Toward High Areal Capacity Lithium Batteries Via 3D Printing: From Liquid to Solid
Bibliographic record
Abstract
3D-printing has found wide applications in numerous research fields, ranging from mechanical engineering, medicine, and material science to chemistry. Among them, it is capable of fabricating electrodes with high active material loading and improved ion/electron conductivity, and is thereby a promising method to improve the energy and power density of energy storage systems. And this technique offers a fresh viewpoint in designing high loading cathodes and will arise interest in other energy storage devices such as Li-ion batteries, Li-S batteries, and Li-Se batteries, etc. In this talk, I will talk about 3D-printed high active material loading cathode applied in batteries: from liquid to solid. In the first part of this talk, I will introduce 3D-printed high S loading cathode applied in liquid-based Li-S batteries. Compared with conventional cathode fabrication method, a 3D-printed S cathode with grid structure, which could facilitate Li+/e- transport at the macro, micro and nano scale in Li-S batteries. Moreover, a thickness-independent S cathode structure is also proposed via converting thick electrode into thousands of vertically aligned thin electrode by 3D printing. And each thin electrode delivers a constant thickness of around 20 μm, which is not affected by the intrinsic thickness of electrode as well as sulfur loading. Compared with other high S loading Li-S batteries performace, this work demonstrate a similar electrochemical kinetics in spite of the total sulfur loading or thickness of the electrode. [1-2] In the second part of this talk, I will talk about 3D-printed ultra-high Se loading cathode applied in solid-state Li-Se batteries. Compared with other Li-Se work, this work exhibits excellent cycling stability and remarkable rate performance with the highest reported Se loadings of 20 mg cm-2, and also delivers the highest reported areal capacity of 12.99 mA h cm-2 under a current density of 3 mA cm-2. The 3D-printed Se cathodes with grid structure provide large spaces for polymer electrolyte impregnation to further build interconnected Li+ transport channels in thick electrodes, enabling fast Li+ transport in solid-state Li-Se batteries. [3] References [1] X. Gao, X. Sun* et al, Nano Energy , 2019, 56, 595-603. [2] X. Gao, X. Sun* et al, Energy Storage Mater. Doi.org/10.1016/j.ensm.2019.08.001. [3] X. Gao, X. Sun* et al, J. Mater. Chem. A , under revision. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".