Can the Calcined Weathered Stones be Employed as Anode Materials for Lithium Ion Batteries?
Bibliographic record
Abstract
A novel finding, that the calcined weathered stones (denoted as CWS) can be employed as the anode materials for lithium ion batteries (LIBs), is reported for the first time in this work. Under the air conditions, the weathered stones were respectively calcined at 400ºC (sample a), 600ºC (sample b) and 800ºC (sample c) for 2 h, with an intention to examine the influence of the calcination temperature on the physicochemical properties of the resultant materials. XRD results indicated that the main components of all the final products were SiO2. And the SEM images demonstrated that all the as-prepared samples were irregular and larger particles with no evident crystal structure. The results of the electrochemical measurements revealed that the initial discharge capacity of sample b was about 104 mAh g-1 at the current density of 100 mA g-1, which was remarkably larger than that of the employed pure SiO2 (50 mAh g-1). Interestingly, after 20 cycles, the discharge capacity of sample b was still maintained as high as 70 mAh g-1, along with a capacity retention rate of about 70%. Although the discharge capacity reported here was lower as compared to the currently reported anode materials, this novel finding was very meaningful to the large scale production of anode materials, mainly due to the rather lower cost and abundant resources as well as the simple preparation process.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".