New Approaches for High Energy Density Lithium–Sulfur Battery Cathodes
Bibliographic record
Abstract
The goal of replacing combustion engines or reducing their use presents a daunting problem for society. Current lithium-ion technologies provide a stepping stone for this dramatic but inevitable change. However, the theoretical gravimetric capacity (∼300 mA h g(-1)) is too low to overcome the problems of limited range in electric vehicles, and their cost is too high to sustain the commercial viability of electrified transportation. Sulfur is the one of the most promising next generation cathode materials. Since the 1960s, researchers have studied sulfur as a cathode, but only recently have great strides been made in preparing viable composites that can be used commercially. Sulfur batteries implement inexpensive, earth-abundant elements at the cathode while offering up to a five-fold increase in energy density compared with present Li-ion batteries. Over the past few years, researchers have come closer to solving the challenges associated with the sulfur cathode. Using carbon or conducting polymers, researchers have wired up sulfur, an excellent insulator, successfully. These conductive hosts also function to encapsulate the active sulfur mass upon reduction/oxidation when highly soluble lithium polysulfides are formed. These soluble discharge products remain a crux of the Li-S cell and need to be contained in order to increase cycle life and capacity retention. The use of mesoporous carbons and tailored designs featuring porous carbon hollow spheres have led to highly stable discharge capacities greater than 900 mA h g(-1) over 100 cycles. In an attempt to fully limit polysulfide dissolution, methods that rely on coating carbon/sulfur composites with polymers have led to surprisingly stable capacities (∼90% of initial capacity retained). Additives will also play an important role in sulfur electrode design. For example, small fractions (> 3 wt%) of porous silica or titania effectively act as polysulfide reservoirs, decreasing their concentration in the electrolyte and leading to a higher utilization of sulfur and increased capacities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".