Quantification and Effect of Residual Water in Solid Polymer Electrolytes
Bibliographic record
Abstract
In the context of the urgent modern energy challenges, lithium-metal (LMB) and lithium-ion batteries (LIB) are conceivable candidates for long term electrochemical storage of renewable energies. As such, while many possible electrolytes are being investigated, solid polymer electrolytes (SPE) are potential replacements for the classic liquid electrolytes that are used in today’s commercial LIB developed and patented by Sony Energitech in 1991. Despite offering non-flammable properties, eliminating the need for heavy casings and allowing the use of higher-energy density lithium at the anode, these electrolytes still suffer from low ionic conductivities. Therefore, plenty of novel SPE systems are being developed and proposed for their captivating properties. Oftentimes, these systems seem appealing but are hardly reproducible in laboratory settings, basing oneself solely upon the published experimental conditions. An interesting inter-laboratory study was published, in which the same thiophosphate ceramic-based solid electrolyte samples were distributed across 11 different research groups. The relative standard deviation calculated from reported conductivities reached a value of 50%. (1) Recently, it has been shown that different post-drying procedures influenced the water content of electrodes which in turn affected the electrochemical properties of LIBs. (2) In SPEs, amongst different factors that could affect ionic conductivities, we believe that water plays a non-neglectable role. Knowing that relative humidity is strongly affected by the local climate, conditions can vary from laboratory to laboratory from a day to another. Being a factor that isn’t controlled effortlessly, samples’ water content can easily be overlooked. However, only a few recent papers mention that matter.(3) The issue is that on one hand, even though some authors reported enhanced ionic conductivities with an increased water content of SPEs, others claim the opposite to be true, which makes the subject ambiguous.(4) On the other hand, very few SPE systems have been studied, which makes the effect of water unclear. In this study, three different types of polymers, poly(ethylene oxide) (PEO), poly(acrylonitrile) (PAN), and acrylonitrile methyl acrylate copolymer latex (AMAC), two different lithium salts (LiTFSI and LiClO4), and two different processing methods (Wet solution casting and solvent-free dry mixing) have been used to prepare different SPE samples. Samples of PEO-LiTFSI, PAN-LiClO4, and AMAC-LiTFSI with different water contents have been prepared and analysed following a strict reproducible drying/doping protocol. Different electrochemical parameters such as the ionic conductivity, the activation barrier and the electrochemical stability window have been assessed in different samples and correlated to their respective water contents. It has been shown that water does indeed influence SPEs and that it’s a factor that needs to be taken into consideration for reproducibility purposes. 1: S.Ohno, T. Bernges, J. Buchheim, M. Duchardt, A. K. Hatz, M. A. Kraft, H. Kwak, A. L. Santhosha, Z. Liu, N. Minafra, F. Tsuji, A. Sakuda, R. Schlem, S. Xiong, Z. Zhang, P. Adelhelm, H. Chen, A. Hayashi, Y. S. Jung, B. V. Lotsch, B. Roling, N. M. Vargas-Barbosa, W. G. Zeier, ACS Energy Lett., 5, 910-915 (2020). 2: F. Huttner, W. Haselrieder, A. Kwade, Energy Technol., 8, 1900245 (2020). 3: B. Commarieu, A. Paolella, S. Collin-Martin, C. Gagnon, A. Vijh, A. Guerfi, K. Zaghib, J. Power Sources, 436, 226852 (2019). 4: M. Z. Munshi, B. B. Owens, Appl. Phys. Commun., 8 (1987). TOTAL Classification: Restricted Distribution TOTAL - All rights reserved
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".