Mechanochemical and interfacial engineering of orthosilicates as Li-ion battery cathodes
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) are in ever-growing demand for portable electronics and all electrical vehicles (EVs).This necessitates the development of new electrode materials especially cathodes with improved charge capacity & rate to meet the full application potential for large scale commercialization of EVs.The development of new cathode materials for LIBs is an active field of research.In this thesis, the relatively less explored low-temperature orthorhombic (Pmn21) phase of lithium iron orthosilicate (Li2FeSiO4, LFS) is studied as a potential candidate for highenergy density cathode.In particular, LFS is synthesized, mechanochemically tuned, interfacially modified, and electrochemically characterized aiming to the design of stable and high-density performing cathodes.In this context, the first part of this research focused on mechanochemical processing of hydrothermally synthesised single phase low-temperature orthorhombic Pmn21 LFS (ortho-LFS) particles.Further, the structural and electrochemical behavior of mechanochemically treated LFS was probed via synchrotron-based X-ray diffraction (XRD), electron backscatter diffraction (EBSD), high-resolution transmission electron microscopy (HR-TEM) and galvanostatic charging/discharging, cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) & differential capacity, respectively.The mechanochemical study led to the discovery that within a certain milling window, annealing-like structural changes occur opening up the pathway to higher accessible Li-ion storage capacity.More specifically controlled highenergy mechanical nanosizing was found to induce structural changes including lattice expansion, reduction of antisite defects and crystallinity improvement.Next, to improve the electronic conductivity of ortho-LFS, high-energy milling in the presence of carbon black was employed at room temperature producing an LFS@C nanocomposite.During follow up electrochemical cycling of this material, it was discovered that such LFS@C nanocomposite exhibits electrochemically induced structural activation leading to doubling its reversible capacity from ~90 mAh g -1 to ~180 mAh g -1 .Interestingly, this impressive increase in charge capacity was associated with simultaneous transitioning from solid solution to two-phase Li-ion storage mechanism clearly indicating in-operando structural transformation unlike previous studies, which attributed such capacity increase to mere electrolyte penetration.To probe further this behaviour, ex-situ post-mortem analysis of the electrode bulk & surface In the name of God, the most Merciful and Beneficent.I present this thesis and
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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 | 0.001 |
| 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 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".