Nickel-Seeded Silicon Nanowires Grown on Graphene as Anode Material for Lithium Ion Batteries
Bibliographic record
Abstract
There is a growing interest for relying on cleaner and more sustainable energy sources due to the negative side-effects of the dominant fossil-fuel based energy storage and conversion systems. Cleaner, electrochemical energy storage through lithium-ion batteries has gained considerable interest and market value for applications such as electric vehicles and renewable energy storage. However, capacity and rate (power) limitations of current lithium-ion battery technology hinder its ability to meet the high energy demands in a competitive and reliable fashion. \n \nSilicon is an element with very high capacity to Li-ion storage although commercially impractical due to its poor stability and rate capabilities. Nevertheless, it has been heavily researched with more novel electrode nanostructures to improve its stability and rate capability. It was found that silicon nanomaterials such as silicon nanowires have inherently higher stability due to mitigation of cracking and higher rate capability due to the short Li-ion diffusion distance. However, electrode compositions based only on silicon nanowires without additional structural features and a high conductive support do not have enough stability and rate capability for successful commercialization. One structural and conductive support of silicon materials studied in literature is graphene. Graphene-based electrodes have been reported as material capable of rapid electron transport enabling new strides in rate capabilities for Li ion batteries. \n \nThis thesis presents a novel electrode nanostructure with a simple, inexpensive, scalable method of silicon nanwire synthesis on graphene nanosheets via nickel catalyst. The research herein shows the different electrode compositions and variables studied to yield the highest achievable capacity, stability and rate capability performance. The carbon coating methodology in addition to enhancing the 3D conductivity of the electrode by replacing typical binders with pyrolyzed polyacrylonitrile provided the highest performance results.
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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".