Bond character, optical properties and ionic conductivity of Li2O/B2O3/SiO2/Al2O3 glass: Effect of structural substitution of Li2O for LiCl

Abdel-Baki, Manal; Salem, A.M.; Abdel-Wahab, F.A.; El-Diasty, Fouad

doi:10.1016/j.jnoncrysol.2008.07.003

Cited by 36 publications

(10 citation statements)

References 48 publications

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“…The boron-boron separation (d <b-b> ) increases continuously with LiCl addition indicating increasing tendency of expansion which is supported by T g results. Similar results have been reported earlier [8]. The density of glasses is found to increase with LiCl addition.…”

Section: Resultssupporting

confidence: 91%

Study of lithium borosilicotitanate glasses with LiCl

Deshpande¹,

Paighan²

2012

AIP Conference Proceedings

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The effect of LiCl addition on the properties of Li 2 O:B 2 O 3 :SiO 2 :TiO 2 glasses has been studied. It has been observed that the ionic conductivity increases by about half an order with LiCl addition. The decrease in glass transition temperature T g and increase in the molar volume with LiCl addition have good correlation with conductivity results. The observed increase in density of glasses has been explained on the basis of heavier Cl -ion which is accommodated in the interstices in the glass network.

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Section: Resultssupporting

confidence: 91%

Study of lithium borosilicotitanate glasses with LiCl

Deshpande¹,

Paighan²

2012

AIP Conference Proceedings

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“…Again, the conductivity of NAPSC-4 glasses, which exhibit the highest conductivity value of 2.44 × 10 –6 at 373 K, indicates a positive effect of Na 2 O, P 2 O 5 , and NaCl. In this glass, by incorporating large-size free Cl – ions in the intrinsic sites of the network structure, NaCl makes the glass structure more stretch and brittle. − For instance, K. Muruganandam et al found that the integration of LiCl into the glass structure did not dramatically alter the glass structure but that by stretching the network, it generates channels for the cation movement. This leads to an increase in the mobility of Na + cations.…”

Section: Resultsmentioning

confidence: 99%

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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“…However, using of organic liquid or polymer electrolyte prevents the creation of completely safe device [4][5][6]. Replacement of the organic electrolyte with inorganic solid will not only significantly improve the safety of lithium-ion battery, but extend its life by reducing the degradation processes [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%