Materials for High Energy Density and Long-Life Lithium-Sulfur Batteries
Bibliographic record
Abstract
Lithium ion batteries have been successfull deployed in several NASA missions since 2000, including the Mars Exploration Rovers (Spirit and Opportunity), Mars Science Laboratory (MSL), Pheonix Lander, JUNO, Aquarius, Keppler Rovers and SMAP. Being compact, lightweight and durable, these batteries have contributed to significant enhancement or even enablement of these missions. However, NASA’s future missions, i.e. small planetary rovers, planter probes, small satellites, CubeSats etc., warrant more efficient battery technologies, ‘beyond Li-ion,’ with higher energy densities. The lithium-sulfur system emerges as the most promising technology because of its high theoretical specific energy (3-4x) compared to Li-ion cells. To address the needs of the future missions, NASA has initiated projects to develop high-energy and long-life lithium-sulfur cells, with a high cell specific energy of 400 Wh/kg, good cycle life of 300 cycles and an ability to operate safely over a wide temperature range of -10 to +30 °C. Despite its high theoretical energy densities and significant developmental efforts in several laboratories, Li-S technology hasn’t matured yet, mainly due to challenges related to the leaching of reduced products into the electrolyte forming a redox shuttle and also poisoning the lithium anode. Several attempts are being made in the literature to develop cathode designs, e.g., hierarchical porous carbon structures to sequester sulfur and its reduction products, and also electrolyte solutions to minimize their solubility. 1-5 In a similar manner, we have developed improved cell components for Li-S cells,6 i.e., i) new sulfur cathodes with metal sulfide blends that show high specific capacities of ≥800 mAh/g at C/3 rates with high material loadings required for achieving high specific energy and energy densities, ii) Li anode protected with suitable polymer electrolytes that display efficient Li cycling and durability in laboratory Li-S cells, and iii) Electrolytes with co-solvents electrolyte additives and iv) new proprietary electrode coatings serving as polysulfide blocking layers to inhibit the deleterious effects of sulfur redistribution and contribute to a good cycle life. In this paper, we will describe some of these material developments and their electrochemical behavior in three-electrode cells and performance in laboratory pouch. Y. Yin, S. Xin, Y. Guo and L. Wan, Angew. Chem. Int. Ed. 2013, 52, 13186 (2013). S. Evers, L. F. Nazar, Acc. Chem. Res., 46, 1135 (2013); X. Ji, K. T. Lee, L. F. Nazar, Nat. Mater. 8, 500 (2009). A. Manthiram, S.-H. Chung, C. Zu, Adv. Mater. 27, 1980 (2015). S. S. Zhang, Front. Energy Res. 1, 1 (2013). Ratnakumar Bugga, Simon Jones, Jasmina Pasalic, Dan Addison and Ramanathan Thillaiyan, 228th ECS Meeting, Phoenix, AZ, Oct. 11 (2015)
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".