(Digital Presentation) Surface Modifications of Cathodes By Coating: A Step Towards Improving the Electrochemical Performance of Lithium Ion Batteries (LIBs)
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) have been widely utilized as power sources for mobile devices. Recently, their use has been expanded to large-scale applications such as electric vehicles (EVs) and energy storage systems (ESSs). These batteries have dominated the energy industry due to their unmatchable properties that include a high energy density, a compact design, and an ability to meet a number of required performance characteristics in comparison to other rechargeable systems. Two vital parameters for LIBs are their stable and safe operation. Since, the cathode serves as a central component of LIBs, the overall cell performance is significantly affected by the chemical and physical properties of the cathode. Cathodes tend to react with the electrolytes and, hence, undergo surface modifications accompanied by degradation. These side-reactions result in an erosion of battery performance and rate capability, thereby causing a reduced battery life and power capacity. Surface coating is the most simple, economical and effective method to protect the cathode surface from degradation and detrimental interfacial reactions with the electrolyte. For the present investigation, a variety of coatings have been used to coat cathode surface. The aim is to explore the effect of these coatings on the physicochemical characteristics as well as electrochemical and thermal properties of spinel cathode using characterization techniques like scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD) and differential thermal analysis (DTA)/ thermogravimetric analysis (TGA).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.205 | 0.075 |
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".