Optimization of electrical conductivity in the Na2O‐P2O5‐AlF3‐SO3 glass system

Calahoo, Courtney; Xia, Yang; Buchheim, Johannes; Bragatto, Caio Barca; Wondraczek, Lothar

doi:10.1111/jace.17150

Cited by 11 publications

(6 citation statements)

References 28 publications

(74 reference statements)

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“…A similar result was also found in our previous study as well, where increasing the NaF concentration up to 7.5 mol % gives an increasing trend in the conductivity value. This suggests that simply increasing the sodium content does not necessarily result in increased sodium conductivity . Rather, the specific batch component used to feed sodium into the glass composition plays a major role in governing the glass structure and, hence, the conductivity of glasses.…”

Section: Resultsmentioning

confidence: 99%

“…The conductivity of a material depends mainly on the charge carrier concentration and the mobility of cations. In general, electrolytes are divided into strong or weak depending on whether the conductivity is governed by ion mobility or concentration of charge carriers. , Studies claim that doping sodium halide components enhances Na-ion conductivity in glasses. ,− Structural modification can also improve the conductivity of glass materials by enhancing the mobility of cations. ,− Thus, ionic conductivity in glass materials can be improved by optimizing the Na-ion concentration or modifying the glass’s network structure. Due to the poor understanding of the composition–structure–ionic conductivity relationships in glasses, a trial-and-error method is widely adopted for developing promising Na-ion electrolyte candidates.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Assisted Design of Na-Ion-Conducting Glasses

et al. 2023

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As an alternative to liquid electrolytes, all-solid-state sodium-ion batteries are receiving significant attention due to their potential for improved safety and efficiency. Here, we propose a combined experimental and machine learning (ML) approach for discovering glass electrolytes while also providing insights into the role of different glass components. Specifically, we experimentally prepare and measure the ionic conductivity of 27 glass compositions of the sodium aluminophosphate glass family. Further, we train ML models on this dataset to predict the ionic conductivity, which exhibits excellent agreement with the experimental results. We interpret the composition–conductivity relationship learned by the ML model using Shapely additive explanations (SHAP), which reveals the role played by the glass components in governing the conductivity. Employing these observations, glass compositions with improved conductivity values are predicted and experimentally validated. The results corroborate the insights from SHAP analysis and enable optimized glass formulations in real-world experiments. This demonstrates how ML tools can significantly accelerate the discovery of Na-ion-conducting glass electrolytes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Design of Na-Ion-Conducting Glasses

et al. 2023

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“…The Raman spectra of PIG- x Sr and MPG- y Sr were simulated by assuming Gaussian lines for quantitative analysis. In the case of MPG- y Sr, the Raman band corresponding to the POP symmetric stretching mode of bridging oxygen (700 cm −1 ) was deconvoluted as Q P 2 long/short chains and Q P 1 [ 51 ], and the band corresponding to the PO 2 symmetric stretching mode of non-bridging oxygen (1170 cm −1 ) was deconvoluted into Q P 2 long and short chains [ 51 ]. The peaks of the phosphate groups in PIG- x Sr were red-shifted on increasing the SrO substitution percentage.…”

Section: Resultsmentioning

confidence: 99%

“…Raman peaks of Q P 2 groups corresponding to POP and (PO 2 ) sym in MPG- y Sr were deconvoluted with long and short chains [ 51 ]. The long chain of Q P 2 groups decreased with increasing SrO substitution percentage, whereas the short chain exhibited an opposite trend.…”

Section: Discussionmentioning

confidence: 99%

Structures and Dissolution Behaviors of Quaternary CaO-SrO-P2O5-TiO2 Glasses

et al. 2021

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Calcium phosphate glasses have a high potential for use as biomaterials because their composition is similar to that of the mineral phase of bone. Phosphate glasses can dissolve completely in aqueous solution and can contain various elements owing to their acidity. Thus, the glass can be a candidate for therapeutic ion carriers. Recently, we focused on the effect of strontium ions for bone formation, which exhibited dual effects of stimulating bone formation and inhibiting bone resorption. However, large amounts of strontium ions may induce a cytotoxic effect, and there is a need to control their releasing amount. This work reports fundamental data for designing quaternary CaO-SrO-P2O5-TiO2 glasses with pyro- and meta-phosphate compositions to control strontium ion-releasing behavior. The glasses were prepared by substituting CaO by SrO using the melt-quenching method. The SrO/CaO mixed composition exhibited a mixed cation effect on the glassification degree and ion-releasing behavior, which showed non-linear properties with mixed cation compositions of the glasses. Sr2+ ions have smaller field strength than Ca2+ ions, and the glass network structure may be weakened by the substitution of CaO by SrO. However, glassification degree and chemical durability of pyro- and meta-phosphate glasses increased with substituted all CaO by SrO. This is because titanium groups in the glasses are closely related to their glass network structure by SrO substitution. The P-O-Ti bonds in pyrophosphate glass series and TiO4 tetrahedra in metaphosphate glass series increased with substitution by SrO. The titanium groups in the glasses were crosslink and/or coordinate phosphate groups to improve glassification degree and chemical durability. Sr2+ ion releasing amount of pyrophosphate glasses with >83% SrO substitution was larger than 0.1 mM at day seven, an amount that reported enhanced bone formation by stimulation of osteogenic markers.

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“…For these glasses, a highly consistent dataset including 56 individual samples with variable chemical composition is available from our lab, together with a range of physical and spectroscopic data. [6][7][8] In order to improve the accuracy of chemical composition data over previously available energy-dispersive X-ray spectroscopic results (EDX), [15] all glasses were reanalyzed by fully quantitative wavelength-dispersive X-ray spectroscopy (WDX) using a JEOL JXA8800L microprobe analyzer. Generally, compared to standardless EDX the WDX technique provides improved energy resolution, detection limit, and more precise results, in particular, for the lighter elements such as F (the elemental composition obtained in this way and the previously reported ionic conductivity are the (X, Y) input in Figure 5).…”

Section: Methodsmentioning

confidence: 99%

Genome Mining in Glass Chemistry Using Linear Component Analysis of Ion Conductivity Data

2023

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Understanding the multivariate origin of physical properties is particularly complex for polyionic glasses. As a concept, the term genome has been used to describe the entirety of structure‐property relations in solid materials, based on functional genes acting as descriptors for a particular property, for example, for input in regression analysis or other machine‐learning tools. Here, the genes of ionic conductivity in polyionic sodium‐conducting glasses are presented as fictive chemical entities with a characteristic stoichiometry, derived from strong linear component analysis (SLCA) of a uniquely consistent dataset. SLCA is based on a twofold optimization problem that maximizes the quality of linear regression between a property (here: ionic conductivity) and champion candidates from all possible combinations of elements. Family trees and matrix rotation analysis are subsequently used to filter for essential elemental combinations, and from their characteristic mean composition, the essential genes. These genes reveal the intrinsic relationships within the multivariate input data. While they do not require a structural representation in real space, how possible structural interpretations agree with intuitive understanding of structural entities known from spectroscopic experiments is finally demonstrated.

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Optimization of electrical conductivity in the Na₂O‐P₂O₅‐AlF₃‐SO₃ glass system

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 11 publications

References 28 publications

Machine Learning-Assisted Design of Na-Ion-Conducting Glasses

Machine Learning-Assisted Design of Na-Ion-Conducting Glasses

Structures and Dissolution Behaviors of Quaternary CaO-SrO-P2O5-TiO2 Glasses

Genome Mining in Glass Chemistry Using Linear Component Analysis of Ion Conductivity Data

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