPP) and of the in-wheel motor to get 130 W. These results are not optimized. Based on the image of the flux density distribution (**Figure 10** right), we could consider to reshape the teeth's geometry and the outer rotor yoke, in the perspective of

In order to prove the important advantage of MGs against classic mechanical gear, that is the possibility to obtain a very high transmission ratio for high-speed applications, and to get a very compact magnetic traction system with very good power density, the reader's attention

The second analyzed MG is with high-speed inner rotor, running at 26,000 r/min (with *pin*=1), and outer rotor for low-speed rotor (with *pout*=15) running at approximately 1500 r/min. The number of static part iron teeth is *Ns*=16. The magnets are buried in order to avoid the surfacemounted pieces which need a consolidating ring. A preliminary check on the mechanical resistance of the inner rotor steel needs to be employed numerically, to check if the iron bridges have not been damaged during MG's operation. A cross section of the high-speed MG is shown

This MG will be evaluated for different steel material characteristics (M335, M400, and Vacoflux48), and permanent magnet types (Nd-Fe-B, Ferrite, Alnico). As a reference variant, the MG with Nd-Fe-B and M400 steel is considered. The first result of this configuration is shown in **Figure 14**, where the field lines and flux density distribution are shown. An obvious saturation is found on the inner rotor core, on the iron's bridge, which is normal behavior since

**3.2. MG with buried permanent magnets for high-speed applications**

increasing the efficiency.

in **Figure 13**.

will be oriented toward a high-speed MG.

8614 Modeling and Simulation for Electric Vehicle Applications

**Figure 13.** The high-speed MG studied variant.

the flux needs to pass the air gap.

Some transient simulations were carried out in Flux2D. In **Figures 15**–**17** are presented the simulated results for the case of an MG with M400 steel used for all cores and armature, and Nd-Fe-b as magnet material. Here, the output power and torque as well as the iron losses in the active parts of the structure are plotted. Very smooth mechanical performances have been obtained with such configuration of MG, with 1/16 ratio. The amount of iron loss in this case is about 800 W.

**Figure 15.** Simulated results for the MG with M400 steel and Nd-Fe-B magnet: input and output power [40].

**Figure 16.** Simulated results for the MG with M400 steel and Nd-Fe-B magnet: input and output torque [40].

**Figure 17.** Simulated results for the MG with M400 steel and Nd-Fe-B magnet: iron losses in the active parts [40].

Now, let us see what happens if better material is used, such as the Vacoflux48 steel, which has a very narrow hysteresis curve. The iron loss results are given in **Figure 18**. (The mechanical output performances are not given here; only the amount of iron loss in the fixed iron teeth, inner, and outer rotor of the MG.)

**Figure 18.** Simulated iron losses for the MG with Vacoflux48 steel and Nd-Fe-B magnet [40].

An amount of 365 W of iron loss was computed here. By comparing these iron losses (for the M400 steel, given in **Figure 17**, and the ones from **Figure 18**, for Vacoflux48), the huge advantage of the structure equiped with Vacoflux material is obvious, for which the iron loss is less than half of the M400—which is neither a bad material nor a cheap one. Of course, products based on Vacoflux48 are rather exclusive units and meant for special applications (like racing cars)—the cost of one prototype is almost prohibitively expensive. For ordinary applications, even the M400 steel is considered an expensive material—it is usually used for prototypes and low series manufacturing. Otherwise, a cheaper variant based on M335 is suitable in terms of output performances, giving similar results as the ones using M400.

A more complete comparison of the performances of the high-speed MG, when equipped with different types of materials and their magnetical characteristics is given in **Tables 4** and **5** the width of the considered steel sheet (0.2, 0.35, and 0.5mm) is given also in **Table 4**.

**Table 4.** The properties of the materials used in the construction of the high-speed MG.

**Figure 16.** Simulated results for the MG with M400 steel and Nd-Fe-B magnet: input and output torque [40].

**Figure 17.** Simulated results for the MG with M400 steel and Nd-Fe-B magnet: iron losses in the active parts [40].

**Figure 18.** Simulated iron losses for the MG with Vacoflux48 steel and Nd-Fe-B magnet [40].

inner, and outer rotor of the MG.)

8816 Modeling and Simulation for Electric Vehicle Applications

Now, let us see what happens if better material is used, such as the Vacoflux48 steel, which has a very narrow hysteresis curve. The iron loss results are given in **Figure 18**. (The mechanical output performances are not given here; only the amount of iron loss in the fixed iron teeth,

**Table 5.** Comparison of different configurations (material based) of MGs.

Some comments need to be made with respect to the Alnico and ferrite, the results of which are summarized in **Table 4**: for this specific configuration of the MG, because of the iron bridge which needs to support important centrifugal forces (we recall that the high-speed rotor is running at 26,000 r/min), and the low level of remanent flux density (for Ferrite material) or coercivity (for Alnico), the magnetic flux is not sufficiently strong to cross the air gap. Different rotor configurations (with concentrated flux or halback array) need to be considered—which means that a topology with more than one pair of poles is requested. To prove this, one can investigate the field lines and the flux densities depicted in **Figure 16**, given within the active parts of the MG while ferrite or Alnico materials are used (**Figure 19**).

