Ternary Cross-Linked Multi-Functional Blended Polymers for High-Performance Silicon Anodes in Lithium-Ion Batteries
Bibliographic record
Abstract
Silicon (Si) based anode material is highly attractive for next-generation lithium‐ion batteries (LIBs) due to its unparalleled theoretical capacity and abundance. However, a severe problem of Si is the significant volume change associated with the lithiation processes, resulting in reduced capacity retention. Researchers have traditionally considered the roles of interactive binders and conductive additives as separate entities. These two components often lead to remarkably decreased mass ratio of Si to nonactive material, which inevitably limits the electrode capacity. To achieve an enhance utilization efficiency, herein, we have developed a multifunctional nanocomposite binder for high capacity nano-size Sibased material through a cross-linked polymer of carboxymethylcellulose (CMC), polyacrylic acid (PAA) spine with graphenized polyacrylonitrile (PAN) through a gradual carbonizing route. This nanocomposite strongly interacts with Si material, providing a robust nanoarchitecture with abundant conductive pathways for Li-ion transport. CMC and PAA with a large number of carboxyl groups provide binding ability by sturdy three-dimensional (3D) cross-linked network with relatively a thin SiO2 mechanical binding film on the surface of Si nanoparticles onto a highly porous carbon and forming a stable solid electrolyte interface (SEI) layer. Meanwhile, graphenized PAN provides a highly interconnected conductive nanoarchitecture of a nitrogen-doped graphenized-like structure (NG), which provides enhanced pathways for lithium ion diffusion. Without any conductive additives, this nanocomposite material not only shows a high superior 1st discharge capacity of 3473 mAh g-1, high initial Coulombic efficiency of 89%, excellent rate capability, and remarkable cycling life for more 600 cycles when cycled at high current density of 3000 mA g-1, but also maintaining good cyclability with a constant high areal capacity of ~ 2.7 mAh cm-2. Together with the ease of fabrication, this provides a promising avenue for commercial LIBs. Figure 1
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".