Quantification and Effect of Residual Water in Solid Polymer Electrolytes
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».