Fast screening of solid electrolytes: A high throughput solid state NMR probe

Schiffmann, Jan Gerrit; Kopp, Jakob; Geisler, Georg; Kröninger, J.; Wüllen, Leo van

doi:10.1016/j.ssnmr.2012.11.001

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…In line with this, Schiffmann et al developed a high‐throughput solid state NMR probe to determine the ionic conductivity of the solid state electrolyte. [ 120 ] This method allows the evaluation with a HT of >100 samples per hour and is easily upscalable. Besides, Tirosh et al demonstrated a HT mapping method based on the F‐doped tin oxide darkening effect for screening the ionic conductivity in solid state electrolytes.…”

Section: High‐throughput Experimentationmentioning

confidence: 99%

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

Benayad

Diddens

Heuer

et al. 2021

Advanced Energy Materials

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The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces.

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Section: High‐throughput Experimentationmentioning

confidence: 99%

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

Benayad

Diddens

Heuer

et al. 2021

Advanced Energy Materials

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“…Using a combinatorial and HT sputtering system, MacEachern et al observed a new Fe-Zn alloy anode from thin film libraries of a Fe x Zn 1−x system; [13] by combining combinatory magnetron sputtering and HT structural determination, Borhani-Haghighi et al identified compositions showing a layered structure in the Li a (Ni x Mn y Co z )O r cathode library; [14] Beal et al used an HT physical vapor deposition technique to optimize the ionic conductivity for solid-state electrolyte Li 3x La 2/3−x TiO 3 across a wide range of parameter space. [15] Simultaneously, the HT synthesis and measurement approaches, including the robottype instruments producing large numbers of non-aqueous electrolyte mixtures, [16] the HT solid-state nuclear magnetic resonance probe for investigation of ionic conductivity [17] and the HT in situ pressure analysis for degradation processes [18] are also proposed for lithium battery studies.…”

Section: Introductionmentioning

confidence: 99%

Discovery and design of lithium battery materials via high-throughput modeling

Wang

Xiao

et al. 2018

Chinese Phys. B

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This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.

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