Solid materials exhibiting purely ionic conduction are used as solid-state electrolytes in a wide variety of electrochemical devices and chemical sensors, with the corresponding charge carriers being specific ions such as H+, Cu+, Ag+, Na+, Li+, F– , and O2– . The resulting charged ion flow in electrolytes creates an electric current that drives the device, the characteristics and performance of which are thus influenced by the nature of the charge carriers. Generally, in view of their small ionic radii, cations migrate easily in solid electrolytes, showing facile diffusion. For example, silver and copper ion solid electrolytes, such as RbAg4I5 and Rb4Cu16I7Cl13, show extremely high ionic conductivities of > 100 mS cm−1 at room temperature [1–3]. Moreover, the recently developed lithium ion conductors (Li10GeP2S12, LGPS) have achieved room temperature conductivities of >10 mS cm−1 [4, 5], with Li-based all-solid-state batteries reported to exhibit exceptionally good power characteristics. On the other hand, newly developed materials such as hydride ion conductors have expanded the research field and the scope of available energy devices [6, 7]. In this section, we focus on Li+ and H– as charge carriers and describe the structural characteristics of the corresponding newly developed materials.

#### **13.2 Lithium Ion Conductors**

Lithium ion conductors continue to attract much attention owing to their practical applications in all-solid-state lithium batteries [5, 8]. A wide variety of such conductors exists (e.g., LISICON, perovskite, garnet, glass, glass ceramics, thio-LISICON, and LGPS), some of which were developed in the 1970s [4, 9–14]. For instance, LGPS-based materials (*σ* > 10 mS cm−1 at 25 °C) enable high-power operation of solid-state lithium batteries; this is an intrinsic merit of solid-state systems, in addition to their safety and reliability. However, sulfide-based solids are sensitive to atmospheric moisture. As a result, most current research focuses on oxide-based materials, in order to satisfy the requirements of practical applications and engineering processes.

Novel ion conductors are typically developed using three methods: (i) element substitution-based, (ii) structure-based, and (iii) composition-based material searches. Approach (i) relies on existing materials with ionic conductivity of the target charge carrier [15], which are amenable to tuning of their physical and electrochemical properties [16]. Therefore, although it is relatively easy to find new materials using this method, remarkable performance improvements are difficult to achieve. Approach (ii) is initiated by selecting a suitable crystal structure candidate for ion diffusion [6, 11], which can be complicated by the fact that the diffusion of the target ion in the selected structure has usually not been demonstrated.

Finally, approach (iii) is the most challenging, but also has the greatest potential to afford new materials with unique structures and properties. This approach starts with the selection of a suitable phase diagram [17]. Subsequently, materials corresponding to the chosen region in this diagram are synthesized and characterized; in certain cases, they exhibit unique structures and properties [4, 18]. In this chapter, some examples of material searches are introduced.

#### *13.2.1 Novel Lithium Ion-Conducting Perovskite Oxides [15]*

Lithium ion-conducting solids are key materials for all-solid-state lithium batteries, which, compared with conventional liquid electrolyte-based lithium batteries, exhibit improved energy density, stability, safety, and reliability. Among the solid electrolytes that have been developed, the oxide-based ones are among the most promising candidates, owing to their high ionic conductivities and good chemical stabilities over a wide range of operating temperatures [11, 19]. Lithium ion-conducting perovskites such as La(2/3)–*x*Li3*x*TiO3 (which exhibits an ionic conductivity above 10−3 S cm−1 at room temperature) are considered to be particularly attractive [19]. However, the interfacial reduction of Ti+4 to Ti+3 during the electrochemical process or upon contact with lithium metal gives rise to undesirable electronic conduction. On this basis, novel perovskite-structured materials were examined, in typical example of an element substitution-based material search. As a result, the Li-Sr-Ta-*M*-O system (*M* = Al, Ga) was postulated to be ideal for achieving high ionic conductivity, with (Li*x*Sr1–*x*)(*M*(1–*x*)/2Ta(1+*x*)/2) O3 mixed oxides expected to exhibit superior characteristics owing to (i) the presence of largely non-reducible metals (Ta and Al/Ga) in their structures, (ii) the presence of a large cation (Sr) at the *A*-site, (iii) the limited distortion of *B*O6 octahedra exhibited by Ta at the *B*-site, (iv) the presence of a small *B*-site cation (Al, Ga), and (v) the availability of controlled vacancies introduced by adjusting the concentration of the *B*-site cation. Thus, (Li*x*Sr1–*x*)(*M*(1–*x*)/2Ta(1+*x*)/2)O3 (*M* = Al, Ga) and (Li*x*Sr1–*x*–*y*V(Li,Sr)*y*)(Ga[(1–*x*)/2]–*y*Ta[(1+*x*)/2]+*y*)O3 systems were synthesized by solid-state reactions involving Li-rich starting materials to obtain a single phase. These were subsequently subjected to electrochemical examination and crystallographic analysis by X-ray and neutron diffraction Rietveld analysis.

The temperature-dependent conductivities of (Li0.2Sr0.65V(Li,Sr)0.15)(Ga0.25Ta 0.75)O3 (*x* = 0.2, *y* = 0.15) and (Li0.25Sr0.625V(Li,Sr)0.125)(Ga0.25Ta0.75)O3 (*x* = 0.25, *y* = 0.125) are shown in Fig. 13.1, with the corresponding activation energies (*E*a) calculated as 35.04 and 34.64 kJ mol−1 , respectively. The comparable *E*a values of these systems indicate that they both feature the same Li+ conduction mechanism. The highest conductivity exhibited by the (Li0.25Sr0.625V(Li,Sr)0.125)(Ga0.25Ta0.75)O3 (*x* = 0.25, *y* = 0.125) sample equaled 1.85 × 10−3 S cm−1 at 250 °C, which was the highest value measured for Li-Sr-Ga-Ta-O perovskite materials. The structure of

this sample was determined by powder neutron diffraction and the obtained data were refined using a structural model of a cubic perovskite-type material with *Pm*-3 *m* symmetry.