**Figure 19.** Investigation of the MG field lines and flux density distribution, when ferrite (top) and Alnico (bottom) magnets were considered.

As 15 years have passed since the first efficient MG proposition, we could expect, in the next years, improvements in the field of MGs, especially for the ones with variable transmission ratio, which could be extremly useful in transportation and aeronautical domains. With the advancement in the field of materials, for rare earth materials and ferromagnetic steel, the future of fully electromagnetic propulsion systems could be considered as a viable industrial solution, and not only a new research subject.


**Treatments for Squamous Cell Cancer**

Editor **Charbel Darido**

MDPI ' Basel ' Beijing ' Wuhan ' Barcelona ' Belgrade ' Manchester ' Tokyo ' Cluj ' Tianjin

*Editor* Charbel Darido The Sir Peter MacCallum Department of Oncology University of Melbourne Melbourne Australia

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Cancers* (ISSN 2072-6694) (available at: www.mdpi.com/journal/cancers/special issues/Squamous Cell Cancer).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-4758-9 (Hbk) ISBN 978-3-0365-4757-2 (PDF)**

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**Author details**

Maher Shehadi Purdue University, United States

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 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.

**59**

*Net-Zero Energy Buildings: Principles and Applications DOI: http://dx.doi.org/10.5772/intechopen.92285*

> [10] Facilities Net. Net Zero Energy Buildings: How States Measure

Up. 2020. Available from: https://www. facilitiesnet.com/energyefficiency/ article/Net-Zero-Energy-Buildings-How-States-Measure-Up--17899

[11] US DoE. A common definition for zero energy buildings. Report prepared by The National Institute of Building Sciences for the U.S. Department of

[12] European Commission. Energy. Available from: https://ec.europa.eu/

[13] CIA. The World Factbook; 2020

[14] NASA. Climate 365. Accessed from: https://www.nasa.gov/content/

energy/content/nzeb-24\_en

climate-365-disagreement

[15] IISD. The Knowledge of Act. International Institute for Sustainable Development. Available from: https://www.iisd.org/topic/

sustainable-development

System-FINAL.pdf

4.30.13.pdf

[16] Bullitt Center. Rainwater-to-potable water system. 2019. Available from: https://bullittcenter.org/wp-content/ uploads/2019/04/Bullitt-Center-Water-

[17] Solar Design Associates. Net-Zero Energy Bullitt Center. Available from: http://solardesign.com/portfolio/ net-zero-energy-bullitt-center/

[18] Connors G, Panchuk I. La Jolla Commons II. 2013. Available from: http://www.josre.org/wp-content/ uploads/2012/10/La-Jolla-Commons-II-by-Hines-by-Panchuk\_Connors-

[19] Alter L. Aldo Leopold Legacy Center: The "Greenest Building on the Planet." treehugger. 2007. Available from: https://www.treehugger.com/

Energy; 2015

[1] Lin B, Li X. The effect of carbon tax on per capita CO2 emissions. Energy

[2] Shehadi M. Energy consumption optimization measures for buildings in the Midwest regions of USA. Buildings.

[3] Dong H, Qi S, MA J, Liu S. Study on energy saving in residential buildings in the cold district. In: Proceedings of the 2010 International Conference on Advances in Energy Engineering. IEEE;

[4] Li G. On the application of foamed

efficiency. In: Proceedings of the Second International Conference on Mechanic Automation and Control Engineering.

composites in building energy

IEEE; 2011. pp. 6654-6656

Vol. 1. 2009. pp. 174-197

2019;**36**(1):16-27

[5] Gellings CW. Efficient use and conservation of energy in buildings. In: Clark W, Gellings PE, editors. Efficient Use and Conservation of Energy – Encyclopedia of Life Support Systems.

[6] EIA. U.S. Energy Information Administration. Available from: https://www.eia.gov/tools/faqs/faq. php?id=86&t=1 [Accessed: April 2019]

[7] Shehadi M. Office thermostat accuracy and reliability. Journal of Engineering Technology.

