<sup>25</sup>mg NMR Studies of Mg-Ion Battery Materials
Bibliographic record
Abstract
Multivalent-ion chemistries such as Mg-ion are emerging as alternative battery systems to Li-ion. Current Mg-ion chemistries are limited to relatively low voltages and relatively low reversible specific capacities (1-2). Recent research on potential high voltage Mg-ion cathode materials and alternative anode materials such as transition metal oxides and metal alloys have highlighted the urgent need to understand structure activity relationships and insertion/intercalation phenomenon for development of such systems (3). Solid state NMR is a powerful tool to investigate local structure and insertion/intercalation phenomena, particularly for batteries as shown for Li-ion chemistries with 6Li and 7Li NMR (4, 5). However, the low natural abundance (10%) of the NMR active Mg isotope (25Mg), highly quadrupolar nuclear spin of 25Mg (spin 5/2) and very low gyromagnetic ratio (i.e 30.6 MHz Larmor frequency relative to 1H = 500 MHz) limits the effective use of 25Mg NMR for solid Mg-ion battery materials (6). In this work, despite the challenges of 25Mg NMR, our recent efforts to characterize Mg environments in cathode materials such as MgMn2O4 spinels, MgV2O5, in anode materials such as h-TiO2 and Mg-Sn alloys will be presented. Chemical magnesiation using dibutylmagnesium and preliminary electrochemical (de)mangesiation and the structral changes induced will be discussed. The results will summarize the effectiveness of the method in distinguishing side reactions or undesirable conversion reactions, including amorphous phases, from intercalation phenomenon. References: 1. D. Aurbach, Z. Lu, A. Schechter, Y. Gofer, H. Gizbar, R. Turgeman, Y. Cohen, M. Moshkovich, and E. Levi, Nature, 407 (6805), 724-727 (2000). 2. H. D. Yoo, I. Shterenberg, Y. Gofer, G. Gershinsky, N. Pour, and D. Aurbach, Energ Environ Sci, 6 (8), 2265-2279 (2013). 3. Magnesium Batteries 1 and 2, 224th Electrochemical Society Meeting, San Francisco CA, 2013 4. C. P. Grey and N. Dupre, Chem. Rev. (Washington, DC, U. S.), 104, 4493 (2004). 5. B. Key, R. Bhattacharyya, M. Morcrette, V. Seznec, J. M. Tarascon and C. P. Grey, Journal of the American Chemical Society, 131, 9239 (2009). 6. R. Dupree and M. E. Smith, Journal of the Chemical Society-Chemical Communications, 1483 (1988).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".