Synthesis and Characterization of Tin Oxide By Atomic Layer Deposition for Solid-State Batteries
Bibliographic record
Abstract
The SnO2 material has been considered as a promising lithium-ion battery anode candidate, and recently, the importance has been increased due to its high performance in sodium-ion batteries. Tin oxide (SnO2) has been studied as a promising alternative to the commercially used graphite anode material because of its much higher theoretical specific capacity 1491 mAh/g [1, 2]. Synthesis of tin oxide (IV) thin films was carried out in Picosun R-150 ALD reactor. Synthesis temperature of the process varied from 2500C to 3500C. As a source of tin was used Russian-made tetraethyltin (TET - Sn(C2H5)4, CAS № 597-64-8), purity 99.999%. Inductively coupled remote oxygen plasma and ozone was used as oxygen sources. Thickness of thin films was measured using spectroscopic ellipsometry UVISEL 2 UV-Vis-NIR, and corresponded 75-80 nm. Surface morphology were obtained by scanning electron microscope Mira Tescan. Valence state of tin was studied by X-ray photoelectron spectroscopy (XPS). Electrochemical study of obtained thin-film electrodes was also carried out. The voltage of cycling was ranged from 0.01 V to 0.8 V. Current density was 25 µA/cm2. During cycling tests was shown that tin oxide has a stable discharge capacity about 900 mAh/g during 500 charge/discharge cycles, with efficiency about 99,5 %, fig.1. Capacity fluctuations likely to be associated with changes in ambient temperature. Financial support was made by Ministry of Education and Science of Russian Federation. Unique identifier of the project is RFMEFI57814X0096. [1] Jiajun Chen Materials 6 (2013), 156-183 [2] S.-Y. Lee et al. Nano Energy 19 (2016), 234–245 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) | 0.001 | 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 | 0.001 |
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".