Figure 13.2 shows the thermal ellipsoid structure model obtained by neutron Rietveld analysis and Table 13.1 summarizes the corresponding interatomic distances, revealing that the O–O distance of 2.79411 Å in (Li0.25Sr0.625V(Li,Sr)0.125) (Ga0.25Ta0.75)O3 was slightly larger than that determined for cubic perovskite-structured Li0.5La0.5TiO3 (2.7358 Å). This increase was most likely due to substitution by the larger Sr2+ cation at the *A*-site, which is responsible for widening the bottleneck for lithium ion diffusion in the structure. The *A*–O distance of 2.79411 Å was larger than the calculated sum of the Li and O ionic radii of 2.32 Å (Li+ (CN 8): 0.92 Å and O2– (CN 6): 1.4 Å), making the Li cation more ionic in nature and, therefore, more mobile. The tolerance factor *t*, calculated based on the ionic radius of Sr2+ at the *A*-site, equaled 0.9855, which was close to unity and indicated an ideal cubic perovskite-type structure.

The increased ionic conduction was confirmed to result from the introduction of vacancies at *A*-sites. Average bond valence sum (BVS) values were calculated for each site of the perovskite structure using refined structural data, with the average BVS for *A*-sites equaling 1.98. The larger average BVS of (Li0.25Sr0.625V(Li,Sr)0.125) (Ga0.25Ta0.75)O3 compared with that of La(2/3)-*x*Li3*x*TiO3 (0.95–1.57) [20]

**Fig. 13.2** Crystal structure model of (Li0.25Sr0.625V(Li,Sr)0.125)(Ga0.25Ta0.75)O3 based on neutron Rietveld analysis

corresponded to the greater activation energy required for ion conduction in the former. Figure 13.2 shows the perovskite structure with thermal ellipsoids for each site, revealing their approximately isotropic thermal nature. The presence of vacancies in conjunction with Li ions at the *A*-sites suggests that Li diffusion proceeds according to the vacancy mechanism.

#### *13.2.2* **M***-Doped LiScO2 (***M** *= Zr, Nb, Ta) [21] as New Lithium Ion Conductors*

No material has yet been discovered that satisfies all requirements imposed on lithium ion conductors as solid electrolytes for battery applications (i.e., high ionic conductivity at room temperature, chemical stability, electrochemical stability, thermal stability, and low cost). This clearly indicates the need for further research efforts in this direction. Herein, we focus on LiScO2, which has an ionic conductivity of 4 × 10−9 S cm−1 at 573 K. Although this value is not overly high, the above material is still attractive in view of its enhanced thermodynamic stability in contact with lithium metal [22].

As shown in Fig. 13.3, LiScO2 has a fractional cationic ordered rock-salt structure exhibiting tetragonal *I*41/*amd* symmetry [23], which has the potential to partially rearrange depending on the synthesis conditions and the doped element [24]. Although element doping is an effective method of increasing the ionic conductivities of solid lithium ion conductors such as LiScO2, no corresponding investigations have been reported. Thus, in an attempt to improve the ionic conductivity of LiScO2 by introducing lithium vacancies into its structure, this material was doped by *M* = Zr4+, Nb5+, and Ta5+, and the crystal structures and ionic conductivities of the thus prepared Li1−*y*Sc1−*xMx*O2 were evaluated in detail.

Li1−*y*Sc1−*xMx*O2 (*M* = Zr4+, Nb5+, or Ta5+; *x* = 0.1) were obtained via a solid-state reaction (sintering at 1073–1623 K for 1–12 h in air). Their impedance spectra and temperature-dependent conductivities are presented in Fig. 13.4. The

**Fig. 13.3** Crystal structure of LiScO2 [23], with blue octahedra and green spheres indicating ScO6 and Li, respectively

**Fig. 13.4 a** Representative impedance plots at 623 K and **b** Arrhenius plots showing the temperature-dependent conductivities of doped Li1−*y*Sc1−*xMx*O2 (*M* = Zr4+, Nb5+, or Ta5+)

conductivities were calculated from the corresponding impedance spectra, which comprised semicircles and spikes corresponding to contributions of the bulk and grain boundary and the electrode, respectively. The bulk and grain boundary contributions could not be separated and were therefore calculated together.

Resistances were calculated from the diameter of the aforementioned semicircles and used to determine conductivities. The diameters of these semicircles decreased upon doping, indicating the suitability of impedance spectroscopy to survey and evaluate ionic conductivities, with capacitance values corresponding to the observed semicircles being in the range of 10−10–10−12 F. Table 13.2 summarizes the ionic conductivities and activation energies of Li1−*y*Sc1−*xMx*O2 (*x* = 0.1) at 573 K, along with the values previously reported for LiScO2. All doped samples showed higher conductivities than the parent compound, owing to the formation of solid solutions upon aliovalent cation doping. Furthermore, this doping decreased the activation energies by more than 10%, indicating that the formation of lithium vacancies in the LiScO2 lattice reduced the energy barrier of lithium diffusion.

**Table 13.2** Ionic conductivities (573 K) and activation energies of Li1−*y*Sc1−*xMx*O2 (*M* = Zr4+, Nb5+, or Ta5+)

**Table 13.3** Refined structural parameters of LiScO2

Unit cell: tetragonal *I*41/*amd*(141); *a* = *b* = 4.1791(18) Å and *c* = 9.3610(4) Å; *R*wp = 11.55

**Table 13.4** Refined structural parameters of Li1−*x*(Sc1−*x*Zr*x*)O2 (*x* = 0.1)

Unit cell: tetragonal *I*41/*amd*(141); *a* = *b* = 4.1804(16) Å, and *c* = 9.4186(3) Å; *R*wp = 7.28

Zr4+-doped samples showed the highest ionic conductivities, with a maximum value of 9.73 × 10−7 S cm−1 observed at 573 K. In order to verify the changes in ionic conductivities caused by Zr4+ doping, the corresponding crystal structures were evaluated in detail.

