Development of Nanostructured LiMPO4 (M=Fe, Mn) as Cathodes for High Performance Lithium-Ion Batteries
Bibliographic record
Abstract
Olivine LiFePO4 has garnered the most interest because of its environmental benignity, high safety and theoretical capacity. However, the major limitation for LiFePO4 is the intrinsically poor electronic conductivity and ionic conductivity. The sluggish kinetics for LiFePO4 could be overcome by reducing the size, coating with conductive carbon, or doping with isovalent ions. The decrease of the size to nanoscale could shorten the diffusion time of Li ions in LiFePO4 during intercalation/deintercalation process, but the nano-size active material usually accompanies with low tap density. Carbon coating and carbon addition could alleviate the poor electronic conductivity. However, simple or nonuniform carbon coating cannot obtain an ideal electrochemical performance due to the fact that the electrons could not reach some positions where Li ions charge/discharge takes place. The research in this thesis aims at developing high electrochemical performance of the LiFePO4 composite.\nIn this research, we proposed as follows: (1) three dimensional (3D) porous LiFePO4 in microscale. The porous strategy could allow efficient percolation of the electrolyte through the electrode, favoring the electrolyte access to active materials via the pores, then make full use of electrode material; (2) the nanosized LiFePO4 anchors in the 3D conducting network. This could achieve fast electronic and ion conduction, leading to high performance of the composites.\nTherefore, we first reported 3D porous LiFePO4 with N-CNTs, CNTs and graphene fabricated by using sol-gel approach. The highly conductive and uniformly dispersed N-CNTs and graphene nanosheets incorporated into 3D interlaced porous LiFePO4, which could facilitate the electric and lithium ion diffusion rate, thus resulting in high performance of LiFePO4 electrodes.\nWe also reported the nanosized and unfolded graphene modified LiFePO4 composites.The LiFePO4 nanoparticles anchored to 3D conducting unfolded graphene network resulted in almost theoretical capacity (171 mAh g-1). One-dimensional LiFePO4@CNTs nanowires have been prepared, while 3D CNTs conducting network structure was also obtained simultaneously. The LiFePO4@CNTs nanowires can give excellent cycling stability and rate capability.\nThe effect of Mn concentration on the morphology of LiFePO4 and the electrochemical performance have been investigated\nIn summary, the discoveries in this thesis contribute to a better understanding and design on LiFePO4 candidate and provide novel hierarchical nanostructured materials as electrodes applied in LIBs as power sources for EVs or HEVs.
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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 | 0.001 |
| 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".