Monitoring Copper Ion Leaching from a Metalloporphyrin-Based Cathode for Rechargeable Magnesium Batteries
Bibliographic record
Abstract
Great efforts are being undertaken to characterize the fundamental processes of solid electrolyte interphase (SEI) formation, degradation, and initial conditioning steps of battery electrode materials, including organic electrode materials.[1] Imaging techniques, such as focused ion beam / scanning electron microscopy (FIB/SEM) tomography in combination with element mapping using energy- and wavelength-dispersive X-ray spectroscopy are employed to gain insight into distribution, interconnectivity, and porosity of active materials as well as electrolyte penetration and their influence on degradation processes after longer cycling times.[2] Additionally, electrochemical impedance spectroscopy[3] and anodic stripping voltammetry[4] are used to shed light on those processes. The metalloporphyrin-based material [5,15-bis(ethynyl)-10,20-diphenylporphinato]copper(II) (CuDEPP) has been used as an interesting material for cathodes and anodes of various battery chemistries. These materials undergo initial self-conditioning by electro-polymerization for monovalent ions. With respect to multivalent ions, like magnesium, this polymerization is far less pronounced and an exchange of the copper from the CuDEPP by magnesium ions appears to be occurring.[5] Gaining insight into this trans- or demetallation process induced by parameters like the electrochemical driving force during galvanostatic cycling is a complex analytical challenge. We report the use of Hg ultramicroelectrodes and anodic stripping voltammetry as a sensitive strategy to detect copper ion leaching from metalloporphyrin-based CuDEPP composite electrodes. We demonstrate in-situ/intermittent monitoring of copper ion release during the initial self-conditioning step of the active material while galvanostatic cycling of the battery electrode in dimethoxy ethane. This method could be used to assess the release of an ionic species from complex systems, like composite battery electrodes. References: [1] Peled and Menkin, J. Electrochem. Soc. 2017, 164, A1703. [2] Philipp, Neusser, Abouzari-Lotf, Shakouri, Wilke, Fichtner, Ruben, Mundszinger, Biskupek, Kaiser, Scheitenberger, Lindén and Kranz, J. Power Sources, 2022, 522, 231002. [3] Iurilli, Brivio and Wood, J. Power Sources, 2021, 505, 299860. [4] Hatami, Polcari, Hossain, Ghavidel, Mauzeroll and Schougaard, J. Electrochem. Soc. 2022, 169, 040526. [5] Abouzari-Lotf, Azmi, Li, Shakouri, Chen, Zhao-Karger, Klyatskaya, Maibach, Ruben and Fichtner, ChemSusChem. 2021, 14, 1840. This work contributes to the research performed at CELEST (Center for Electrochemical Energy Storage Ulm - Karlsruhe) and was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2154 – Project number 390874152 (POLiS Cluster of Excellence).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".