Tables 13.3 and 13.4 summarize the refinement-determined structural parameters for *x* = 0.0 and *x* = 0.1, respectively. All diffraction peaks were indexed to the *I*41/*amd*(141) space group with tetragonal symmetry, with the exception of reflections ascribed to impurities. The lattice parameters of LiScO2 were determined as *a* = *b* = 4.1791(18) Å and *c* = 9.3610(4) Å, making them nearly identical to the reported values of *a* = *b* = 4.182 Å and *c* = 9.318 Å [23]. The lattice parameters calculated for *x* = 0.1 (*a* = *b* = 4.1804(16) Å and *c* = 9.4186(3) Å) were increased by doping with Zr4+, with refinement results showing that 10% Zr4+ was doped at Sc3+ sites in the above structure, in agreement with the ratio of utilized reactants. Concomitantly, lithium vacancies were probably formed to maintain the charge balance in LiScO2, since the doped Zr4+ ion has a higher charge than Sc3+. These results demonstrate that the ionic conductivity of LiScO2 was markedly improved by substitution with certain aliovalent cations, owing to the resulting lattice expansion and formation of lithium vacancies.

#### **13.3 Development of Hydride Ion Conductors**

Hydride ion conduction is particularly attractive, as H– is similar in size to fast ionic conduction-suitable oxide and fluoride ions, while exhibiting strong reducing properties (standard H−/H2 redox potential = − 2.3 V), comparable to those of

Mg/Mg2+ (−2.4 V) (Fig. 13.5). Thus, hydride ion conductors may be applied in energy storage/conversion devices with high energy densities. To indicate a new direction for next-generation battery systems beyond lithium ion batteries and fuel cells, we herein focus on hydride ion conduction in solids.

Hydride ion conduction in CaH2 was first described by Andresen et al. in 1977 [25], with similar reports on other materials following in later years [26–31]. However, experimental evidence of H– conduction was not obtained until Irvine et al. determined the transport number of BaH2 by electromotive force measurements in 2015 [7]. Although alkaline earth metal hydrides such as BaH2 act as pure H– conductors, they are also strong reducing agents. This complicates their use as solid electrolytes of energy devices, in which electrochemical stability to both oxidation and reduction is required. Indeed, these metal hydrides have not yet been applied to battery reactions. From the viewpoint of material design, the structural inflexibility of metal hydrides complicates the control of their lattice structure (which is required to create smooth transport pathways) and their conducting hydride ion content. Thus, little progress has been achieved in the development of H– conductors. We have considered oxyhydrides, in which hydride and oxide ions share anion sublattices, as prospective hydride conductors with flexible anion sublattices. Known oxyhydrides include *A*2*B*H*x*O4–*x* (K2NiF4 structure; *A* = La, Ce, Nd, Pr, Sr; *B* = Co, V, Li; 0 < *x* ≤ 1), Sr3Co2O4.33H0.84 (Ruddlesden-Popper structure), *A*TiO3–*x*H*x* (perovskite structure; *A* = Ba, Sr, Ca) [32–37], and [Ca24Al28O64] 4+ ⋅ 4H– (mayenite structure) [38–40]. However, none of these materials display pure H– conductivity, since hydride ions have been reported to act as electron donors in oxide-based materials [38–42], donating electrons to their lattice and thus causing electron conduction accompanied by a characteristic change in hydrogen charge from H– to H+. Indeed, perovskite- and mayenite-type oxyhydrides predominantly exhibit electron conduction caused by the dissociation of hydride ions into electrons and protons [33, 38–40, 43]. Taking this into consideration, preventing the above electron donation may be important for achieving pure H– conduction in the oxide framework structure. Herein, we attempted to synthesize a series of K2NiF4-type oxyhydrides, La2–*x*–*y*Sr*x*+*y*LiH1–*x*+*y*O3–*y* (0 ≤ *x* ≤ 1, 0 ≤ *y* ≤ 2, 0 ≤ *x* + *y* ≤ 2) featuring cation sublattices that contain cations that are more electron-donating than H– and anion sublattices that allow flexible storage of H– , O2– , and vacancies.

#### *13.3.1 Hydride-Conducting Oxyhydrides La2–***X***–***Y***Sr***x***+***Y***H1–***X***+***Y***O3–***Y**

Novel La2–*x*–*y*Sr*x*+*y*LiH1–*x*+*y*O3–*y* oxyhydrides were synthesized by a high-temperature solid-state reaction in a cubic anvil cell [6] under high pressure to prevent the loss of light elements such as hydrogen, which can easily vaporize at high temperatures. The compositions and structures of La2–*y*Sr*y*LiH1+*y*O3–*y* (*y* = 0, 1, 2) were determined by X-ray and neutron Rietveld analyses (Fig. 13.6). In La2LiHO3 (*x* = *y* = 0). The two apical sites of Li*X*6 (*X* = H– , O) octahedra were occupied only by O2– , with the four in-plane apexes occupied by O2– and H– . These results indicate that highly charged cations, i.e., La3+ and Sr2+, need to be surrounded by highly charged anions. LaSrLiH2O2 (*x* = 0, *y* = 1) was composed of tetragonal (LiH2) – and (LaSrO2) + layers alternately stacked along the *c*-axis, with the further increased hydride content of Sr2LiH3O resulting in the formation of (Sr2HO)+ layers. Considering the above series of compositions, it should be noted that there exists a K2NiF4-type H– –free oxide, La2LiO3.5, in which the anion vacancies are randomly distributed in basal (LiO0.75) 0.5– layers [44].

Remarkably, *t*-La2LiHO3 contains anion vacancies (V(H,O)), which are best represented as La2Li(H0.53O1.21V(H,O)0.26)O2 and exhibit H– , O2– , and V(H,O) disorder at the axial sites of Li*X*6 octahedra. By contrast, the orthorhombic phase, *o*-La2LiHO3, is stoichiometric, with H– and O2– located in axial anion sites. This symmetry change can be attributed to the order–disorder transition of H– and O2– in axial sites, both with and without vacancies. The crystal structures of the anion-deficient series, La2–*x*Sr*x*LiH1–*x*O3 and La1–*x*Sr1+*x*LiH2–*x*O2, were also determined by Rietveld analysis. Representative results obtained for

**Fig. 13.6** Crystal structures of t-La2LiHO3 and La2–*y*Sr*y*LiH1+*y*O3–*y* (*y* = 0, 1, 2)

**Fig. 13.7** Neutron Rietveld analysis of anion-deficient La0.7Sr1.3LiH1.7O2 (*x* = 0.3, *y* = 1)

La0.7Sr1.3LiH1.7O2 (*x* = 0.3, *y* = 1) are shown in Fig. 13.7 and Table 13.5. The H and O occupancy parameters were calculated as *g*H1 = 0.938(2), *g*H2 = 0.118(3), and *g*O1 = 0.882(3), leading to a composition of La0.7Sr1.3Li(H1.88V(H)0.12) H0.24O1.76. Thus, doping resulted in the generation of vacancies in the LiH4 plane and caused H– /O2– anion mixing at apical sites.

