Polymer Nanocomposite Materials Based on Carbon Nanotubes
Bibliographic record
Abstract
In the past two decades, mobile devices have decreased significantly in size and yet their capabilities and storage capacities continue to grow dramatically. Not unexpectedly, the energy demands of these devices are rather substantial, which has led to a huge increase in the level of research into batteries. In recent years, millions of dollars of research funding have been directed towards the development of more efficient battery systems, with a large focus on lithium ion batteries. These batteries are among the most popular for devices with high energy demands due to their high energy capacities. However, despite the impressive performance of lithium ion batteries to date, there is still significant room for improvement. Lithium ion batteries have progressed significantly since they were first developed in the early 1970s. These early systems consisted of lithium metal anodes combined with titanium disulfide cathodes; however, they demonstrated limited cell potentials and these chalcogenidic cathodes were soon replaced by layered oxide systems Many such layered oxides were studied by the Goodenough group These batteries were marketed by the electronics giant, the Sony Corporation, and they consisted of a lithium cobalt dioxide, LiCoO 2 , cathode and a graphitic anode (Nazri & Pistoia, 2004). Lithium metal anodes were discarded earlier in favour of safer systems such as graphite due to the dangers associated with recharging. In the years since the commercialization of lithium ion batteries, there have been many modifications to the three components of the cell: the anode, the cathode and the electrolyte. However, due to the low cost and relative efficiency of graphite as the anode system, very little work has been done in this area. Nonetheless, recently, silicon and germanium nanowires have demonstrated great potential as possible anode materials (Chan et al., 2008a; Chan et al., 2008b). It has long been known that silicon has a greater capacity for lithium ions than does graphite; however, previous attempts employing silicon particles and thin films have demonstrated significant degradation of the materials upon cycling. With these novel nanowires, this degradation is not observed. A small increase in the diameter of the nanowires occurs upon lithium intercalation, but this expansion is reversible upon the removal of the lithium ions. Research is still continuing into these materials due to their enhanced lithium storage capacities; however, these systems are plagued by their considerable expense when compared to graphite. Therefore, www.intechopen.com
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".