Fabrication and Characterization of Highly Ordered Graphite Nanofiber Network Structure for Lithium-Sulfur Batteries
Bibliographic record
Abstract
Graphitic carbon fiber felt (GCFF) with a highly ordered crystalline graphitic carbon structure was fabricated by a combination of electrospinning and graphitization (2800 °C heat treatment) of carbon precursor polymer (PAN; Polyacrylonitrile) and was used as an interlayer between the cathode and separator in Li-S batteries. This GCFF interlayer trapped the polysulfides on the cathode side and increased the utilization of sulfur by suppressing the shuttle phenomenon. Also, the GCFF was shown to be able to act as an upper current collector to reduce the charge-transfer resistance owing to the high crystallinity of the graphitic carbon fibers. The sulfur cathode with the GCFF interlayer showed a high specific initial discharge capacity of 1280 mAh g−1 and excellent cycling stability (1004 mAh g−1 after 100 cycles) at 0.2 C. Also, an image of the glass fiber (GF) separator on the anode side confirmed the presence of an SEI(solid-electrolyte interphase) after 200 cycles, which apparently resulted from stable Li deposition on the Li metal because of the low or medium concentration of sulfur in the electrolyte solution. Our observations should contribute to elucidating the key features of complex three-dimensional carbon fabrics with crystalline graphitic structures that allow them, when inserted as interlayers, to markedly improve the performance of rechargeable batteries. Reference: Journal of Power Sources, Volume 360, 31 August 2017, Pages 559-568
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".