Synthesis, structural and conductive properties of Nd doped garnet-type Li7La3Zr2O12 Li-ion conductor

Saran, Sevda; Eker, Yasin Ramazan

doi:10.1016/j.cap.2022.06.004

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…Among the elements fulfilling both cases, except for La, there are prior studies in which Nd and Y have already been doped to the LLZO structure. 60,61 In general, Y is doped to the Zr site, but the DFT calculation results in this study show that the doping of Y to the La site outperforms. However, Tm was not found in the case of doping or substitution of LLZO in the previous literature, to the best of our knowledge.…”

Section: ■ Results and Discussionmentioning

confidence: 66%

“…First, analyzing the La site, elements V, La, Mo, Sm, Pm, Ac, Fe, Tm, Nd, Tb, Pr, Y, Rb, Dy, and Tl belong to the top 15 dopants out of 758 materials, as shown in Figure a, and the final 10 candidates are shown in Figure b. Among the elements fulfilling both cases, except for La, there are prior studies in which Nd and Y have already been doped to the LLZO structure. , In general, Y is doped to the Zr site, but the DFT calculation results in this study show that the doping of Y to the La site outperforms. However, Tm was not found in the case of doping or substitution of LLZO in the previous literature, to the best of our knowledge.…”

Section: Resultsmentioning

confidence: 99%

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Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Sun

Kang

Kim

et al. 2023

ACS Appl. Mater. Interfaces

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All-solid-state batteries (ASSBs) have attracted considerable attention because of their higher energy density and stability than conventional lithium-ion batteries (LIBs). For the development of promising ASSBs, solid-state electrolytes (SSEs) are essential to achieve structural integrity. Thus, in this study, a machine-learning-based surrogate model was developed to search for ideal garnet-type SSE candidates. The well-known Li 7 La 3 Zr 2 O 12 structure was used as a base material, and 73 chemical elements were substituted on La and Zr sites, leading to 5329 potential structures. First, the elasticity database and machine learning descriptors were adopted from previous studies. Subsequently, the machine-learning-based surrogate model was applied to predict the elastic properties of potential SSE materials, followed by first-principles calculations for validation. Furthermore, the active learning process demonstrated that it can effectively decrease prediction uncertainty. Finally, the ionic conductivity of the mechanically superior materials was predicted to suggest optimal SSE candidates. Then, ab initio molecular dynamics simulations are followed for confirmation of diffusion behavior for materials classified as superionic; 10 new tetragonal-phase garnet SSEs are verified with superior mechanical and ionic conductivity properties. We believe that the current model and the constructed database will become a cornerstone for the development of next-generation SSE materials.

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Section: ■ Results and Discussionmentioning

confidence: 66%

Section: Resultsmentioning

confidence: 99%

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Sun

Kang

Kim

et al. 2023

ACS Appl. Mater. Interfaces

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show abstract

“…Atoms cannot vibrate in phase in the disordered system due to the altered local symmetry of the crystals, which causes a shift in the vibrational modes and a widening of the profile. [ 26,41,44,47 ] The C‐LLZO sample obtained after re‐heating at 650 °C has shown peak profile like samples reheated at 150 °C as shown in Figure 6c.…”

Section: Resultsmentioning

confidence: 99%

Investigation of Low‐Temperature Synthesis Strategy for a Cubic Phase Li₇La₃Zr₂O₁₂

Alam,

Das

2023

Cryst. Res. Technol.

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In the present work, a solid‐state reaction (SSR) method is used to prepare a cubic phase Li7La3Zr2O12 (C‐LLZO) via a two‐step process from tetragonal phase Li7La3Zr2O12 (T‐LLZO). To identify the tetragonal to cubic phase change region a thermogravimetric analysis (TGA) of the precursor material is carried out. Subsequently, another TGA analysis is carried out for tetragonal sample to identify tetragonal to cubic phase transition regions. TGA of tetragonal sample shows multiple phase transition regions at ≈250°, 400°, and 610 °C. A series of T‐LLZO samples are reheated to obtain C‐LLZO. The T‐LLZO samples reheated at 150 °C, and 650 °C show cubic symmetry, whereas the sample heated at 450 °C and 950 °C show mixed phase. The obtained C‐LLZO samples have lattice constant a = b = c = 12.9389 Å for 150 °C heated sample and a = b = c = 12.9375 Å for 650 °C heated samples with space group Ia‐3d. Considering the TGA of the T‐LLZO sample and XRD analysis of the T‐LLZO samples reheated at different temperatures, the transition at ≈150 °C is attributed to the hydration process of T‐LLZO and 650 °C is due to structural deformation of LLZO.

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“…Since the relative intensity of the M-O peak can be correlated to the doping concentration, the intensity ratio of the M/Zr bands can potentially provide a good estimate on the concentration of the doping element in LLZO, where a higher intensity ratio indicates a higher doping concentration. 23,67,68 The M/Zr peak intensity ratios obtained in this study are provided in Table 5 along with the related dopant concentrations. These values can be used as a good starting point to determine the concentrations of specific doping elements in future studies.…”

Section: Materials Advances Papermentioning

confidence: 99%

Supervalent doping and its effect on the thermal, structural and electrochemical properties of Li₇La₃Zr₂O₁₂ solid electrolytes

Košir,

Mousavihashemi,

Suominen

et al. 2024

Mater. Adv.

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Synthesis, structural and conductive properties of Nd doped garnet-type Li7La3Zr2O12 Li-ion conductor

Cited by 13 publications

References 52 publications

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Investigation of Low‐Temperature Synthesis Strategy for a Cubic Phase Li₇La₃Zr₂O₁₂

Supervalent doping and its effect on the thermal, structural and electrochemical properties of Li₇La₃Zr₂O₁₂ solid electrolytes

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Synthesis, structural and conductive properties of Nd doped garnet-type Li7La3Zr2O12 Li-ion conductor

Cited by 13 publications

References 52 publications

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Investigation of Low‐Temperature Synthesis Strategy for a Cubic Phase Li7La3Zr2O12

Supervalent doping and its effect on the thermal, structural and electrochemical properties of Li7La3Zr2O12 solid electrolytes

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Product

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Investigation of Low‐Temperature Synthesis Strategy for a Cubic Phase Li₇La₃Zr₂O₁₂

Supervalent doping and its effect on the thermal, structural and electrochemical properties of Li₇La₃Zr₂O₁₂ solid electrolytes