[8] Mujahid RM, Gandhidasan P, Shafiqur R, Al-Hadhrami LM. A review on desiccant based evaporative cooling systems. Renewable and Sustainable Energy Reviews. 2015;**45**:145-159. DOI:

[9] Sadineni SB, Madala S, Boehm RF. Passive building energy savings: A review of building envelope

components. Renewable and Sustainable Energy Reviews. 2011;**15**:3617-3631

10.1016/j.rser.2015.01.051

Policy. 2011;**39**(9):5137-5146

2018;**8**(12):170-186

2010. pp. 351-354

**References**

*Net-Zero Energy Buildings: Principles and Applications DOI: http://dx.doi.org/10.5772/intechopen.92285*


*Article* **An NMR-Based Method for Multiphase Methane Characterization in Coals**

**Sijian Zheng 1,2, Shuxun Sang 1,2,3,\*, Shiqi Liu 1,2,\*, Xin Jin 4,5, Meng Wang 1,2, Shijian Lu 1,2, Guangjun Feng 3, Yi Yang 4 and Jun Hou 6**

**Abstract:** Discriminating multiphase methane (adsorbed and free phases) in coals is crucial for evaluating the optimal gas recovery strategies of coalbed methane (CBM) reservoirs. However, the existing volumetric-based adsorption isotherm method only provides the final methane adsorption result, limiting real-time dynamic characterization of multiphase methane in the methane adsorption process. In this study, via self-designed nuclear magnetic resonance (NMR) isotherm adsorption experiments, we present a new method to evaluate the dynamic multiphase methane changes in coals. The results indicate that the *T*2 distributions of methane in coals involve three different peaks, labeled as P1 (*T*2 < 8 ms), P2 (*T*2 = 20–300 ms), and P3 (*T*2 > 300 ms) peaks, corresponding to the adsorbed phase methane, free phase methane between particles, and free phase methane in the sample cell, respectively. The methane adsorption Langmuir volumes calculated from the conventional volumetric-based method qualitatively agree with those obtained from the NMR method, within an allowable limit of approximately ~6.0%. Real-time dynamic characterizations of adsorbed methane show two different adsorption rates: an initial rapid adsorption of methane followed by a long stable state. It can be concluded that the NMR technique can be applied not only for methane adsorption capacity determination, but also for dynamic monitoring of multiphase methane in different experimental situations, such as methane adsorption/desorption and CO2-enhanced CBM.

**Keywords:** low-field NMR; coal; free methane; paramagnetic mineral

#### **1. Introduction**

The increasing attention paid to clean energy has promoted the efficient development and exploration of the coalbed methane (CBM) industry [1–4]. At the same time, the scientific characterization of coals' petrophysical properties, such as pore structure, permeability, and methane adsorption capacity, have recently aroused great research interest [5–8]. For example, the methane adsorption capacity is a **crucial parameter** in evaluating the methane content of CBM reservoirs [7,9–11]. Additionally, investigating the methane adsorption process in coal pore systems is essential to establish and assess CBM wells' production [12,13]. Thus, an accurate and real-time dynamic characterization of the methane adsorption process in coals is necessary for CBM production.

**Citation:** Zheng, S.; Sang, S.; Liu, S.; Jin, X.; Wang, M.; Lu, S.; Feng, G.; Yang, Y.; Hou, J. An NMR-Based Method for Multiphase Methane Characterization in Coals. *Energies* **2022**, *15*, 1532. https://doi.org/ 10.3390/en15041532

Academic Editor: Nikolaos Koukouzas

Received: 9 January 2022 Accepted: 16 February 2022 Published: 18 February 2022

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

**Copyright:** © 2022 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/).

Commonly, the methane adsorption capacity is determined using the volumetric-based adsorption isotherm [14,15]. However, this method is quite complicated and susceptible to impurities or the volume effect, may result in larger variations in experimental results [16]. Furthermore, the Langmuir isotherm curve consists only of cross-plots of adsorbed methane content and experimental pressure, and has the significant shortcoming that it is incapable of depicting any details in the methane adsorption process. The dynamic characterization of the methane adsorption process in coals is usually evaluated based on the assumption of a single pore or regular structure pore network model, which is derived from well-established sandstone or tight sandstone [17,18]. However, due to complicated and heterogeneous coal pore structures, the model obtained from sandstone or tight sandstone needs to be modified, which is complex and time consuming. To the best of the authors' knowledge, few methods can be individually and directly applied for the dynamic characterization of methane adsorption in coals.

