(Invited) Vanadium Based Phosphate, Phosphite, Oxalate Cathode Materials for Lithium Batteries
Bibliographic record
Abstract
Commercial lithium ion batteries (LIBs) use layer-type compounds, lithium cobalt oxide (LiCoO2) or LiFePO4 as the cathode (positive electrode) and graphite (C) as the anode (negative electrode) material, and a non-aqueous Li- ion conducting electrolyte. The liquid electrolyte in the form of a solution or immobilized in a gel-polymer. LIBs with an operating voltage of 3.6 V are extensively used in the present-day portable electronic devices like, cell phones and other low power operated devices. For high-power applications like, electric/hybrid electric vehicles and back-up power supplies and, the LIBs need to satisfy several criteria, namely, cost-reduction, improvement in the energy density, safety-in-operation at high current charge/discharge rates and improvement in the low-temperature-operation. To satisfy the above criteria, researches are being carried out worldwide to find alternative cathode materials. In my presentation, I will discuss our group studies on V-based Flouro phosphates cathode materials. Specifically, I will focus bare and Fe doped-LiVPO4F, αI-LiVOPO4 and V-based metal organic framework materials, MX[(VO)2N2(C2O4)], where M= K, Li, Na , N= HPO4, HPO3. and LiSiV2O6. Materials were prepared variety of chemical methods carbothermal/Graphenothermal reduction, hot plate and hydrothermal methods. Materials were characterized by Rietveld refinement X-ray diffraction, X-ray photoelectron spectroscopy, Raman, XPS, Microscopy (SEM &TEM) and BET surface area methods. Electro analytical studies like cyclic voltammetry, galvanostatic cycling and electrochemical impedance spectroscopy techniques. Interestingly V-based cathodes showed around 4 V redox couple (except LiSiV2O6) which is higher than that of LiFePO4 (3.5V vs. Li) and observed reversible capacity is order 80-140 mAh/g. Discuss in detail long term cycling studies and rate capabilities and reaction mechanisms. Keywords: Materials synthesis; Batteries; Cathodes; Electrochemical Properties; Energy storage References 1. M.V. Reddy, M. V.; G.V. Subba Rao, B.V.R. Chowdari, Chem. Rev. 113 (2013) 5364 2. A.S. Hameed, M.V. Reddy*, M. Nagarathinam, T. Runčevski, R.E. Dinnebier, S. Adams, B.V.R. Chowdari, J.J. Vittal, Scientific Reports, 5 (2015) 16270 3. M. Shahul, M. V. Reddy*, N.Sarakar, B. V. R. Chowdari,J. J. Vittal RSC Adv., 5(2015), 60630 4. M. Shahul, M. Nagarathinam, M. V. Reddy*, B. V. R. Chowdari,J. J. Vittal Journal of Materials Chemistry 22(2012)7206-7213. 5. M. Nagarathinam, K. Saravanan, E. J. Han Phua, M. V. Reddy*, B. V. R. Chowdari,J. J. Vittal Angewandte Chemie Intl. 51(24)(2012)5866-5870. 6. M .V. Reddy, G.V. Subba Rao and B. V. R. Chowdari Journal of Power Sources 195(2010)5768-5774.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".