3D-Structured Porous Carbon Host with Iron Nanoparticles for High Performance Sodium-Metal Batteries
Bibliographic record
Abstract
Sodium (Na)-metal batteries have emerged as a promising alternative to lithium-metal batteries for next-generation batteries due to the low cost and natural abundance of Na. However, high reactivity of Na metal and uncontrollable growth of Na dendrites hinder the safe operation of Na-metal batteries. Several studies have been conducted to resolve those problems and regulating the initial Na nucleation is one of the promising methods for dendrite-free Na-metal anodes. Previous studies reveal that sodiophilic metal nanoparticles (Au, Ag, Sn, Sb, etc.) embedded 3D structures can induce the homogeneous initial Na nucleation and further guide uniform Na deposition. Herein, we newly develop a 3D nanostructured porous carbon particle containing carbon-shell-coated Fe nanoparticles (PC-CFe) as a highly reversible Na-metal host.1 PC-CFe delivers excellent cycling stability in asymmetric cells over 500 cycles with an average Coulombic efficiency of 99.6% at 10 mA cm-2 with 10 mAh cm-2 and in symmetric cells over 14,400 cycles at 60 mA cm-2. The role of carbon coating on Fe nanoparticles for enhanced sodiophilicity is also investigated by DFT calculations. Furthermore, the anode-free Na-metal batteries with a PC-CFe host and a high-loading Na3V2(PO)4 cathode shows an excellent capacity retention of 97% after 100 cycles at 1 mA cm-2. This work provides a novel approach toward the rational design of 3D hosts for next-generation Na-metal batteries. References: 1. Lee, K.; Lee, Y. J.; Lee, M. J.; Han, J.; Lim, J.; Ryu, K.; Yoon, H.; Kim, B.-H.; Kim, B. J.; Lee, S. W. A 3D Hierarchical Host with Enhanced Sodiophilicity Enabling Anode-Free Sodium-Metal Batteries. Adv. Mater. 2022, 34, 2109767.
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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 | 0.001 | 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".