Nuclear magnetic resonance (NMR) is a non-destructive technique that has been widely applied for investigating hydrogen-bearing reservoir fluids' petrophysical properties [19–25]. Based on the fully water-saturated and centrifuged NMR transverse relaxation time (*T*2) distribution of coals, Zheng et al. [22] calculated the *T*2 cutoff values and then used them to classify pore types and evaluate the full-scale pore size distribution. Guo et al. [26] first applied the NMR *T*2 measurement to evaluate the methane *T*2 distributions of lowrank coals. They found three different methane relaxation mechanisms existing in coals: adsorbed methane, porous medium confined methane, and free methane. Vasilenko [27] presented an NMR study of the ratio between the free and adsorbed phase methane in fossil coals, and found that the adsorbed methane was the predominant phase state, only upon the opening of a high-pressure chamber after the emission of methane from filtration channels. Liu et al. [28] discussed the CO2 enhanced gas recovery efficiency in shales based on NMR measurement. They found that the higher concentration ratios of CO2/CH4 are more efficient for gas recovery. By introducing the NMR relaxation method, Yao et al. [29] investigated the multiphase methane relaxation characterization in shales. In their study, NMR peaks with *T*2 < 1 ms and 1–50 ms corresponded to the adsorbed phase methane and free phase methane in shale, respectively. Additionally, Yao et al. [30] characterized the methane adsorption capacity on two low volatile bituminous coals using low-field NMR measurements and revealed that the P1 peak amplitude increases rapidly at the beginning stages, and then trends to attain an ultimate value, in a manner similar to the Langmuir equation. However, the research objects in Yao et al. [30] were only two low-rank coals, which restricted the NMR relaxation measurement application for medium- and high-rank coals, especially for some coals containing paramagnetic minerals. Previous achievements have verified the qualitative ability of the NMR relaxation method in methane adsorption characterization in unconventional reservoirs (e.g., shale and coal). Moreover, few studies relate to the application of the NMR data for estimating the real-time dynamic methane adsorption process in coals.

In this paper, a series of comparative volumetric- and NMR-based methane adsorption measurements were performed for eight coals whose ranks were strikingly different. Comparing experimental results from the volumetric method, the accuracy of the NMRbased method for methane adsorption characterization in coals having different ranks was validated. Based on the NMR *T*2 distribution in different time intervals, the methane adsorption dynamic process characteristics in coals were investigated.

#### **2. Experimental Methods**

#### *2.1. Coal Sampling*

In this study, a total of eight block coals (20 × 20 × 20 cm3) having different ranks were collected from the Junggar basin, Ordos basin, and Qinshui basin, China. Table 1 shows the summary of the detailed basic petrophysical coal information. The maximum vitrinite reflectance (*R*o,m) of the selected samples ranges from 0.52% to 3.03%, covering a wide array of coal ranks, including low, medium, and high ranks.

**Table 1.** Detailed information of the selected coals.

Notes: *R*o,m—maximum vitrinite reflectance, V—vitrinite; I—inertinite; E—exinite; M—minerals. *M*ad—moisture (air-dried basis); *A*d—ash (dry basis); *FC*d—carbon (air-dried basis).

Figure 1 shows the pore morphology characteristics of the selected coals by the scanning electron microscope (SEM) measurement. Results show that the pore types developed in samples L3, M1, H1, and H2 are mainly gas pores. Intergranular pores and residual plant tissue pores are found in samples M2 and H3, respectively, whereas fractures are well developed in samples L1 and L2.

**Figure 1.** Pore morphology characteristics of the selected coals by SEM measurement.

#### *2.2. NMR Adsorption Measurements*

Compared with NMR *T*1 measurements, *T*2 measurements are preferred for the characterization of petrophysical properties in rocks due to their obvious advantages, such as short testing time, good applicability, and convenient operation. Generally, the NMR *T*2 characteristics are tested by applying the CPMG sequence. Based on the NMR principle, *T*2 can be characterized as follows [31–35]:

$$\frac{1}{T\_2} = \frac{1}{T\_{2B}} + \frac{1}{T\_{2S}} + \frac{1}{T\_{2D}} = \frac{3T\_k}{298\eta} + \rho\_2 \left(\frac{S}{V}\right) + \frac{D\left(\gamma GT\_E\right)^2}{12} \tag{1}$$

where *T*2*B* represents bulk relaxation; *T*2*S* represents surface relaxation; *T*2*D* represents diffuse relaxations; *Tk* means experimental temperature; *η* means methane viscosity; *ρ*2 means surface relaxivity of the sample; *S* means specific surface area; *V* means pore volume; *D* means methane diffusion coefficient; *γ* means proton gyromagnetic ratio; *G* means magnetic field intensity; *T*E means echo spacing time.

$$\frac{1}{T\_2} = \frac{1}{T\_{2\text{B}}} + \rho\_2 \left(\frac{S}{V}\right) \tag{2}$$

As shown in Figure 2, the experimental set-up of NMR methane adsorption measurement mainly includes five parts: (1) the gas supply system, providing a different gas source for the experimental system; (2) the gas exhaust system, recycling exhaust gas after experiments; (3) a non-magnetic PEEK sample cell, for placement of coal powders; (4) a non-magnetic PEEK reference cell, for transporting and sustaining methane pressure for the sample cell; and (5) an NMR measurement device, measuring the NMR *T*2 distributions in methane adsorption process. Here, NMR experimental parameters were set to: waiting time, 3000 ms; echo spacing, 0.3 ms; echo numbers, 10,000.

**Figure 2.** Schematic diagram of the NMR methane adsorption experimental set-up.

Two independent experiments were performed for methane adsorption characterization by the NMR measurement; one was a free methane *T*2 relaxation property experiment (no powder coals in the sample cell) and the other was a methane adsorption *T*2 relaxation experiment.

