Two-Dimensional Protective Layers of MX<sub>3</sub> to Stabilize Lithium and Sodium Metal Anodes
Bibliographic record
Abstract
The development of metal anodes is severely restricted by challenges such as dendrite growth, uncontrolled interface reactivity, and huge volume change in energy storage devices. Inserting ideal two-dimensional (2D) protective layers with matched mechanical performance and fast ionic transport character is an effective strategy to stabilize metal anodes. Herein, a family of 2D metal trihalides MX 3 (ScCl 3, ScBr 3, ScI 3, YCl 3, YBr 3, and YI 3 ) with natural atomic pore structures have been explored to examine the protective performance for the Li (Na) metal anode by utilizing first-principles calculations. The theoretical results indicate that 2D MX 3 /Li (Na) systems are energetically stable by analyzing the binding energies. A Young’s modulus of ∼14.68–29.61 GPa and the high shear modulus of pristine 2D MX 3 materials are suitable to ensure close contact and inhibit dendrite formation compared with conventional inorganic solid electrolytes. In addition, the introduction of metal anodes has a negative effect on the stiffness of the pristine 2D MX 3 . Nevertheless, critical strain can be achieved by about ∼12%. Furthermore, the energy barriers within ∼0.37–0.63 eV for ionic transport through monolayer MX 3 at the MX 3 /Li (Na) interface demonstrate fast ionic transport. These theoretical results provide fundamental guidance for 2D MX 3 materials with excellent mechanical performance and fast ionic transport as the ideal protective layers to achieve a high-performance Li (Na) metal anode in the relevant energy storage systems.
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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".