Finely‐Dispersed Ni<sub>2</sub>Co Nanoalloys on Flower‐Like Graphene Microassembly Empowering a Bi‐Service Matrix for Superior Lithium–Sulfur Electrochemistry
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries present a promising solution to high‐energy and low‐cost energy storage. However, the conversion‐type redox mechanism determines the poor fulfillment of battery chemistry in terms of reversibility and kinetics. Herein, a flower‐like graphene microassembly decorated with finely‐dispersed Ni2Co nanoalloy (Ni2Co@rGO) is developed as advanced host matrix for Li–S batteries. Combining computational, physicochemical, and electrochemical studies, Ni2Co nanoalloys are unveiled synergizing strong adsorbability against polysulfide shuttling and excellent catalytic activity for sulfur conversions. Meanwhile, the sophisticated architecture renders facile electron/ion transport and highly‐exposed active interfaces. These virtues collaboratively contribute to fast and durable sulfur electrochemistry with a minimum capacity degradation of 0.034% per cycle over 500 cycles and a rate capability up to 5 C. Besides, the implementation of Ni2Co@rGO as the anode matrix tames the Li redox behavior benefiting from the enhanced lithiophilicity and reduced local current density. As such, the full cell configuration pairing S‐Ni2Co@rGO cathode and Li‐Ni2Co@rGO anode realizes a favorable areal capacity of 4.53 mAh cm−2 under high sulfur loading (4.0 mg cm−2) and limited electrolyte (E/S = 6.0 mL g−1). This work offers an elaborate bi‐service matrix engineering to simultaneously improve the conversion reversibility and kinetics for superior 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".