Evaluation of glass-stability criteria for chalcogenide glasses: Effect of experimental conditions

Svoboda, Roman; Málek, Jiřı́

doi:10.1016/j.jnoncrysol.2015.01.021

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…Note that the latter not only disregards the fact that the Hruby criterion is a measure of glass stability , but this approach also further complicates the interpretation of materials that do not fully crystallize or which exhibit recrystallization into multiple crystalline phases. This ambiguity of K Hruby utilization thus requires the report to always state the exact conditions for which the calculation was performed . For the present data, the K Hruby criterion was evaluated in conformity with Figure C; note that the T c values obtained for the powdered InSe films fall into the temperature ranges used for the measurements of the films deposited on the Kapton foil.…”

Section: Resultsmentioning

confidence: 98%

Formation of 2D-Structured InSe Ceramics from Amorphous Phase Deposited on a Kapton Foil

et al. 2023

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The formation kinetics of the 2D-layered InSe crystalline phase in thin amorphous InSe films was studied by means of differential scanning calorimetry, Raman spectroscopy, X-ray diffraction analysis, and X-ray absorption spectroscopy. All measurements were performed on as-deposited films (thicknesses ranging between 200 and 1500 nm), with Kapton foil used as a substrate. In the films with thickness ≥ 1000 nm, the formation was found to proceed via two steps: the exothermic reorganization of the amorphous phase (activation energy of 420 kJ•mol −1 ), followed by the exothermic formation of the crystalline phase (activation energy of 227 kJ•mol −1 ). For InSe films with thickness ≤ 500 nm, the two processes merged, but their reaction mechanism was still found to consist of two sequential phase transformations, with the reorganization in the amorphous phase being the necessary preliminary step. Both kinetic processes were found to be slowed down by the presence of mechanical defects and by the surface/interface imperfections. The possibility of two-step preparation of ideally layered flexible 2D InSe ceramics is suggested based on the separated hightemperature amorphous phase reorganization, followed by a low-temperature crystal growth step (during which excellent 2D InSe layering could be achieved).

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Section: Resultsmentioning

confidence: 98%

Formation of 2D-Structured InSe Ceramics from Amorphous Phase Deposited on a Kapton Foil

et al. 2023

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“…The main advantage of ML is that it allows the development of a surrogate model for composition–property relationships from data, which, in turn, can be used for screening materials . Since ML approaches are data-hungry, most works rely on data obtained from the literature or produced synthetically using computer simulations. ,,,− However, glass properties are highly sensitive to the preparation and testing conditions. , Thus, the raw materials, experimental conditions, and glass preparation techniques of all the glasses must be consistent for developing accurate ML models. If available, some of these properties can be extracted from the literature employing approaches such as natural language processing. ,,, While this issue could be alleviated to a great extent by using synthetic data generated using molecular simulations, these methods have inherent limitationssuch as high cooling rates and small system sizeswhich makes the extrapolation to new glass compositions questionable.…”

Section: Introductionmentioning

confidence: 99%

“…22,24,31,33−36 However, glass properties are highly sensitive to the preparation and testing conditions. 37,38 Thus, the raw materials, experimental conditions, and glass preparation techniques of all the glasses must be consistent for developing accurate ML models. If available, some of these properties can be extracted from the literature employing approaches such as natural language processing.…”

Section: Introductionmentioning

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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“…[1 -5] As was shown recently [6,7], experimental variables such as particle size and heating rate can significantly influence the outcomes of the glass-stability studies utilizing the basic three-temperature criteria (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, detailed studies of non-isothermal [11 -14] and isothermal crystallization [15], structural relaxation and viscosity behavior [16 -19], nucleation [12] and glass-stability [7] were performed for this material. Regarding the crystallization behavior of Se70Te30 glass, complex crystallization behavior was observed, characterized by two fully overlapping peaks corresponding to the crystal growth from surface and mechanically induced defects and to spherulitic crystallization from volume-located nuclei.…”

Section: Introductionmentioning

confidence: 99%