The free methane *T*2 relaxation property was measured under different methane pressures, at a stable temperature (304.15 K). The amount of free methane can be determined according to the ideal-gas equation. At the same time, the NMR relaxation distributions of free methane under different pressures were measured using an NMR device.

Prior to methane adsorption *T*2 relaxation experiments, the sample cell and reference cell must be cleaned to prevent the contamination of impurities. Then, 60–80 mesh powder coals were dried at 368 K in a vacuum oven at least for one day. The workflow of methane adsorption *T*2 relaxation experiments was as follows:

P1: The powder coals were transformed into the sample cell and placed in vacuum for three hours;

P2: ~8 MPa free methane was injected into the reference cell;

P3: The G8 valve was switched on to introduce the reference cell methane into the sample chamber until it reached the set pressure;

P4: The NMR *T*2 distributions were continuously measured for methane adsorption until completely adsorbed, with an interval time of 45 min;

P5: The sample cell methane pressure was increased to six different pressure levels, which were repeated for the same sample.

#### **3. Results and Discussions**

#### *3.1. Free Methane T2 Distributions*

As shown in Figure 3, the *T*2 characteristics for free methane under different pressures exhibit one significant peak. This peak appears at approximately ~50–1500 ms. As shown in Figure 4, the NMR spectra amplitude shows an evident linear relationship with the free methane mass:

$$y = 0.000015x \left(R^2 = 0.9990\right) \tag{3}$$

where *y* means the free methane mass; *x* means the free methane NMR signal amplitude.

**Figure 3.** NMR *T*2 distributions of free methane.

**Figure 4.** Correlations between *T*2 amplitude vs. methane mass.

#### *3.2. Methane Adsorption T2 Relaxation Characteristics*

Methane adsorption *T*2 spectra for the selected coals are presented in Figure 5. The minimal *T*2 amplitude of the dry samples (solid purple line) indicates the nuclear responses from the coal itself can be ignored. Results show that the methane adsorption *T*2 spectra exhibit three peaks: P1, P2, and P3. The NMR P1 peak appears in approximately ~0.1–8 ms, whereas the P2 and P3 peaks emerge in approximately ~20–300 ms and 300–2000 ms, respectively. Based on the NMR principle in Equation (2) and the research results in Yao et al. [30], the multiphase methane in coals characterized by NMR was divided into

three parts: (1) the adsorbed phase occurring in the coal pore surface, represented by the NMR P1 peak; (2) the free phase emerging between coal particles, represented by the NMR P2 peak; and (3) the free phase occurring in the sample cell free space, represented by the NMR P3 peak. Here, we defined the multiphase methane in coals as the adsorbed phase and the free phase.

**Figure 5.** NMR *T*2 spectra of methane adsorption in coals.

Figure 5 shows that all peak (P1, P2, and P3) amplitudes have an increasing trend with the increase in methane pressure. In order to advance quantitative characterization of the NMR P1 peak variation, the relationship between NMR P1 peak amplitude and methane pressure is plotted in Figure 6a. The results show that the NMR P1 peak amplitude rapidly increases at the beginning stages, and then trends to attain an ultimate value, in a manner similar to the Langmuir equation.

**Figure 6.** NMR P1 (**a**) and P2 + P3 (**b**) amplitude changes during the process of methane adsorption.

Figure 6b shows that the free phase methane amplitudes have a linear relationship with the methane pressure. The results in Figure 6 confirm that the NMR P1 peak represents the adsorbed phase methane, and NMR P2 + P3 represent the free phase methane. Moreover, the adsorbed methane content cannot be determined by NMR P1 amplitude directly because the relaxation mechanisms are obviously different between free phase methane and adsorbed phase methane. Free phase methane was controlled by bulk relaxation, whereas adsorbed phase methane surface relaxation was controlled by surface relaxation [36].

#### *3.3. Methane Adsorption Capacity Determination*

Methane adsorption capacity is **crucial for** understanding and predicting the CBM content [37–40]. This section provides a detailed comparison of the methane adsorption capacity based on the volumetric method and the self-designed NMR method. Then, the application of the NMR-based method to different coals is further estimated and discussed.

#### 3.3.1. Volumetric-Based Method

Volumetric-based adsorption measurements are commonly used for calculating the methane adsorption capacity in coals. Based on the equation of the state of an ideal gas, the reduced amount of free phase methane can be calculated based on the reduction in sample cell pressure:

$$PV' = nZRT\tag{4}$$

where *P* means experimental pressure; *V* means free methane volume; *n* means free methane amount; *Z* means compressibility factor; *R* is 8.3144 J/(mol·K); *T* means experimental temperature.

