Electrochemical Study of a Polarized Electrochemical Vapor Deposition Process
Bibliographic record
Abstract
The interfacing of modern vapor deposition technology and solid‐state ionic technology has led to the recent development of polarized electrochemical vapor deposition (PEVD). Due to the unique electrocrystallization behavior of its products and easy process control through a solid electrochemical cell, PEVD holds promise for a wide range of potential applications. However, the migration of charged ionic and electronic carriers during PEVD is a kinetic process. The task related to studying PEVD working electrode kinetics in this investigation is to explain the sequence of partial reactions constituting the overall PEVD electrochemical reaction for product formation at the working electrode. The PEVD process for auxiliary phase deposition at the working electrode of a potentiometric sensor was selected for the current electrochemical studies. The dependence of current density, or of reaction rate, upon various working electrode overpotentials and temperatures (500–550°C) was studied by a steady‐state potentiostatic method. The PEVD reaction rate‐limiting steps are solved for samples undergoing second‐stage growth. The results from this investigation help in understanding the kinetics of the PEVD reaction and subsequent product formation in a PEVD system, improve knowledge of PEVD kinetics, and elucidate the possibility of further process control in PEVD. © 2000 The Electrochemical Society. All rights reserved.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".