Garnet-Based Electrolytes for All-Solid-State Li-S Batteries
Bibliographic record
Abstract
Lithium sulfur (Li-S) batteries have emerged as one of the most promising post LIBs technologies with a remarkably high theoretical energy density and abundance of elemental sulfur. Nonetheless, there are several problems associated with Li-S batteries such as safety hazard due to lithium dendrite formation and fast capacity decay due to polysulfide dissolution effect.1 Employment of solid-state electrolytes is a promising strategy to address those issues. Among different solid-state Li-ion electrolytes, Li-garnet attracts a lot of attention as it has a wide electrochemical window (> 6 V vs. Li/Li+), and high ionic conductivity (~ 1 mS cm-1) at room temperature. However, the application of garnet is hampered by its interfacial resistance against electrodes.2 In order to the reduce the interfacial area specific resistance (ASR) of Li/garnet interface, we devised a surfactant-processed interlayer for ceramic electrolytes (SPICE) method which can uniformly deposit a layer of ZnO onto the garnet surface. This process improves the wetting of Li and reduces the interfacial ASR to 10 Ω cm2 at room temperature.3 Stable Galvanostatic cycling of Li/garnet/Li at current densities up to 0.5 mA cm−2 was conducted, which presents a compelling method to solve the Li/solid electrolyte interface problem. Another strategy we applied is incorporating garnet into polymer matrix to fabricate a flexible hybrid electrolyte. Polymer-based electrolytes possess low interfacial resistance due to its intimate contact with electrodes.4 The hybrid electrolyte merging the merits of garnet and polymer has been successfully employed in all-solid-state Li-S batteries operating at room temperature. Toward improving the energy density of the battery, we are working on tuning the cathode structure to effectively load more sulfur active materials. In this presentation, the SPICE method to tailor the interfacial resistance and the performance of all-solid-state Li-S batteries based on hybrid electrolyte will be discussed. Manthiram, A.; Fu, Y.; Chung, S.; Zu, C.; Su, Y. Chem. Rev. 2014, 114, 11751-11787. Han, X.; Gong, Y.; Fu, K.; He, X.; Hitz, G.; Dai, J.; Pearse, A.; Liu, B.; Wang, H.; Rubloff, G.; Mo, Y.; Thangadurai, V.; Wachsman, E.; Hu, L. Nat. Mater. 2016, 16, 572-579. Zhou, C.; Samson, A.; Hofstetter, K.; Thangadurai, V. Sustainable Energy & Fuels 2018, 2, 2165-2170. Zhou, C.; Bag, S.; Thangadurai, V. ACS Energy Lett. 2018, 3, 2181-2198.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".