Based on the volumetric-based adsorption experimental data, including adsorption pressure (*P*) and adsorption volume (*V*), the Langmuir volume (*V*L) and pressure (*P*L) can be calculated using the Langmuir equation:

$$V = \frac{V\_L P}{P + P\_L} \tag{5}$$

where *V* means experimental adsorption volume, equaling to the reduction in free methane volume in the cells; *P* means experimental pressure, which can be obtained directly from a pressure sensor in the sample cell. *V*L and *P*L mean the Langmuir volume and Langmuir pressure, which can be determined based on Langmuir curve fitting between the parameters of experimental adsorption volume (*V*) and experimental pressure (*P*).

As shown by the blue dotted line in Figure 7, the volumetric-based adsorption experimental data exhibit an excellent fit with the Langmuir curve (Figure 7). As listed in Table 2, the volumetric-based Langmuir volumes (*V*L-vol) are in the range of 5.79–21.14 cm3/g, whereas the volumetric-based Langmuir pressure (*P*L-vol) ranges from 0.65 to 2.75 MPa.

**Figure 7.** Adsorption isotherms obtained from two different methods.

**Table 2.** Comparison of the Langmuir parameters determined by volumetric-based and NMR-based methods.

Note: The index variables 'vol' and 'NMR' represent the results from the volumetric isotherm adsorption method and NMR isotherm adsorption method, respectively.

Figure 8 displays the relationships between *V*L-vol and coal's basic petrophysical parameters (*R*o,m, vitrinite + inertinite content, *FC*d, and *A*d content). The *V*L-vol shows a 'U-morph' relationship, with *R*o,m, having a minimum value at approximately ~1.6% (Figure 8a). The 'vitrinite + inertinite' content shows a positive relationship with the values of *V*L-vol (Figure 8b), but no significant correlations were found between the vitrinite content, inertinite content, and *V*L-vol (data not shown here). As shown in Figure 8c, the *V*L-vol is positively correlated with the *FC*d content. Compared with the results in Figure 8a, the influence of *FC*d content on Langmuir volume is weaker than that of *R*o,m. Figure 8d displays the relationship between the *A*d content and *V*L-vol. The results show that, with the increase in the *A*d content, *V*L-vol shows a decreasing trend, probably because the presence of ash can decrease the concentration of organic matter and reduce the methane adsorption capacity in coals [41].

#### 3.3.2. NMR-Based Method

As discussed in Section 3.2, Equation (3) may lead to an erroneous adsorbed methane content. In the process of methane adsorption, only injected methane initially consists of a pressure-tight sample cell. Based on conservation of mass, the increase in adsorbed methane must have resulted entirely from the decrement in free methane. Similarly, the amount of adsorbed methane under specific pressures can be indirectly determined as:

$$V\_{ads} = V\_{tot} - V\_{pip} - V\_{fre} \tag{6}$$

where *Vads* means the adsorbed methane content, cm3; *Vpip* means the free methane content in the pipeline between two cells, 34.5 cm3; *Vtot* means the total methane content in the sample cell, which can be determined according to the equation of state of ideal gas, cm3; *Vfre* means the free methane content in sample cell, cm3.

Similar to the calculation steps of the volumetric-based Langmuir parameter, the NMRbased adsorption experimental data, including adsorption pressure (*P*) and adsorption volume (*V*), are fitted by the Langmuir equation using Excel. As shown in Figure 7, the adsorption isotherms determined by the NMR method fitted well with the Langmuir equation.

#### 3.3.3. Validity Application of the NMR Method

To verify the NMR method for quantitative evaluation of adsorbed phase methane content in coals, the results from the NMR method were compared with those from the volumetric-based method. Figure 7 shows the adsorption isotherms evaluated by the two different methods (volumetric- and NMR-based methods). The results show that the adsorption isotherms from these two methods have an excellent agreement for the selected coals (except for M2). Additionally, Figure 9 shows the scatter plots of the Langmuir volume obtained from the two methods contrasted in this study. The results show that all data points are approximately distributed on the 45◦ diagonal line (the dashed line in Figure 9), with the exception of sample M2. It is worth noting that the *V*L-NMR values are slightly less than *V*L-vol (Figure 9); this is probably because: (1) the minimum *T*E value of NMR device in this study is 0.24 ms, thus does not detect the NMR distributions when *T*2 is smaller than 0.24 ms; and (2) due to the methane adsorption exothermic in coals, the temperature error would have little effect on the experimental results. The relative deviation between the NMR and volumetric methods is ~5.95% (except for sample M2), which is within an allowable error. Additionally, the coal powders used in this study may result in a discrepancy in the experimental data for granulated samples because the grinding of samples would destroy the primary texture of the coal pore systems.

**Figure 8.** The cross-plots of *R*o,m, vitrinite + inertinite content, *FC*d content, and *A*d content versus Langmuir volume *V*L-vol. (**a**) *R*o,m versus Langmuir volume *V*L-vol, (**b**) vitrinite + inertinite content versus Langmuir volume *V*L-vol, (**c**) *FC*d content versus Langmuir volume *V*L-vol, (**d**) *A*d content versus Langmuir volume *V*L-vol.

