Ethylenediamine-Enabled Sustainable Synthesis of Mesoporous Nanostructured Li<sub>2</sub>Fe<sup>II</sup>SiO<sub>4</sub> Particles from Fe(III) Aqueous Solution for Li-Ion Battery Application
Bibliographic record
Abstract
Engineering of nanostructured lithium iron silicate (LFS) particles is pursued via a novel benign synthesis approach seeking to understand the crystalline particle formation process and its impact on energy storage capacity. Specifically, mesoporous Li 2 FeSiO 4 nanostructured particles are synthesized via a novel dual-step process involving organic-assisted hydrothermal precipitation from concentrated Fe(III) (1 mol/L) aqueous solution followed by reductive (5 vol % H 2 ) thermal transformation of the precipitate at 400 °C (LFS400) and 700 °C (LFS700). Scanning and transmission electron microscopy revealed the formation of secondary sub-micron-sized porous agglomerates of unitary primary nanocrystals (∼50 nm for LFS400 and ∼200 nm for LFS700). Both ethylene glycol and ethylenediamine are used as crystallization control additives. It is demonstrated that formation of LFS from Fe(III) precursor is made possible only by the action of ethylenediamine. The obtained LFS particles are found to be predominantly monoclinic as per X-ray diffraction and Rietveld refinement and bear an in situ formed N-doped carbon coating layer as characterized by X-ray photoelectron spectroscopy. TEM coupled with selected area electron diffraction (SAED) analysis confirmed the Rietveld refined XRD phase compositions. The reductive annealing-induced phase transformation sequence leading to LFS crystallization is characterized, and the enabling role of ethylenediamine is discussed. Initial galvanostatic charging–discharging and cyclic voltammetry measurements indicate the annealing temperature of LFS formation to influence the Li-ion storage profile as it shifts from two-phase reaction in LFS700 to solid solution in LFS400—this being attributed to nanostructural changes.
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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) | 0.000 | 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".