High Mass Loading of Edge-Exposed Cu<sub>3</sub>P Nanocrystal in 3D Freestanding Matrix Regulating Lithiophilic Sites for High-Performance Lithium Metal Anode
Bibliographic record
Abstract
Lithium (Li) dendrites and volume expansion during repeated Li plating and stripping processes are the major obstacles to the development of advanced Li metal batteries. Li nucleation and dendrite growth can be controlled and inhibited spatially by using 3-dimensional (3D) hosts together with efficient lithiophilic materials. To realize next-generation Li-metal batteries, it is critical to effectively regulate the surface structure of the lithiophilic crystals. Herein, exposed-edged Cu 3 P faceted nanoparticles anchored along the interlaced carbon nanofibers (ECP@CNF) are developed as a highly efficient 3D Li host. Through the 3D interlaced rigid carbon skeleton, volume expansion can be accommodated. The (300)-dominant edged crystal facets of Cu 3 P with abundant exposed P 3– sites not only exhibit strong micro-structural Li affinity but also have relatively high charge transference to nucleate uniformly and effectively, resulting in reduced polarization. Consequently, under a high current density of 10 mA cm –2 with a high discharge of depth (60%), ECP@CNF/Li symmetric cells demonstrate outstanding cycling stability for 500 h with a small voltage hysteresis of 32.8 mV. Notably, the ECP@CNF/Li∥LiFePO 4 full cell exhibits a more stable cycling performance for 650 cycles under a high rate of 1 C, with capacity retention up to 92% (N/P = 10, 4.7 mg cm –2 LiFePO 4 ). Even under a limit Li (3.4 mA h) with an N/P ratio of 2 (8.9 mg cm –2 LiFePO 4 ), ECP@CNF/Li∥LiFePO 4 full cell can also demonstrate excellent reversibility and stable cycling performance with higher utilization of Li. This work provides an insight view into constructing high-performance Li-metal batteries under more strict conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".