A Comparison of Thiospinel Mg Battery Cathode Materials: Mg<sub>x</sub>Ti<sub>2</sub>S<sub>4</sub> and Mg<sub>x</sub>Zr<sub>2</sub>S<sub>4</sub>
Bibliographic record
Abstract
The search for batteries with higher volumetric energy densities than conventional Li-ion batteries has led to the pursuit of several new technologies including rechargeable Mg batteries. Mg metal is attractive as a negative electrode material because it has a higher volumetric capacity density (3833 mAh/mL) than Li metal (2062 mAh/mL), is the 8th most abundant element in the earth’s crust, is safe to handle in ambient atmosphere, and can be electrodeposited (charged) without the formation of dendrites.1 The seminal work by Aurbach et al. in 20002 established an electrolyte and a positive electrode material, the Chevrel phase (Mo6S8), that was paired with Mg metal to form the first rechargeable Mg battery. Mg2+ intercalation in host materials is more difficult than that of Li+ or Na+, displaying lower ion mobility in solid oxide hosts,3 a tendency toward conversion reactions,4 and a possibly higher desolvation energy penalty.5 In fact, the second material to reversibly intercalate Mg2+, without debilitating decomposition over more than 100 cycles, was only demonstrated last year: the thiospinel Ti2S4.6 Unfortunately, our attempts to insert (or remove) Mg2+ into other 1st row transition metal thiospinels were unsuccessful; however, Mg2+ can be reversibly intercalated from thiospinel Zr2S4, a 2nd row transition metal. Figure 1 shows the 1st discharge and charge of two coin cells using Mg foil anodes, Mg(CB11H12)2 in tetraglyme electrolyte and either a MgxTi2S4 or MgxZr2S4 electrode. We will present a comparison of the Mg intercalation into the Zr2S4 and Ti2S4 thiospinels, including Mg site occupancy, cell parameter variation, and Mg diffusion coefficients. In doing so, we will explore the crucial parameters that allow facile Mg2+ diffusion for consideration when designing new Mg2+ intercalation materials. References J. Muldoon, C. B. Bucur, and T. Gregory. Chem. Rev. 114, 11683-11720 (2014). D. Aurbach, Z. Lu, A. Schechter, Y. Gofer, H. Gizbar, R. Turgeman, Y. Cohen, M. Moshkovich and E. Levi. Nature 407, 724-727 (2000). E. Levi, Y. Gofer, and D. Aurbach. Chem. Mater. 22, 860-868 (2010). P. Canepa, G. Sai Gautam, D.C. Hannah, R. Malik, M. Liu, K.G. Gallagher, K.A. Persson and G. Ceder. Chem. Rev. 117, 4287-4341 (2017). L. F. Wan, B. R. Perdue, C. A. Apblett and D. Prendergast. Chem. Mater. 27, 5932-5940 (2015). X. Sun, P. Bonnick, V. Duffort, M. Liu, Z. Rong, K.A. Persson, G. Ceder and L. F. Nazar. Energy Environ. Sci. 1, 297-301 (2016). Figure 1
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".