A system for performing simultaneous <i>in situ</i> atomic force microscopy/optical microscopy measurements on electrode materials for lithium-ion batteries
Bibliographic record
Abstract
An atomic force microscope (AFM) equipped with an optical charge coupled device camera has been placed in an Ar filled glovebox for the purpose of studying the change in morphology of electrode materials as they react with lithium. In order to minimize noise induced by vibration, the AFM is mounted on granite blocks suspended from the ceiling of the glovebox by a combination of flexible rubber cords and metal springs. The AFM, which is equipped with an environmental chamber surrounding the sample, is then enclosed in a specially constructed draft shield that allows the circulation of Ar gas by the purification system during imaging. A special electrochemical cell was constructed to hold the working electrode under study. Repeated imaging with little drift is possible while electrodes are reacted with lithium for periods of many days. Examples of measurements made by this device will be given for the case of lithium alloying with sputter-deposited Si–Sn thin films. The optical and AFM images obtained as a function of lithium content in the films are assembled into time-lapsed “movies” showing the evolution of the morphology of the sample along with the corresponding electrochemistry. These movies are available for download through the Electronic Physics Auxiliary Publication Service (EPAPS).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".