Diffusion Measurements of Mg in High Capacity Thiospinel Mg<sub>x</sub>Ti<sub>2</sub>S<sub>4</sub>
Bibliographic record
Abstract
The demand for smaller batteries capable of storing the same amount of energy as conventional Li-ion technology 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 hosts3 and a probable higher desolvation energy penalty.4 No further positive electrode materials with both notable capacity and cycle life have been demonstrated since the Chevrel phase, until now. In this presentation, we will demonstrate that the thiospinel Ti2S4 reversibly intercalates Mg2+ with a 2ndcycle capacity of about 165 mAh/g, which drops to only 140 mAh/g after 40 cycles at C/10 as shown in Figure 1. Of crucial scientific importance is that Ti2S4 provides a second example of a material that supports facile Mg2+ diffusion, which could help elucidate why Mg2+ intercalation is so difficult in other potential cathode materials. In exploring Mg2+ diffusion, the first step is to measure the chemical diffusion coefficient, D. The galvanostatic intermittent titration technique (GITT)5 is a versatile method of carrying this out for an intercalant like Mg2+ if a reliable cell can be constructed that has a long enough diffusion length to produce a linear potential vs time response reflecting Fick’s laws of diffusion. Figure 2 shows a typical GITT experiment on thiospinel MgxTi2S4, which displays the required potential vs time response. This talk will elaborate on the results of the diffusion measurements we have performed and compare those results to theory. 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). L. F. Wan, B. R. Perdue, C. A. Apblett and D. Prendergast. Chem. Mater. 27, 5932-5940 (2015). W. Weppner and R. A. Huggins. Solid-State Science and Technology 124, 1569-1578 (1977). 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".