Mechanically Milled Si-Mn-Fe Alloys as Negative Electrodes for Li-Ion Batteries
Bibliographic record
Abstract
The theoretical volumetric capacity of silicon (2194 Ah/L) is much larger than that of graphite (756 Ah/L). However, the large volume expansion of silicon is still a main concern for practical utilization. One method of reducing the volume expansion of Si is to alloy it with an inactive element. Both iron and manganese are cheap and abundant transition metals and are therefore interesting candidate inactive alloying metals for silicon based anodes. The binary systems, including Si-Fe and Si-Mn, have been previously studied in our group and by others. It has been found that adding Mn or Fe to Si reduces volume expansion during lithiation by forming inactive phases with Si and by inhibiting Li15Si4 formation. The silicides of Fe and Mn are different in structure and stoichiometry. Ball milled alloys in the Si-Fe-Mn ternary system have not been previously reported. Therefore it is meaningful to investigate the Si-Fe-Mn ternary system. Here, Si100-x-y Mn x Fe y ((x,y)=(0,15), (5,12), (10,9), (15,6), (20,3), (25,0)) alloys were prepared by ball milling and their phase composition, microstructure and electrochemistry in Li half cells were studied. The XRD results show that α-Si2Fe and β-Si2Fe are formed in low Mn content samples, i.e. Si85Fe15, Si83Mn5Fe12. As the Mn content is increased, only Si19Mn11 phase is detected. However, the continuous peak shifts in XRD profiles and Mossbauer spectroscopy, shown in Figure 1, demonstrate that Fe atoms are present in the Si19Mn11 structure, to form ternary solid solutions. The potential and differential capacity curves of Si100-x-y Mn x Fe y alloys are typical of alloys in which Si is the only active phase. The formation of Li15Si4 is successfully suppressed in all the samples in the initial cycles. However, Li15Si4 forms after about 10 cycles, as shown in Figure 2. The volumetric capacity and volume expansion of the materials increase with increasing silicon ratio, as shown in Figure 3. In this presentation the composition, microstructure and electrochemistry of alloys in the Si-Mn-Fe system will be thoroughly discussed. Figure 1
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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".