The valence states of all constituent atoms in La2LiHO3, LaSrLiH2O2, and Sr*2*LiH3O were determined by valence charge integration over the corresponding Voronoi cells (Table 13.6). The valences of hydrogen and oxygen in all materials were estimated at approximately –0.8 to –1.0 and –1.3 to –1.6, respectively, indicating that these elements were present as H– and O2– . Electronic density of states calculations corroborated the presence of hydride ions (Fig. 13.8), with their localized electrons located between approximately 0 and –5 eV below the Fermi level, confirming the ionic nature of the Li–H– bond.

#### *13.3.2 Hydride Ion Conductivity of La2–***X***–***Y***Sr***x***+***Y***H1–***X***+YO3–***Y**

The ionic conductivities of La2–*x*–*y*Sr*x*+*y*LiH1–*x*+*y*O3–*y* were examined by impedance measurements. The Arrhenius plots of conductivities are shown in Fig. 13.9. In the case of La2-*y*Sr*y*LiH1+*y*O3-*y* (*x* = 0), conductivity increased with increasing H– content, with the highest value of 3.2 × 10−5 S cm−1 at 573 K observed for Sr2LiH3O (*y* = 2) (Fig. 13.9a). Thus, introduction of hydride ions into the anion sites of the K2NiF4 structure improved ionic conductivity, confirming that these ions were primary charge carriers. Conduction was further facilitated by the introduction of vacancies, indicating that structural defects can affect ionic diffusion, as can be seen for La2–*x*Sr*x*LiH1–*x*O3 (*y* = 0) and La1–*x*Sr1+*x*LiH2–*x*O2 (*y* = 1),

**Table 13.5** Rietveld refinement results for La0.7Sr1.3LiH1.7O2 (*x* = 0.3, *y* = 1)

Unit cell: tetragonal *I*4/*mmm*, *a* = 3.65672(4), *c* = 13.3066(2) Å

Phase 2: Li2O

with conductivities of up to 2.1 × 10−4 S cm−1 observed for La0.6Sr1.4LiH1.6O2 at 590 K (activation energy ∼ 68.4 kJ mol−1 ) (Fig. 13.9b, c).

To further identify the nature of the charge carriers, the electrical conductivity of La0.6Sr1.4LiH1.6O2 (*x* = 0.4, *y* = 1.0) was evaluated by the Hebb-Wagner polarization method [45] at 480 and 590 K using an asymmetric (–) Pd/ La0.6Sr1.4LiH1.6O2/Mo (+) cell, with the total electrical conductivities (electrons + holes) at the irreversible Mo-electrolyte interface (2.9 × 10−8 and 4.1 × 10−7 S cm−1 , respectively) showing that La0.6Sr1.4LiH1.6O2 is a purely ionic conductor (Fig. 13.10 and Table 13.7).

**Fig. 13.8** Electronic densities of states for La2–*y*Sr*y*LiH1+*y*O3-*y* (*y* = 0, 1, 2) determined by first principles calculations

**Fig. 13.9** Temperature-dependent ionic conductivities of La2–*x*–*y*Sr*x*+*y*H1–*x*+yO3–*y*. **a** La2–*y* Sr*y*LiH1+*y*O3–*y* (*x* = 0, *y* = 0, 1, and 2) with a fixed cation/anion ratio of (A2B)/X4, where A, B, and X are La(Sr), Li, and O(H), respectively. Anion-deficient series: **b** La2–*x*Sr*x*LiH1–*x*O3 (*y* = 0, 0 ≤ *x* ≤ 0.2) and **c** La1–*x*Sr1+*x*LiH2–*x*O2 (*y* = 1, 0 ≤ *x* ≤ 0.4)

#### *13.3.3 Development of Electrochemical Devices Based on Hydride Ion Conduction*

To verify the occurrence of H– conduction in La2–*x*–*y*Sr*x*+*y*LiH1–*x*+*y*O3–*y*, we constructed a Ti/*o*-La2LiHO3/TiH2 all-solid-state cell and subjected it to galvanostatic discharge, with an electrode configuration (powdered mixture of electrode and electrolyte materials) similar to that previously used in an all-solid-state lithium battery [46]. Figure 13.11a shows the discharge curve of the cell, revealing a constant discharge current of 0.5 μA at 300 °C. Moreover, the cell showed an initial open circuit voltage of 0.28 V, which was consistent with the theoretical value calculated from the standard Gibbs energy of formation of TiH2 [47]. During

**Fig. 13.10** Hebb-Wagner polarization curves of the (–) Pd/La0.6Sr1.4LiH1.6O2/Mo (+) cell at **a** 590 K and **b** 480 K

**Table 13.7** Partial conductivities (*σ*) and transference numbers (*t*) at the irreversible Mo/ La0.6Sr1.4LiH1.6O2 interface of the asymmetric (–) Pd/La0.6Sr1.4LiH1.6O2/Mo (+) cell at different temperatures. Subscripts *e* and *h* denote electrons and holes, respectively

the electrochemical reaction, the cell voltage rapidly dropped from 0.28 to 0.06 V and then gradually decreased to 0.0 V. The initial steep drop corresponded to an increase in hydride ion content at the anode, owing to the following constant current discharge reaction:

$$\text{Ti} + \text{xH}^- \rightarrow \text{TiH}\_x + \text{xe}^-$$

with the cathode reaction represented as:

$$\text{TiH}\_2 + \text{xe}^- \rightarrow \text{TiH}\_2\text{-}\_\text{x} + \text{xH}^-$$

The occurrence of these discharge reactions was confirmed by analysis of the produced phases. Figure 13.11b shows the synchrotron X-ray diffraction patterns of the cathode, electrolyte, and anode materials before and after the reaction. The absence of any variation in the diffraction patterns of the electrolyte indicates that the La2LiHO3 electrolyte was stable in contact with the Ti and TiH2 electrodes

