Using Pectin for Energy Storage Devices
Bibliographic record
Abstract
Wearable and flexible printed electronics are more than ever in demand. The value of the flexible electronics market reached USD 26.5 million in 2021 and its revenue forecast will reach USD 63.1 million in 2030.[1] Increasing investments in research & development in these fields have already led to several achievements in the past years. Nevertheless, serious remains concerns about the ecological footprint of such technologies must be addressed early in their conception and throughout the whole electronics life cycle.[2] In printed electronics, electrical energy is supplied by energy storage devices, such as batteries. Most of the time, these systems contain polymers that can be used either as electrolyte, e.g., solid polymer electrolyte or gel polymer electrolyte, to facilitate flexible electronics/batteries fabrication or as binders in positive or negative electrodes ensuring the mechanical cohesion within the composite electrodes.[4]. Several characteristics to meet environmental-friendly flexible electronics requirements. Biobased polymers are one of the promising alternatives in this regard. Their general affinity with water makes them suitable for aqueous rechargeable batteries, implying several technologies such as aqueous rechargeable lithium-ion batteries (ARLB) or zinc rechargeable batteries (ZRB). For instance, polymer hydrogel electrolytes have recently been investigated [3]: their strength lies in promising ionic conductivity (> 10-2 S.cm-1) while maintaining a sufficient mechanical strength and elasticity to be adaptable to flexible energy storage devices. In this study, we developed a hydrogel electrolyte made of pectin, a polysaccharide contained in the cell plants’ wall, as an alternative to synthetic polymers in batteries. Hydrophobic interactions and hydrogen bonds, together with bivalent cation interactions, allow the free-standing electrolyte gelation.[5] The gelation mechanism is first studied, using NMR spectroscopy together with thermal analysis. Then, electrochemical characterization is carried out to analyze the ionic conduction pathways of the gel electrolyte. Its electrochemical stability as well as galvanostatic cycling are investigated to figure out its ability to be used as a electrolyte in hybrid device, such as zinc-lithium-ion batteries. Moving toward printed devices requires to take a closer look to the rheological properties of this system as well as its printability: these challenges will be addressed to ultimately develop an understanding of the impact of this material’s processing on the electrolyte and electrodes properties.[6] References [1]. Flexible Electronics Market Size to Hit US$ 63.1 MN by 2030. (May 2022). Acumen Research and Consulting, https://www.acumenresearchandconsulting.com/. [2]. Baran, D.; Corzo, D.; Blazquez, G. Flexible Electronics: Status, Challenges and Opportunities. Frontiers in Electronics 2020, 1, 2673-5857. DOI: 10.3389/felec.2020.594003. [3]. Liu, J.; Yuan, H.; Tao, X.; et al. Recent progress on biomass-derived ecomaterials toward advanced rechargeable lithium batteries. EcoMat 2020, 2 (1), e12019. DOI: 10.1002/eom2.12019. [4]. Bresser, D.; Buchholz, D.; Moretti, A.; Varzi, A.; Passerini, S. Alternative binders for sustainable electrochemical energy storage – the transition to aqueous electrode processing and bio-derived polymers. Energy Environm Sci 2018, 11, 3096-3127. DOI: 10.1039/C8EE00640G. [5]. Chelfouh, N.; Coquil, G.; Rousselot, S.; Foran, G.; Briqueleur, E.; Shoghi, F.; Caradant, L.; Dollé, M. Apple Pectin-Based Hydrogel Electrolyte for Energy Storage Applications. ACS Sustainable Chemistry & Engineering 2022 , Article ASAP . DOI: 10.1021/acssuschemeng.2c04600. [6]. Clement, B.; Lyu, M.; Kulkarni, S. E.; Lin, T.; Hu, Y.; Lockett, V.; Greig, C.; Wang, L. Recent Advances in Printed Thin-Film Batteries. Engineering 2022, 13, 238-261. DOI: 10.1016/j.eng.2022.04.002.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".