**Figure 9.** Scatter plots of Langmuir volume obtained from two contrasting methods.

For the sample M2, the adsorption isotherms determined from the NMR method show a large deviation from those of the volumetric-based method (Figure 7), and the Langmuir volume relative deviation is relatively high, at ~17.15%. Previous literature has found that paramagnetic minerals can form larger internal magnetic fields, and thus influence the NMR signal response [42]. SEM-EDS results demonstrate that some paramagnetic minerals were present in sample M2, namely, pyrite (Figure 10a) and ferrocalcite (Figure 10b). This probably results in a larger variation in methane adsorption capacity. The experimental results indicate that NMR *T*2 measurements can be considered to be an effective method for the characterization of adsorbed methane content, even though some limitations exist due to paramagnetic minerals containing coals. Thus, the results observed in this study identify the directions for future work because the influence of paramagnetic minerals on NMR signals cannot be neglected.

**Figure 10.** SEM-EDS results in sample M2. (**a**) pyrite minerals occured in sample M2, (**b**) ferrocalcite minerals occured in sample M2.

#### *3.4. Methane Adsorption Dynamic Process Characterization*

It is known that the Langmuir isotherm curves are only cross-plots of the adsorbed methane content and methane pressure. The shortcoming of this is that it is incapable of depicting any details of the methane adsorption dynamic process. An evident advantage of the NMR measurement designed in this study is that it can dynamically monitor multiphase methane in the methane adsorption process, because the NMR device can automatically measure the multiphase methane *T*2 characteristics at any time. Here, the sample L2 is taken as a representative sample to discuss the application of the NMR relaxation method to dynamic characterization methane adsorption.

Figure 11a shows the NMR methane adsorption spectra under a methane pressure of 3.46 MPa with an interval time of 45 min. At the methane first injection, the NMR P1 peak shows a clear increasing trend, whereas the NMR P2 peak shows a decreasing trend. By comparison, the NMR P3 peak has changed to an obvious spectral peak. This is probably because of the larger amount of methane injected into the sample cell free space, which results in the clearer *T*2 relaxation. The amplitude of the P1 peak increases rapidly from 1150.71 to 1652.53 during the first 45 min, and then decreases to an essentially stable level after 180 min (Figure 11b). The NMR P2 peak shows a rapidly decreasing trend during the first 135 min, and then increases slowly to reach an equilibrium condition.

**Figure 11.** NMR *T*2 spectra of methane adsorbed (**a**) and peak amplitude change characteristics (**b**) under a pressure of 3.46 MPa with an interval time of 45 min for sample L2.

Figure 12 displays the relationship between the adsorbed methane *T*2 amplitude and injection time under six different pressures for sample L2. The results indicate that the NMR *T*2 amplitude of the adsorbed methane increased rapidly at about 45 min, and then decreased to an equilibrium condition at approximately 3 h, indicating the adsorption time was approximately ~3 h. Compared with the conventional method (e.g., volumetric- or gravimetric-based methods), the unique features of the NMR-based method not only enable the determination of methane adsorption capacity, but also allow dynamic monitoring of multiphase methane in different experimental situations, such as methane adsorption/desorption and CO2-enhanced CBM.

**Figure 12.** Relationship between adsorbed methane *T*2 amplitude and injection time under six different pressures, for sample L2.

#### **4. Conclusions**

This paper proposed a novel method for the evaluation of the methane adsorption capacity and the quantitative characterization of the methane adsorption dynamic process based on NMR *T*2 measurements. **The main conclusions are:**

**Author Contributions:** S.S. and S.L. (Shiqi Liu) provided the funding acquisition; S.Z. performed the experiments and wrote the paper; M.W. and S.L. (Shijian Lu) analyzed the data; G.F. and J.H. revised the paper; Y.Y. and X.J. provided technical support. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (grant number: 42141012; 42030810; 41972168), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

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

#### **References**


**Otorhinolaryngologic Manifestations in Leishmaniasis**

Luiz Alberto Alves Mota and Roberta Correia Ribeiro Ferreira de Miranda

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57303


**4. Materials and Methods**

Filters were applied to the homodimeric structures of the MFIB database. A reference dataset was created from homodimeric globular proteins, where the monomeric form was also globular in itself. Another reference dataset was created from monomeric globular proteins.

All homodimeric structures were collected from the MFIB database, and the modified PDB files were used. Entries belonging to the "coils and zippers" structure class were discarded since structures belonging to this class are both sequentially and structurally different from other homodimers. It is evident that a structure like a leucine-zipper cannot exist in monomeric form, thus no reference dataset can be created from "coils and zippers" where the monomer is not disordered in itself. A contact map matrix for all remaining structures was created. Entries with unusual contact maps were manually inspected. After inspection, the following entries were discarded: 2adl, 1r05, 4ath, 1aa0, 4w4k, 1ejp, resulting in a dataset of 60 homodimeric structures (Table S1). Heteroatoms were deleted from the structure. This dataset was referred to as MFIB homodimeric dataset (MFHD). We checked the secondary structure of the databases using the DSSP 2.0.4 program [35]. We found that 39.4% of the residues belonged to α-helices and 21.2% to β-sheets. The size distribution of the dataset was investigated. We counted the number of residues belonging to the N, N + 20 intervals. We found that the 140–240 interval was predominant, thus the reference datasets were created according to this size distribution.