**Fig. 13.11** All-solid-state cell fabricated for verification of H− conduction in La2–*x*–*y*Sr*x*+*y*LiH1–*x*+*y* O3–*y*. **a** Discharge curve for a Ti/*o*-La2LiHO3/TiH2 solid-state battery, with the inset showing a schematic illustration of the cell and the proposed electrochemical reaction. **b** X-ray diffraction patterns of electrolyte (*o*-La2LiHO3), cathode (TiH2 + *o*-La2LiHO3), and anode (Ti + *o*-La2LiHO3) materials after the reaction; the two right panels show expanded ranges of 13–13.8° and 15.1–15.8°

during the reaction. Conversely, phase changes were observed for the cathode and anode materials, as expected from the Ti-H phase diagram [47], where the δ-TiH2 (*Fm* 3 m) phase releases hydrogen and is transformed into α-Ti (*P*63/*mmc*), passing through a two-phase (α-TiH*b* + δ-TiH2-*a*) coexistence region found below ∼ 573 K. In the case of the cathode, additional diffraction peaks corresponding to species with *P*63/*mmc* symmetry were detected, and the signals of TiH2 shifted to a higher angle, indicating that the release of hydrogen from TiH2 induced lattice shrinkage. In the case of the anode, peaks corresponding to species with *Fm* 3̄m symmetry were detected. Thus, the results indicate that during the electrochemical reaction, hydride ions were released from the TiH2 cathode and diffused into the Ti anode through *o*-La2LiHO3. The present success in the construction of an all-solid-state electrochemical cell exhibiting H– diffusion confirms not only the ability of oxyhydrides to act as H– solid electrolytes, but also the possibility of developing electrochemical solid devices based on H– conduction.

#### *13.3.4 Ambient-Pressure Synthesis of H– -Conductive Oxyhydrides*

The abovementioned high-pressure method is efficient for synthesizing oxyhydrides, owing to its ability to inhibit hydrogen desorption from the starting materials during sintering. However, in order to apply H– conductors to electrochemical devices, a simple synthetic protocol needs to be established for oxyhydrides, in parallel with the development of highly H– -conductive novel materials. Here, we

describe the synthesis of LaSrLiH2O2 by a conventional solid-state reaction under ambient pressure and characterize its electrochemical properties.

The starting materials (which were identical to those used in the high-pressure method) were pelletized and placed in a sealed sample container made of stainless steel, with subsequent sintering performed at 650 °C for 6 h under H2.

Figure 13.12 shows the X-ray diffraction patterns of LaSrLiH2O2 synthesized using different amounts of LiH (stoichiometric, 20, 50, and 100 wt% excess), with the main diffraction peaks corresponding to the space group of LaSrLiH2O2, i.e., *I*4/ *mmm*. However, small diffraction peaks indexed to SrO, SrH2, and/or La2O3, which were present in the raw starting materials, were observed for samples synthesized using a small excess or no excess of LiH (stoichiometric, 20 wt%, and 50 wt%). The amount of residual starting materials decreased as the amount of LiH increased, with LaSrLiH2O2 obtained as a single phase only at a 100 wt% excess. In addition, excess LiH improved the crystallinity of LaSrLiH2O2, i.e., the magnification of the normalized 004 peaks (Fig. 13.12) showed that their full width at half maximum decreased as the amount of LiH increased. Therefore, excess LiH not only prevented the loss of lithium and hydrogen during the synthesis of LaSrLiH2O2, but also acted as a flux for reducing the synthesis temperature.

Crystal structure analysis revealed that the sample prepared under ambient pressure had almost the same structure as the high-pressure one, with the refined site occupancies of each atom indicating that the former exhibited a nearly stoichiometric composition without vacancies. However, mixing of H− and O in the 4*c* axial anion site (*g*(H1) = 0.9361(5) and *g*(O1) = 1 − *g*(H1)) in Li octahedra

profiles of LaSrLiH2O2

and **d** 100 wt% excess

amounts of LiH:

was detected for the ambient-pressure sample, whereas this site was exclusively occupied by H− in the high-pressure sample.

The ionic conductivity of LaSrLiH2O2 synthesized at ambient pressure was evaluated by AC impedance measurements. The corresponding impedance and Arrhenius plots are shown in Fig. 13.13, with the conductivity of the high-pressure LaSrLiH2O2 synthesized in our previous study also plotted for comparison [6]. The impedance plot exhibited a typical form, comprising a semicircle in the high-frequency range and a spike in the low-frequency range, which corresponded to contributions of the bulk and grain boundary and the electrode, respectively. The former contribution was estimated by fitting impedance spectra using an equivalent circuit, as shown in Fig. 13.13. For the ambient-pressure sample, the activation energy of ionic conduction was calculated as 80.7 kJ mol−1 , which is nearly equal to that observed for the high-pressure sample [6]. The total conductivity (bulk + grain boundary) of the ambient-pressure sample was determined as 3.2 × 10−6 S cm−1 at 300 °C, slightly less than that of the high-pressure sample. Given the crystal structure of LaSrLiH2O2, in which tetragonal (LiH2) − and (LaSrO2) + layers are alternately stacked along the *c*-axis, the hydride ions were expected to exhibit two-dimensional diffusion in the LiH4 plane. Hence, the movement of H− in the crystal lattice of ambient-pressure LaSrLiH2O2 may have been inhibited by the presence of oxide ions in the (LiH2) − layer.

Thus, we successfully synthesized LaSrLiH2O2 by a conventional solid-state reaction under ambient pressure [48], with a two-fold molar excess of LiH required to obtain single-phase LaSrLiH2O2. The sample synthesized at ambient pressure exhibited a crystal structure and H− conductivity similar to those observed for the high-pressure sample, implying that the method described here should increase the applicability of H− conductors as solid electrolytes.

