Transbattery, a Novel Class of Device to Study Electronic Properties of Nanostructured Materials for Energetics.
Bibliographic record
Abstract
International energy policies aim to promote the efficient use of renewable energy sources and sustainable electric transport. Furthermore, the growing demand for energy autonomy is pushing research towards the development of low consumption/ self-powered electronic devices. This requires high-efficiency energy storage/conversion systems. In this context, the research is strongly committed to the development of new nanostructured electrode materials, especially for lithium batteries. Nowadays, attention is also paid to the miniaturization of electrochemical energy storage systems and their on-a-chip integration. Therefore, here we report a systematic study on the electronic conductivity of thin films of nanostructured electrode materials for lithium-ion batteries. In particular, attention has been devoted to high voltage cathodic and stable anodic active materials. This has been addressed, for the first time, using an "Electrolyte gated transistor" as cell configuration. Indeed, with this cell configuration, it has been possible to evaluate the electronic characteristics of the materials in situ, i.e. during the redox processes that are at the basis of battery operation. In this study correlation between morphological and electronic properties of the material under charge/discharge cycling has been studied in order to monitor their evolution. The effect of different electrolytes such as commercial ones and, super-concentrated has been studied as well as the cycling performance. Finally, it has been possible to determine the most appropriate strategy to prepare the electrodes and select the right electrolyte in order to guarantee high current output and stability. Acknowledgments The research has been carried out under the Executive Bilateral Program Italy-Quebec 2017-2019 “Electronic properties of nanostructured materials for energetics” (M02241, QU17MO01 ).
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".