Organic Template-Assisted Synthesis & Characterization of Active Materials for Li-ion Batteries
Bibliographic record
Abstract
The Lithium-ion (Li-ion) battery is one of the major topics currently studied as a potential way to help in reducing greenhouse gas emissions. Major car manufacturers are interested in adapting the Li-ion battery in the power trains of Plug-in Hybrid Electric Vehicles (PHEV) to improve fuel efficiency. Materials currently used for Li-ion batteries are LiCoO2 (LCO) and graphite—the first materials successfully integrated by Sony into Li-ion batteries. However, due to the high cost and polluting effect of cobalt (Co), and the low volumetric capacity of graphite, new materials are being sought out. LiFePO4 (LFP) and SnO2 are both good alternatives for the cathode and anode materials in Li-ion batteries. But, to create high-performance batteries, nano-sized carbon-coated particles of LFP and SnO2 are required. The present work attempts to develop a new synthesis method for these materials: organic template-assisted synthesis for three-dimensionally ordered macroporous (3DOM) LFP and porous SnO2. With the newly developed synthesis, highly pure materials were successfully synthesized and tested in Li-ion batteries. The obtained capacity for LFP was 158m Ah/g, which is equivalent to 93% of the theoretical capacity. The obtained capacity for SnO2 was 700 mAh/g, which is equivalent to 90% of the theoretical capacity. Moreover, Hybrid Pulse Power Characterization (HPPC) was used to test LFP and LCO for comparison and feasibility in PHEVs. HPPC is generally used to test the feasibility and capacity fade for PHEVs. It simulates battery use in various driving conditions of PHEVs to study pulse energy consumption and regeneration. In this case, HPPC was conducted on a half-cell battery for the first time to study the phenomena on a single active material, LFP or LCO. Based on the HPPC results, LFP proved to be more practical for use in PHEVs.
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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".