#### **13.4 Concluding Remarks**

This chapter outlined the properties of ion conductors and material search methods, introducing Li+ and H− conductors and providing examples of material search (e.g., element substitution and structure-based methods). However, broader material variability will be required to fabricate viable electrochemical devices based on solid electrolytes, necessitating the utilization of composition-based material search, which is one of the conventional material discovery methods. The approach described is significantly influenced by the experience and intuition of researchers, and it generally takes longer than element substitution and structure-based methods. However, the recent development of theoretical calculation and material informatics methods is expected to shorten the time required [49–53], allowing high-speed screening of prospective compositions/structures. In some cases, this approach might be misleading, since not all theoretically predicted compositions or structures can be obtained by the present synthetic techniques, as exemplified by the failure of the composition/structure-based search in the case of Li+ and H− conductors. Thus, the area of materials informatics for composition-based material search is still in its infancy, but it holds promise for the future.

**Acknowledgements** This research was supported by JST, PRESTO, and Grant-in-Aid for Young Scientists (A) no. 15H05497 and (B) no. 24750209; Grant-in-Aid for Challenging Exploratory Research no. 15K13803, 23655191, and 25620180; and Grant-in-Aid for Scientific Research on Innovative Areas no. 25106005 and 25106009, from the Japan Society for the Promotion of Science. Synchrotron and neutron radiation experiments were carried out as four projects approved by the Japan Synchrotron Radiation Research Institute (JASRI) (proposals no. 2013A1704, 2015A1778, and 2015B1768), the Japan Proton Accelerator Research Complex (J-PARC) (proposal no. 2010A0058), the Spallation Neutron Source (SNS) in the Oakridge National Laboratory (proposal no. IPTS5808 and 10030), and the Neutron Scattering Program Advisory Committee of IMSS, KEK (proposal no. 2014S10). Part of the neutron experiments (proposal no. 2014S10) was performed at the BL09 Special Environment Neutron Powder Diffractometer (SPICA) developed by the Research and Development Initiative for Scientific Innovation of New Generation Batteries (RISING) project of the New Energy and Industrial Technology Development Organization (NEDO). Supercomputing time at the Academic Center for Computing and Media Studies (ACCMS) at Kyoto University is gratefully acknowledged. Further information regarding the materials and methods is included in the supplementary materials.

#### **References**

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3.1.1. Emission Data

Due to the location of the studied area (direct neighborhood of Poland, Czechia and Germany), and the resulting problems with the unification of emission databases, data from three sources described below were used for more comprehensive analyses.

In the area of Poland, data from a detailed emission inventory were used for annual air quality assessments performed up to 2017 by the Voivodship Inspectorate for Environmental Protection in Wrocław (currently the Regional Department of Environmental Monitoring of the Chief Inspectorate for Environmental Protection) [28,29]. This database contains data on all types of emissions from the voivodship, including emissions from residential heating (SNAP0202), transport (SNAP07), agriculture (SNAP10) and industry (SNAP0201, SNAP01, SNAP03, SNAP04). Inventories took into account the activity of sources. Due to the fact that the above database has been created continuously since 2012, data contained therein can be considered of a good quality. The database was additionally validated with annual model calculations [30].

In the calculations, an indispensable element is capturing the temporal and spatial variability of the emission field, because such changes together with the variability of meteorological conditions practically determine the final distribution of pollution concentrations in the studied area. Considering the temporal variability of emissions can also capture the cases of high concentration episodes and link them to the types of emissions responsible for poor air quality. Therefore, the concentration calculations were implemented in an emission model, which includes primarily variations dependent on changes in meteorological conditions (e.g., temperature for emissions from residential heating and precipitation for emissions from transport, agriculture) or the mode of source operation. This methodology was used, among others, in modeling air quality for the needs of annual air quality assessments in the Lower Silesian Voivodship [29]. The results of the calculations obtained in the context of the balance of specific sources in PM2.5 and PM10 emissions in Bogatynia commune are presented in Figure 4.

**Figure 4.** Particulate matter emissions for different emission sources inventoried in Bogatynia commune (source: own study).

The performed analyses show that Turów power complex, particularly the mine, has the largest share in the total PM10 emission in Bogatynia commune. In the case of PM2.5 emissions, the communal and household sector is clearly the main source of emissions in the studied area. While emissions from household sources, or from transport, can be considered a seasonal variable (heating and post-heating season, daily and weekly transport cycle) and of a quasi-uniform nature, the emission associated with industrial sources may be characterized by short-term changes and be dependent on a number of factors resulting from, e.g., technology, specificity of the sources or a specific mode of operation of an industrial installation. Therefore, considering the impact of industrial sources on air quality, it was necessary to build an emission model dedicated to the examined facility (in this case, ELT), which was presented in the report on the environmental impact of the KWBT [23].

In the case of the ELT power plant, organized emissions resulting mainly from the boiler operating conditions were taken into account. In addition, in the case of ELT, fugitive emission sources were considered, i.e., combustion waste buffer area, where ash from boilers is temporarily stored before being mixed with overburden in an open pit. This happens when it cannot be collected by the mine (KWBT). Emissions for this source were determined on the basis of United States Environmental Protection Agency (US-EPA) emission factors adapted to the characteristics of the source in terms of quantity and quality of the stored material, including machine operation [23]. Examples of emission values included in the calculation scenarios are presented in Table 2.

**Table 2.** PM10 and PM2.5 emission from ELT in 2018.

In the case of KWBT, four basic groups of emission sources were identified: emissions related to the mining process, storage and reloading of coal, transport and entrainment of material from exposed surfaces. The emissions related to the mining process covered the exploitation area (northern part of the open pit) and the backfilling area (southern part of the open pit). It was assumed that the volume of emissions from the exploitation area compared to the backfilling area would be significantly lower due to the quarried material, which is very moist and heavy. In the backfilling area, the quarried material is much more volatile because it consists mainly of overburden loam and ash from power plant boilers. Emissions related to storage and reloading concerned a coal bin located in the north of the facility. In the area of the bin, there are two trenches in which higher quality coal is deposited and two coal sales points—wholesale and retail. In addition to emissions from coal loading and unloading, emissions associated with wind entrainment are an important factor in this case. The last group is formed by emissions related to transport, both through belt conveyors and through vehicles and railways (wholesale and retail). The emission model for the mine was based on indicators determined with reverse modeling, US-EPA methodologies, considering the specificity of meteorological conditions and mineral material mined and backfilled in the open pit, and the time of operation of the machines [23,30]. Examples of emission values included in the calculation scenarios are presented in Table 3.

