Tuning Metallic Co0.85Se Quantum Dots/Carbon Hollow Polyhedrons with Tertiary Hierarchical Structure for High-Performance Potassium Ion Batteries
Bibliographic record
Abstract
Abstract Potassium-ion batteries (KIBs) are a potential candidate to lithium-ion batteries (LIBs) but possess unsatisfactory capacity and rate properties. Herein, the metallic cobalt selenide quantum dots (Co0.85Se-QDs) encapsulated in mesoporous carbon matrix were designed via a direct hydrothermal method. Specifically, the cobalt selenide/carbon composite (Co0.85Se-QDs/C) possesses tertiary hierarchical structure, which is the primary quantum dots, the secondary petals flake, and the tertiary hollow micropolyhedron framework. Co0.85Se-QDs are homogenously embedded into the carbon petals flake, which constitute the hollow polyhedral framework. This unique structure can take the advantages of both nanoscale and microscale features: Co0.85Se-QDs can expand in a multidimensional and ductile carbon matrix and reduce the K-intercalation stress in particle dimensions; the micropetals can restrain the agglomeration of active materials and promote the transportation of potassium ion and electron. In addition, the hollow carbon framework buffers volume expansion, maintains the structural integrity, and increases the electronic conductivity. Benefiting from this tertiary hierarchical structure, outstanding K-storage performance (402 mAh g−1 after 100 cycles at 50 mA g−1) is obtained when Co0.85Se-QDs/C is used as KIBs anode. More importantly, the selenization process in this work is newly reported and can be generally extended to prepare other quantum dots encapsulated in edge-limited frameworks for excellent energy storage.
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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".