“Sauna” Activation toward Intrinsic Lattice Deficiency in Carbon Nanotube Microspheres for High‐Energy and Long‐Lasting Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) battery technology offers one of the most promising replacement strategies for conventional lithium‐ion batteries, but for several serious obstacles remain, such as the notorious polysulfide shuttling and their sluggish reaction kinetics. In this work, it is demonstrated that these problems can be significantly ameliorated via intrinsic lattice defect engineering in carbon‐based sulfur host materials. Specifically, porous carbon nanotube microspheres (ePCNTM) are developed through a scalable spray drying method, followed by a critical water‐steam etching under high temperature. Such “sauna” activation constructs abundant intrinsic topological defects in the carbon lattice, endowing ePCNTM with enhanced sulfur adsorbability and catalytic activity in sulfur redox reactions. In addition, the interwoven and highly porous architecture renders favorable conductivity, homogeneous sulfur distribution, and massive host–guest interactive surfaces. As a result, the ePCNTM‐based sulfur electrodes achieve excellent cyclability with an ultralow capacity attenuation rate of 0.046% per cycle upon 500 cycles, excellent rate capability up to 3 C, and decent areal capacity retention of 3.2 mAh cm −2 after 50 cycles under raised high sulfur loading. Thus, this synergistic approach, combining composite nanostructuring and intrinsic defect engineering, yields highly competitive Li–S batteries, which is also expected to inform advanced material development in related energy fields.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".