**Table 3.** PM10 and PM2.5 emission from ELT in 2018 by sector (on the basis of [23,30]).

The last group of data is information on emissions from Czechia and Germany from the emission inventory posted on the public websites of the Czech Hydrometeorological Institute and the German Ministry of the Environment [31,32]. In Germany, the inventory included sources related to residential heating (SNAP 0202) and SNAP 07 road transport (also SNAP 08 other transport), and the total emission of PM10 and PM2.5 was 44.6 Mg. For Czechia, the data concerned industry (SNAP01, SNAP03, SNAP04), residential heating (SNAP0202) and road transport (SNAP07). Total emissions in the studied area are around 182 Mg for PM10 and 169 Mg for PM2.5.

#### 3.1.2. Validation of Modeling Results in the Base Year

The obtained calculation results were validated using the available measurement data from measuring stations located in Poland, Czechia and Germany (Figure 3, Table 2). The stations were selected based on the availability of results in publicly available databases (e.g., AIRBASE, EEA European Air Quality Portal, JPOAT via the Air Quality Portal of the Chief Inspectorate for Environmental Protection). It was also assumed that the completeness of the measurement series must meet the requirements of the CAFE directive [1]. Ultimately, the analysis included measurements from six stations: four in Poland (Wyszków, Jasna Góra, Bogatynia, Działoszyn), one in Czechia (Frýdlant) and one in Germany (Zittau), the location of which is shown in Figure 5. Three of the

selected stations located in Poland belong to the measurement network of ELT, and the station in Działoszyn is included in the network of the State Environmental Monitoring (SEM) run by the Chief Inspectorate of Environmental Protection under the national code DSDzialoszyn. The stations in Zittau and Frýdlant are national stations of the German and Czech networks.

**Figure 5.** Location of measuring stations (source: own study).

The relative error rate of the modeling result in relation to the measurement was the basic measure of the correctness of the results of model calculations. According to the CAFE directive, this parameter for particulate matter (including PM10 and PM2.5) should not exceed 50% for annual mean values [1]. Negative values indicate an underestimation of concentrations.

The values obtained as a result of the comparison allowed us to determine the uncertainty of the model (relative errors) and are listed in Table 4. In most cases, the uncertainty of the model did not exceed 13%. The highest compliance was obtained at the station in Bogatynia (for both PM10 and PM2.5) and at stations in Zittau and Frýdlant, where the relative errors did not exceed 5%. The highest 32% underestimation of results was obtained at the station in Działoszyn. At the same time, it can be stated that a match of results was much better for PM2.5 concentrations than for PM10.

**Table 4.** Annual mean concentrations of PM10 and PM2.5 modeling results and measurement.

#### 3.1.3. Emission Change Scenarios

The assessment of the e ffectiveness of planned long-term actions or ad hoc measures limiting the emission of pollutants is carried out, among others, through changes in the emission introduced into the model, activity of sources and through a possible modification of the original assumptions regarding, e.g., operating mode or frequency of preventive measures used. These changes may concern both the volume of emission loads and the modification of time variations. Then, based on the new emission values, re-calculations were made, assuming no changes for the remaining emission data and/or meteorological parameters. This study discusses three scenarios for emission changes. The first scenario is related to changes in the volume of emissions from the mine, indicated in the report on the mine's Environmental Impact Assessment [23]. The second scenario evaluates the e ffectiveness of implementing the anti-smog resolution in force in the Lower Silesian Voivodship, which will result in a deep modification in the profile of residential heat sources [11]. The last scenario is an assessment of the implementation of both previously mentioned scenarios. The data included in the calculations for the three assumed scenarios are summarized in Table 5.

**Table 5.** Emission for individual sources included in the calculations for individual scenarios.

As a result of the analyses presented in the report on the environmental impact of the mine [23], in order to avoid a significant impact of the object on the neighboring areas, it was necessary to indicate possible additional mitigation measures (Table 6).

In addition, in 2019, a new ash conveyor was created directly from the power plant to the open pit, which will eliminate the impact of the storage area. It was estimated that, as a result of the implemented measures, the total emissions from the mine will be reduced from 935.4 Mg by approximately 44% for PM10 and from 363.6 Mg by approximately 43% for PM2.5 and will be 521.5 Mg and 207.9 Mg, respectively.

Another important change in the emission characteristics of the commune is the implementation of the anti-smog resolution, according to which, by 2028, boilers of a class lower than 5 will not be operating in the entire Lower Silesian Voivodship. Within the voivodship, it is allowed to burn solid fuels in devices from which "particulate matter emissions do not exceed the emission threshold values set out in Commission Regulation (EU) 2015/1189 of 28 April 2015 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to ecodesign requirements for solid fuel boilers" [12]. The implementation of the resolution is estimated to result in around a 95% reduction in dust pollution. Total PM10 emissions are to be reduced from 452.8 to around 19 Mg and PM2.5 from 394.3 to 18.1 Mg. The values of emission totals and the scale of emission reduction were estimated on the basis of an emission database from a detailed inventory of emissions prepared for annual air quality assessments carried out until 2017 by the Voivodship Inspectorate for Environmental Protection in Wrocław (currently the Regional Department of Environmental Monitoring of CIEP). This database contains, among others, information on surfaces heated using old-type boilers fed with coal and wood.

#### *3.2. Health Risk Assessment*

The health risk assessment associated with long-term (annual) PM2.5 exposure for the analyzed scenarios was performed using the dose–response function and relative risk index (RR (95% CI) = 1.062 (1.040–1.083) for every 10 μg/m3) [33,34].

The crucial element of health risk assessment for the analyzed area was accurate mapping of the exposure of the population at risk. For this reason, the health assessment was performed for each of the separated areas adopted as the air quality model grid, based on obtained results of the level of annual mean PM2.5 concentration and demographic indicators assigned to the model grid (Figure 6). The number of exposed people and the number of deaths classified by causes were determined on the basis of data from official statistics with a division of the population into age groups [35]. For more detailed analysis, data from spatial distribution were used, considering the current administrative division of the country according to Nomenclature of Territorial Units for Statistics (NUTS) level 5 and the state register of administrative borders [36–39]. For this purpose, among others, the dasymetric map of population density developed by the European Environment Agency—raster layer "Population density grid of EU-27+, version 4 and 5"—was used. The dasymetric map and population density were corrected in line with the current land use information. To verify the population in the model grid, the National Official Register of the Territorial Division of the Country was used with the distinction of towns with district rights, urban, rural and urban-rural communes as well as towns and rural areas in urban-rural communes.