A non-redundant reference dataset was created from homodimeric globular proteins. All homodimeric structures within the 140–240 amino acid size range were collected from the non-homologous PDB\_Select database as of November, 2017 [36]. Structures containing coiled-coil structural elements identified with the Socket 3.0.3 program were excluded from the dataset [37]. The proper quaternary structure of the homodimers was created according to the BIOMT records of the PDB files. Entries with the following PDB ligand summary "ids" of large molecular sizes ligands and cofactors were discarded from the dataset because they could significantly alter the results of the solvent-accessible surface area calculations (017, 1BG, 1PE, 1PG, 5GP, C2E, FAD, HEC, KI1, MYA, MYR, MYS, NER, O8N, OLC, P33, P6G, PE5, UNL). Heteroatoms were deleted from the remaining structures. This procedure resulted in a list of 218 protein structures. This dataset was referred to as the globular homodimeric dataset (GLHD). For the PDB codes, see Table S1. According to DSSP, 27.9% of the residues belonged to α-helices and 27.1% to β-sheets.

An additional non-redundant reference dataset of the monomeric structures in the 140–240 amino acid size range containing only one structural domain was created from the PDB\_SELECT database. The initial database was filtered by size and monomeric state criteria. All entries proved to be single domain according to the DDomain program using authors-trained parameters [38]. This dataset was referred to as the globular monomeric dataset (GLMD) and contained 191 entries (Table S1). According to DSSP, 24.9% of the residues belonged to α-helices and 28.3% to β-sheets.

Differences in the amino acid composition of the proteins sequences from the MFHD, GLHD, and GLMD datasets were revealed by principal component analysis (PCA) ordination using the plotly software according to Raska [39].

Hydrogen bonds were identified using the find\_pairs command of PyMOL using 3.5 Å distance and 45 degree angle criteria between the donor and acceptor groups [40]. The calculation of the wrapping of hydrogen bonds and the identification of dehydrons was performed with the dehydron\_ter.py program [41].

Stabilization centers (SCs) are pairs of residues, called stabilization center elements (SCEs), which are involved in several long-range interactions. These residues can be identified with our publicly available web server at http://scide.enzim.hu [42].

The solvent-accessible surface area (SASA) was calculated using the FreeSASA 2.03 program [43]. A residue was classified as buried when its relative SASA was below or equal to 0.2. Residues with a relative SASA value of over 0.2 were considered as exposed. A residue was classified as part of the interface region when its all-atom SASA calculated from the dimeric structure was less than 20% of the value calculated from the monomeric structure (created by deleting the second chain from the PDB file).

Ion-pairs were defined as pairs of negatively and positively charged residues, where the distance between the charged groups was equal to or less than 4 Å [44]. Ion pairs were identified using our own C++ program.

**Supplementary Materials:** Supplementary materials can be found at http://www.mdpi.com/1422-0067/19/ 11/3340/s1. Table S1. List of PDB entries in the MFHD, GLHD, GLMD datasets. Table S2. Average amino acid sequence composition of proteins from MFHD, GLMD, and GLHD. Table S3. Amino acid sequence composition of proteins from MFHD, GLMD, and GLHD for PCA. Table S4. Disorder content by various predictors. Table S5. Average amino acid composition of interface region of the proteins from MFHD and GLMD. Table S6. Average amino acid composition of RSAMPs of the proteins from MFHD and GLMD.

**Author Contributions:** Conceptualization, I.S., C.M., M.C.; methodology, A.M., E.F., C. M.; software, A.M., E.F., M.C., C.M.; validation, A.M., C.M; formal analysis, C.M.; investigation, A.M., E.F.; resources, A.M, E.F.; data curation, A.M., E.F., C.M.; writing—original draft preparation, A.M., C.M., I.S.; writing—review and editing, E.F., A.M., C.M; visualization, A.M.; supervision, I.S., M.C.; project administration, I.S.; funding acquisition, I.S.

**Funding:** This work was financially supported by the National Research, Development and Innovation Office (grant no. K115698). IS was supported by project no. FIEK\_16-1-2016-0005 financed under the FIEK\_16 funding scheme (National Research, Development and Innovation Fund of Hungary). The work of AM was supported by the ÚNKP-18-3 New National Excellence Program of the Ministry of Human Capacities (Hungary).

*Int. J. Mol. Sci.* **2018**, *19*, 3340

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.