(Invited) Calcium-Ion Cathode Materials
Bibliographic record
Abstract
Calcium-ion batteries (CIBs) could be an alternative to lithium-ion batteries (LIBs) for certain niche applications due to their theoretically high operating potentials and the high natural abundance of calcium. However, the development of CIBs has been limited by the lack of proper electrolytes and electrode materials due to the difficulties identifying Ca-ion insertion host materials. Previous efforts to develop CIB cathodes have been made mainly based on intercalation materials such as Prussian blue-based materials (1) , layered oxide materials (MoO3 (2), V2O5 (3), CaCo2O4 (4-5), and layered phosphates (VOPO4·2H2O) (6). In this discussion we will be reviewing the state of Ca-ion battery materials and delve into an analysis of several functional polyanionic materials that show promising properties and characteristics as CIB cathodes. The NASICON-type NaV2(PO4)3, derived from de-sodiation of Na3V2(PO4)3, has been shown to reversibly intercalate Ca2+ -ions (capacity 81 mA h g-1) above 3V (vs. Ca2+/Ca) with stable cycling performance. DFT calculations, XAS, XRD, and TEM studies were used to support the insertion of Ca-ions and give insights into the diffusion mechanism of the materials involved (7,8). The presentation will also provide insights into materials design aspects of CIB materials and note areas of need that would benefit the MV battery community. References: (1) Albert L. Lipson, Baofei Pan, Saul H. Lapidus, Chen Liao, John T. Vaughey,Brian J. Ingram “Odyssey of Rechargeable Ca-Ion Batteries: A New Energy Storage System”. Chem. Mat. 2015, 27 , 8442. (2) Marta Cabello, Francisco Nacimiento, Ricardo Alcántara, Pedro Lavela, Carlos Pérez Vicente,José L. Tirado “Applicability of Molybdite as an Electrode Material in Calcium Batteries: A Structural Study of Layer-type CaxMoO3” Chem. Mat., 2018, 30, 5853−5861 (3) M. Bervas, L.C. Klein, G.G. Amatucci “Vanadium oxide–propylene carbonate composite as a host for theintercalation of polyvalent cations”. Solid State Ionics 2005 176, 2735–2747. (4) A. Ponrouch, M.R. Palacin “On the road toward calcium-based batteries” Current Opinion in Electrochemistry 2018, 9, 1-7. (5) Haesun Park, Yanjie Cui, Sanghyeon Kim, J. T. Vaughey, PeterZapol “Ca Cobaltites as Potential Cathode Materials for Rechargeable Ca-Ion Batteries: Theory and Experiment” J. Phys. Chem. C 2020, 124, 5902−5909 (6) J.J. Wang, S.S. Tan, F.Y. Xiong, R.H. Yu, P.H. Wu, L.M. Cui, Q.Y. An “VOPO4 2H(2)O as a new cathode material for rechargeable Ca-ion batteries” Chem Comm. 2020 56 3805. (7) M.L. Mao, T. Gao, T, S.Y., Hou, C.S. Wang “A critical review of cathodes for rechargeable Mg batteries” Chem Soc Reviews 2018 47 8804. (8) T.N. Chen, G.S. Gautam, W.X. Huang, G. Ceder, G “First-Principles Study of the Voltage Profile and Mobility of Mg Intercalation in a Chromium Oxide Spinel” Chem Mat 2018 30 152.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".