Hierarchical Micro‐Nanoclusters of Bimetallic Layered Hydroxide Polyhedrons as Advanced Sulfur Reservoir for High‐Performance Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Rational construction of sulfur electrodes is essential in pursuit of practically viable lithium–sulfur (Li–S) batteries. Herein, bimetallic NiCo‐layered double hydroxide (NiCo‐LDH) with a unique hierarchical micro‐nano architecture is developed as an advanced sulfur reservoir for Li–S batteries. Compared with the monometallic Co‐layered double hydroxide (Co‐LDH) counterpart, the bimetallic configuration realizes much enriched, miniaturized, and vertically aligned LDH nanosheets assembled in hollow polyhedral nanoarchitecture, which geometrically benefits the interface exposure for host–guest interactions. Beyond that, the introduction of secondary metal intensifies the chemical interactions between layered double hydroxide (LDH) and sulfur species, which implements strong sulfur immobilization and catalyzation for rapid and durable sulfur electrochemistry. Furthermore, the favorable NiCo‐LDH is architecturally upgraded into closely packed micro‐nano clusters with facilitated long‐range electron/ion conduction and robust structural integrity. Due to these attributes, the corresponding Li–S cells realize excellent cyclability over 800 cycles with a minimum capacity fading of 0.04% per cycle and good rate capability up to 2 C. Moreover, highly reversible areal capacity of 4.3 mAh cm−2 can be achieved under a raised sulfur loading of 5.5 mg cm−2. This work provides not only an effective architectural design but also a deepened understanding on bimetallic LDH sulfur reservoir for high‐performance Li–S batteries.
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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) | 0.001 | 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".