The analyses were performed for the baseline condition without considering emission reduction scenarios and for each scenario separately. The results are presented both in the form of the estimated number of premature deaths related to the exposure of the general population to PM2.5 and relative changes in the impact assessment for each scenario.

**Figure 6.** Map of the population density distribution (source: own study).


**1. Introduction**

Guatemala has the largest population among Central American countries (over 17 million) and is bordered by Mexico, Belize, Honduras, and El Salvador [1]. While considered an upper-middle-income country due to a GDP of \$4603 per capita in 2020 [1], Guatemala has marked inequalities, with wealth held in a small sector of the population, low access to basic services for much of the population, and an overall lack of investment in the public sector [1–3].

Guatemala has the lowest COVID-19 vaccination coverage in Central America and is among the lowest compared with its regional neighbors in South America [4,5]. As of November 2022, there have been over one million SARS-CoV-2 cases and nearly 20,000 COVID-19-related deaths reported in Guatemala [6,7]. Within Guatemala, it was estimated that COVID-19-related mortality has been higher in people aged 60–69 years and in frontline healthcare workers [8]. As part of the National Vaccination Plan Against COVID-19, frontline healthcare workers were prioritized for vaccines when they initially became available in March 2021 [8,9]. In later phases, people aged 50 years and older and those

**Citation:** Choudhary, R.; Carter, E.; Monzon, J.; Stewart, A.; Slotnick, J.; Samayoa Jerez, L.L.; Rodriguez Araujo, D.S.; Zielinski-Gutierrez, E.; Suchdev, P.S. Sociodemographic Factors Associated with COVID-19 Vaccination among People in Guatemalan Municipalities. *Vaccines* **2023**, *11*, 745. https://doi.org/ 10.3390/vaccines11040745

Academic Editors: Ahmad Reza Hosseinpoor, M. Carolina Danovaro, Devaki Nambiar, Aaron Wallace and Hope Johnson

Received: 6 March 2023 Revised: 22 March 2023 Accepted: 25 March 2023 Published: 28 March 2023

**Copyright:** © 2023 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/).

with underlying medical conditions were prioritized [8,9]. Vaccines became available free of charge to the general public in May 2021 [8,9]. Vaccines were obtained through donations, via the COVID-19 Vaccines Global Access (COVAX) mechanism for which Guatemala was a "self-pay" country, and one bilateral purchase of the Sputnik vaccine [9]. To date, there are four COVID-19 vaccines available to Guatemalans and all of them are two-dose primary series courses (Pfizer/BioNTech, Oxford/AstraZeneca, Moderna, and Sputnik V) [10]. As of 30 November 2022, 19,960,793 COVID-19 vaccines had been administered, with 49.4% of the total population having received at least one dose, and approximately seven million people, or 40.0% of the total population, having completed a primary COVID-19 vaccination series with two doses [4,6,7,10]. Of those who had completed a primary vaccination series, 42.8% had received Moderna, 23.0% received Oxford/AstraZeneca, 18.4% received Sputnik V, and 15.9% had received Pfizer/BioNTech vaccines [11]. In addition, about four million people, or 24.0% of the population, had received one or two booster doses by 30 November 2022 [12].

The inequity in the global distribution of COVID-19 vaccines with preferential access for countries with higher per capita incomes and gross domestic products has been welldocumented [13–15]. However, there are limited studies describing COVID-19 vaccination disparities within low- and middle-income countries, and many focus on vaccination intent. Studies that have explored sociodemographic factors associated with COVID-19 vaccination coverage have primarily focused on the United States. One survey conducted among U.S. adults found that participants with lower incomes, lower educational attainments, those without health insurance, who were non-Hispanic Black, and who lived outside of metropolitan areas had the lowest reported COVID-19 vaccination coverage and intent to get vaccinated [16]. Two other analyses showed that vaccination coverage was lower in rural compared with urban U.S. counties [17], and lower in counties with a higher percentage of people with incomes below the poverty threshold, experiencing unemployment, and not graduating from high school [18].

Several reasons have been posited for the low COVID-19 vaccination coverage in Guatemala. The country faces multiple challenges in its healthcare and public health system such as inadequate financing of the health sector, disparities in access to public health services in rural areas, and a shortage of healthcare workers [19,20]. COVID-19 vaccination coverage has been highest in the capital, Guatemala City, while rural areas with higher concentrations of Indigenous people have had lower vaccination coverage [5,21]. In the 2018 census, the Maya comprised 41.7% of the total population, and the Xinca were 1.8% of the total population [22]. The disproportionate burden of COVID-19 on Indigenous people and those of low socioeconomic status has been studied regionally, for instance, in Colombia [23]. A 2021 UNESCO report on COVID-19 vaccination in Latin America and the Caribbean noted that in communities with higher "unemployment or informal employment, or where ethnic groups live, there is a higher prevalence of COVID-19 and a higher risk of mortality" [24]. Moreover, there is evidence of vaccine hesitancy among people with lower levels of institutional trust, those living in rural areas, and those experiencing economic insecurity [25–27]. Early vaccination outreach in Guatemala was often conducted in Spanish using mainstream media instead of through local organizations, and using local Indigenous languages, according to a Pan American Health Organization report [5]. As part of the Ministry of Public Health and Social Assistance (MSPAS) *Strategy to Strengthen the COVID-19 Vaccination Plan in Rural Areas*, vaccination activities have more recently incorporated community leader guidance and local media campaigns [28].

Given the limited studies describing COVID-19 vaccination coverage disparities within Guatemala, we performed an ecological analysis to understand the association between sociodemographic factors and primary vaccination coverage among the 340 Guatemalan municipalities. Identifying factors associated with low vaccination could inform strategies to improve COVID-19 vaccine coverage and other public health interventions in Guatemala.