Comparative Study of Si-Ti Thin Films and Alloys As Lithium Ion Anodes
Bibliographic record
Abstract
Si based materials have been intensively studied due to the high theoretical capacity of Si (2194 Ah/L, 3579 mAh/g [1, 2]) and applicable lithiation voltage (around 0.4 V [3]). To address the issues brought by severe volume change during the charge-discharge process, the concept of active-inactive Si based alloys has been developed. Si can form intermetallic compounds with some inactive (towards Li) metals, such as Fe, Co, Mn and Ni [4]. These inactive Si1-xMx phases dilute the expansion of Si and thereby improve electrode cycling performance. Recent reports suggested that the voltage curves of Si1-xMxalloys can be shifted by hundreds of millivolts, presumably due to the stress induced by the volume expansion of active Si while being bonded to the inactive phase [5]. In this work, Si1-xTix (0 ≤ x ≤ 0.4, Δx = 0.05) alloys were prepared both by ball milling and magnetron sputtering. The structure evolution of these alloys and their electrochemical performance as negative electrodes for lithium ion batteries were compared. The capacity dependence of the Si-Ti alloys on Ti content suggests that these alloys comprise an active Si phase and an inactive Si phase. A voltage shift was observed during the lithiation process of Si1-xTix thin-films. Increasing the Ti content (x ≤ 0.3) was found to gradually lower the lithiation voltage by a shift of up to 600 millivolts, while the average voltage during delithiation process remains constant. (Figure 1). The voltage shift during discharge suppresses the formation of the Li15Si4phase at full lithiation, resulting in improved cycling performance. This presentation will discuss the structural and electrochemical characteristics of sputtered and ball-milled Si-Ti alloys and their relation to alloy reversible capacity and cycle life. Acknowledgements The authors acknowledge funding from NSERC and 3M Canada, Co. under the auspices of the Industrial Research Chair and Discovery grant programs. We also acknowledge the support of the Canada Foundation for Innovation, the Atlantic Innovation Fund and other partners that fund the Facilities for Materials Characterization managed by the Institute for Research in Materials. Yukun Wang acknowledges the support from SCUT Doctoral Student Short-term Overseas Visiting Study Funding Project. References [1] M.N. Obrovac and L. Christensen, Electrochem. Solid-State Lett., 7 (5) A93, (2004). [2] M.N. Obrovac and L.J. Krause, J. Electrochem. Soc., 154(2) A103, (2007). [3] L. Baggetto, R.A.H. Niessen, F. Roozeboom, and P.H.L. Notten, Adv. Funct. Mater., 18(7) 1057 (2008). [4] Z. Du, T.D. Hatchard, R.A. Dunlap and M.N. Obrovac, J. Electrochem. Soc., 162 (9) A1858, (2015). [5] Z. Du, S.N. Ellis, R.A. Dunlap and M.N. Obrovac, J. Electrochem. Soc., 163 (2) A13, (2016). 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 | 0.001 | 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".