(Sn[sub 0.5]Co[sub 0.5])[sub 1−y]C[sub y] Alloy Negative Electrode Materials Prepared by Mechanical Attriting
Bibliographic record
Abstract
Samples of for were prepared in increments of using a vertical-axis attritor. The effect of the carbon content on the structure and performance of the Sn–Co–C nanocomposites was examined by X-ray diffraction (XRD), Mössbauer effect spectroscopy, and electrochemical methods. Thermal stability aspects of these nanocomposites were inferred from differential scanning calorimetry (DSC) and surface area measurements. XRD experiments show diffraction patterns characteristic of nanostructured materials, except for the sample without carbon, which shows broad Bragg peaks of . Mössbauer effect spectroscopy shows that the samples are best described as Sn–Co grains surrounded by a carbon matrix. DSC of the samples in air showed crystallization of CoSn for samples with low carbon content and combustion of carbon for samples with high amounts of carbon. The specific surface area of the samples was less than for samples with . Excellent charge–discharge capacity retention was observed for samples with . Samples with acceptable electrochemical performance and low reactivity with air at elevated temperature were found in the range .
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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 | 0.000 | 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) | 0.002 | 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".