Bibliographic record
Abstract
Active-inactive alloy systems (such as Cu–Sn–Zn) have been proposed to replace graphitic negative electrodes in commercial lithium-ion batteries due to their high theoretical capacity and relatively good cyclability. The performance of these electrode materials is largely determined by their composition. Identifying the optimal composition that provides both large capacity and good cyclability requires the testing of tens, hundreds or even thousands of different alloys. Combinatorial deposition and screening techniques lend themselves well to this kind of optimization study. Galvanostatic deposition in an electrochemical Hull cell is used to fabricate a composition-spread film of binary Sn–Zn alloys. A second step involving a water gun and weak copper sulfate solution is used to achieve a composition spread film of ternary Cu–Sn–Zn alloys. Energy-dispersive spectroscopy and X-ray diffraction are used to characterize the electrodeposited films. The significance and uniqueness of this work is illustrated by our ability to perform combinatorial material science simply and inexpensively. In addition, a phenomenological model is presented to describe the deposition process. The success of the phenomenological model demonstrates we have an understanding, albeit rudimentary, of the immersion deposition process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".