Multifunctional Nano-Architecting of Si Electrode for High-Performance Lithium-Ion Battery Anode
Bibliographic record
Abstract
Silicon (Si)-based anodes for lithium-ion batteries are highly attractive due to their high lithium storage capacity, but their performance is typically plagued by huge volumetric changes during battery cycling. Researchers have traditionally considered the roles of interactive binders and conductive additives as separate entities. Necessary additions of these two components often leads to significantly decreased mass ratio of Si to non-active material, which inevitably limits the anode's absolute capacity. To achieve a better utilization efficiency, a multifunctional composite binder was developed by cross-linking a poly(acrylic acid) (PAA) and carboxymethyl cellulose (CMC) spine with polyacrylonitrile (PAN) through a thermolysis induced nanoarchitecturing (TIN) process. The composite binder strongly interacts with Si, providing a sturdy structure with efficient pathways for both Li-ion and electron transport. The cross-linked carboxyl groups from PAA and CMC offered a robust 3D cross-linked network, anchoring SiO 2 coated Si nanoparticles onto a highly-porous carbon scaffold, creating a stable solid electrolyte interphase. The composite anode not only exhibits a high initial capacity of 3472.6 mAh g −1 with an initial Coulombic efficiency of 89.1%, but also provides excellent cycling stability for 650 cycles at a high current density of 3000 mA g −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) | 0.000 | 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 | 0.001 |
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".