Electrospun Li<sub>1.3</sub>Al<sub>0.3</sub>Ti<sub>1.7</sub>(PO<sub>4</sub>)<sub>3</sub> Nanofibers to Develop Solid-State Electrolytes for Lithium Metal Batteries
Bibliographic record
Abstract
During the last decades lithium-ion battery (LIB) has progressively become the benchmark for developing novel energy storage systems for portable and automotive applications. Although the energy density of commercial LIBs has increased significantly over time, it is now reaching a physicochemical limit (~800 Wh L-1), which arises from current materials technology [1]. Hence, a new paradigm is needed to develop next-generation high-energy batteries. A promising candidate is the all-solid-state lithium battery, which features a solid ion-conductive material acting as both separator and electrolyte and usually referred to as solid-state electrolyte (SSE). When compared to flammable organic-based liquid electrolytes, widely used in commercial LIBs, SSEs are characterized by better electrochemical and thermal stabilities as well as by a higher mechanical strength, all of which are beneficial for the safety of the final device. They also enable the use of lithium metal as anode, which potentially increases the volumetric energy density of the cell by up to 70% [1]. SSEs are usually made of polymeric, ceramic or composite materials and, regardless of the composition, they are characterized by some key issues that undermine the performance of the final device. Specifically, the low ionic conductivity at room temperature and the poor interfacial compatibility with the electrodes are the main challenges the scientific community is addressing. Recently, the use of 1-dimensional structures as nanofiller has been reported as an effective strategy to improve ionic conductivity and mechanical properties of composite polymer electrolytes (CPEs) [2–4]. Inorganic nanowires and nanofibers resulted to be also advantageous for increasing the density and therefore the ionic conductivity of ceramic electrolytes [5,6]. Herein, we propose the use of ceramic NASICON-like Li1.3Al0.3Ti1.7(PO4)3 (LATP) nanofibers to develop SSEs for lithium batteries. LATP is one of the most promising ceramic material for designing an SSE because it has the highest ionic conductivity in the Li-NASICON family (7 · 10-4 S cm-1 at 25 °C), it is chemically and thermally stable in atmosphere conditions, and it can be synthesized by using low cost and easily-processable precursors [7]. The synthesis of LATP nanofibers was performed by incorporating an electrospinning step into a conventional sol-gel process [8]. Specifically, a solution containing the precursor materials and a polymer carrier was electrospun to produce a nanofibrous precursor membrane. The achieved membrane was then calcined to synthesize ceramic LATP nanofibers. Purity and morphology of the synthesized material have been investigated by X-ray diffraction and electron microscopy techniques. Finally, LATP nanofibers have been used as ceramic filler to produce a poly(ethylene oxide)-based CPE. Its electrochemical performance are here discussed and compared to those of the equivalent nanoparticle-filled CPE and the plain polymer electrolyte. Preliminary data on a dense ceramic electrolyte achieved by pressing and then calcining the precursor membrane are here reported too. [1] J. Janek, W.G. Zeier, Nat. Energy 1 (2016) 16141 [2] T. Yang, J. Zheng, Q. Cheng, Y.-Y. Hu, C.K. Chan, ACS Appl. Mater. Interfaces 9 (2017) 21773–21780. [3] W. Liu, S.W. Lee, D. Lin, F. Shi, S. Wang, A.D. Sendek, Y. Cui, Nat. Energy 2 (2017) 17035. [4] Z. Wan, D. Lei, W. Yang, C. Liu, K. Shi, X. Hao, L. Shen, W. Lv, B. Li, Q.-H. Yang, F. Kang, Y.-B. He, Adv. Funct. Mater. 29 (2019) 1805301. [5] T. Yang, Z.D. Gordon, Y. Li, C.K. Chan, J. Phys. Chem. C 119 (2015) 14947–14953. [6] T. Yang, Y. Li, C.K. Chan, J. Power Sources 287 (2015) 164–169. [7] H. Aono, E. Sugimoto, Y. Sadaoka, N. Imanaka G. Adachi, J. Electrochem. Soc. 137 (1990) 1023–1027. [8] A. La Monaca, A. Paolella, A. Guerfi, F. Rosei, K. Zaghib, Electrochem. Commun. 104 (2019) 106483.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".