Carbon Nanotubes Doped with Silicon for Use in the Anode of Lithium Ion Batteries
Bibliographic record
Abstract
The lithium ion battery is an attractive and environmental friendly energy source because of its large versatile applications ranging from portable electronics systems to support renewable energy sources. In addition, lithium ion batteries have the highest energy density among others known chemical batteries and are the most promising technology for portable applications. However, to satisfy such applications is essential to improve some components of lithium ion battery. One of the main challenges is to increase their electrochemical performance by improving the storage of lithium ions in the anode electrode. Graphite has been the most successful materials for the anode electrode, which has reached a specific capacity of 372 mAh g-1. However, those materials have reached its storage capacity limit. Thus, alternative supports which can provide higher capacity than graphite are needed. Carbon nanotubes (CNT) can be a promissory alternative material, especially those that are doped with heteroatoms. The physical and chemical properties of CNT are modified by the dopant atoms intrusion in the carbon lattice that improve the electrical conductivity, morphology and the structure of carbon. The present study shows the synthesis and characterization of silicon doped carbon nanotubes (Si-CNT). Si-CNTs were synthesized by a modified chemical vapor deposition using toluene as carbon source and ferrocene as metal catalyst for the nanotubes growth. Triphenylsilane were used as silicon doping. The effects of various parameters including furnace temperature and silicon concentration were investigated using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X ray diffraction (XRD) and Raman spectroscopy. The results showed that the morphological and physical properties of Si-CNT, such as lengths, diameter, defects, and wall thickness, changed in comparison to those of undoped CNT. According to the Raman results the doped nanotubes showed major defect in the structure; this phenomenon can be explained by the integration of the silicon atoms in the structure of the nanotubes, which increase the order of the graphitic network. On the other hand, the elemental atomic composition analysis using SEM by energy-dispersive x-ray spectroscopy (SEM-EDS) showed that silicon is presented with Si content of 0.25±0.09 at. %. The Si-CNT properties and its evaluation as anode materials in lithium ion batteries are discussed.
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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".