3D Printing Technology Applied in Lithium Metal Batteries: From Liquid to Solid.
Bibliographic record
Abstract
Li-metal batteries are strongly considered to be one of the most promising candidates for high energy density energy storage devices in our modern society. However, the state-of-the-art Limetal batteries are still limited by several challenges including 1) low energy/power density; 2) Li dendrite growth; 3) low coulombic efficiency, and 4) safety concerns within the liquid electrolyte. This thesis mainly focuses on addressing these challenges by using a 3D printing technique to realize high energy/power density Li-metal batteries.\nA self-standing high areal energy density cathode for Li-S battery was developed by the 3D printing method in the first part. The optimized porosity and conductivity of cathode design from macroscale to the nanoscale are beneficial for Li+/e- transport in a thick electrode. This work offers a new strategy to fabricate high sulfur loading cathodes and improve the electrochemical performance of advanced Li-S batteries.\nHowever, Li+ transport is usually poor in thick cathodes, resulting in low capacity output, fast capacity decay, and large overpotential. To tackle the issue of thick sulfur cathodes, a thickness independent electrode structure is proposed in the second part which can transform a thick electrode into a combination of vertically aligned “thin electrodes”.\nApart from the cathode, Li anode also plays an important role in determining the Li-metal batteries performance. Herein, in the third part, a 3D-printed vertically aligned Li anode (3DP-VALi) is shown to efficiently guide Li deposition via a “nucleation within micro-channel walls” process, enabling a high-performance dendrite-free Li anode.\nIssues like leakage, flammability, and electrochemical instability of liquid electrolytes have triggered safety issues as well as restrictions on the practical application of Li-metal batteries. Herein, in the fourth part, an ultra-high-energy/power density quasi-solid-state Li-Se battery was realized by combining a 3D-printed carbon nanotube interlayer with a high Se-loading gel polymer electrolyte-filled cathodes.\nTo achieve a high energy density all-solid-state Li metal battery, a dual vertically aligned electrodes structure with well-controlled microscale features is proposed in the fifth part to promote the development of fast charging all-solid-state Li metal battery.\nIn summary, these five parts in this thesis provide an important guide to achieve a high energy density Li metal battery by a 3D printing technique
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".