Data‐Driven Analysis of High‐Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity**

Krishnamoorthy, Anand Narayanan; Wölke, Christian; Diddens, Diddo; Maiti, Moumita; Mabrouk, Youssef; Yan, Peng; Grünebaum, Mariano; Winter, Martin; Heuer, Andreas; Cekic‐Laskovic, Isidora

doi:10.1002/cmtd.202200008

Cited by 11 publications

(30 citation statements)

References 55 publications

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“…High throughput experimentation (HTE) system. The robotic HTE system 13 , used to acquire the data 12 presented here, is designed for high-throughput operation in a nitrogen atmosphere. The setup designed for the formulation of electrolyte solutions is able to prepare 96 formulations in 8 h by gravimetric dosing of solid and liquid materials into polymer or aluminium vials.…”

Section: Methodsmentioning

confidence: 99%

“…For the measurement, the samples are arranged in groups of eight samples per rack, three of which are mounted on one larger rack. Four of these combined racks can be connected to the Metrohm Autolab potentiostat, which is used for the measurements 13 .…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, it can help to gain deeper insights regarding composition-property-performance relationships. Fractions of this dataset served as the basis for several machine learning models published elsewhere 11,13,14 . The automated high-throughput experimentation system 13 available at the Helmholtz Institute Münster is used to formulate a variety of electrolyte solutions based on EC, EMC, PC and LiPF 6 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Fractions of this dataset served as the basis for several machine learning models published elsewhere 11,13,14 . The automated high-throughput experimentation system 13 available at the Helmholtz Institute Münster is used to formulate a variety of electrolyte solutions based on EC, EMC, PC and LiPF 6 . Ratios of (PC + EC):EMC of 3:7 and 1:1 are covered in the dataset 12 .…”

Section: Background and Summarymentioning

confidence: 99%

“…The robotic system 13 used for the acquisition of the data is able to dispense liquid and solid components into aluminium or polymer vials with high accuracy. Each formulation is identified by a batch number and measurements are identified by a unique ID stored and reported on the vial through a QR code.…”

Section: Background and Summarymentioning

confidence: 99%

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Conductivity experiments for electrolyte formulations and their automated analysis

et al. 2023

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Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6). The mass ratio of EC to PC was varied, while keeping the mass ratio of (EC + PC) and EMC at fixed values of 3:7 and 1:1. The conducting salt concentration was also varied during the study. Conductivity data was obtained from electrochemical impedance spectroscopy (EIS) measurements at various temperatures. Based on the thus obtained temperature series, the activation energy for ionic conduction was determined during the analysis. The data is presented here in a machine-readable format and includes a Python package for analyzing temperature series of electrolyte conductivity according to the Arrhenius equation and EIS data. The data may be useful e.g. for the training of machine learning models or for reference prior to experiments.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

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Conductivity experiments for electrolyte formulations and their automated analysis

et al. 2023

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Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations

Vogler,

Steensen,

Ramírez

et al. 2024

Advanced Energy Materials

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Non‐trivial relationships link individual materials properties to device‐level performance. Device optimization therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. This study demonstrates a Materials Acceleration Platform (MAP) in the field of battery research based on the problem‐agnostic Fast INtention‐Agnostic LEarning Server (FINALES) framework, which integrates simulations and physical experiments while leaving the active control of the hardware and software resources executing experiments or simulations with the partners running the respective units. This decentralization of control is a distinctive feature of MAPs using the FINALES framework. The connected capabilities entail the formulation and characterization of electrolytes, cell assembly and testing, early lifetime prediction, and ontology‐mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimize the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium‐ion coin cells by varying the electrolyte formulation. Trends in ionic conductivity are rediscovered and the effect of the electrolyte formulation on the EOL is investigated. Further, the capability of this MAP to bridge diverse research modalities, scales, and institutions enabling system‐level investigations under asynchronous conditions while handling concurrent workflows on the material‐ and system‐level is shown, demonstrating true intention‐agnosticism.

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Evaluation of Alternative Lithium Salts for High‐Voltage Lithium Ion Batteries: Higher Relevance of Plated Li Morphology Than the Amount of Electrode Crosstalk

Arifiadi,

Wichmann,

Brake

et al. 2024

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Increasing the upper cut‐off voltage (UCV) enhances the specific energy of Li‐ion batteries (LIBs), but is accompanied by higher capacity fade as a result of electrode cross‐talk, i.e., transition metals (TM) dissolution from cathode and deposition on anode, finally triggering high surface area lithium (HSAL) formation due to locally enhanced resistance. Here, LiPF6, LiBF4, lithium difluoro(oxalate)borate (LiDFOB), lithium bis(oxalate)borate (LiBOB), lithium bis(fluorosulfonyl)imide (LiFSI), and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in carbonate‐based solvents are investigated in LiNi0.6Co0.2Mn0.2O2 (NCM 622) || graphite pouch cells with 4.5 V UCV. Despite the lower oxidative stabilities of LiBF4 and LiDFOB, thus enhanced HF formation, TM dissolution, and consequently electrode cross‐talk, higher capacity retention is observed compared to the case of LiPF6 electrolyte. Counterintuitively, it is not the TM deposit amount but rather the Li plating morphology that governs capacity fade, as these salts cause more uniform and compact lithium plating, i.e., lower surface area. In contrast, the dendritic HSAL with LiPF6 has a higher surface area, and more parasitic reactions, thus active Li (“Li inventory”) losses and capacity fade. Although NCM initiates the failure cascade, the capacity losses and cycle life of high‐voltage LIBs are predominantly determined by the anode, in particular the Li plating morphology and the corresponding surface area.

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Data‐Driven Analysis of High‐Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity**

Cited by 11 publications

References 55 publications

Conductivity experiments for electrolyte formulations and their automated analysis

Conductivity experiments for electrolyte formulations and their automated analysis

Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations

Evaluation of Alternative Lithium Salts for High‐Voltage Lithium Ion Batteries: Higher Relevance of Plated Li Morphology Than the Amount of Electrode Crosstalk

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