Monitoring Atomic Layer Deposition by Potentiodynamic Electrochemical Impedance Spectroscopy of Multielement Adlayers
Bibliographic record
Abstract
Atomic multilayer assembly monitoring is now possible using a method that has been extended from multiparametric characterization of changeable electroactive interfaces using potentiodynamic electrochemical impedance spectroscopy (PDEIS).The multilayers were created by depositing adlayers of Sb, Se, and Sn on Ag in a sequential fashion, and by depositing an adlayer of Zinc on an antimony underlayer that was supported by Silver.The multilayers were characterized using the potentiodynamic mode, which utilizes the relationship between AC circuit characteristics and electrode potential.The dependences show changes in the double electric layer at the interface, and in diffusion and charge transfer.While Ag/Sbad/Sead/Agad and Ag/Sead/Agad exhibit considerable similarity in the Faradaic part of the ac response, the dependencies of the distinctive variables of the Ag/Sbad/Sead/Snad (Ag/Sbad/Sead/Snad) composite threelayer differ significantly from those of the Ag/Sbad/Snad and Ag/Sead/Snad bilayers, respectively.The adlayer oxidation potential shifts dramatically when tin is deposited on a bi-chalcogen Ag/Sbad/Sead underlayer, proving that the upd of Sn, Ag, and Zn on chalcogen adlayers is irreversible.Silver can partially dissolve in the Ag adlayer oxidation potential when it penetrates the Ag/Sbad/Sead bilayer, unlike the Sn adlayer that forms on top of the Antimony-selenium bilayer and dissolves completely in the anodic scan within the stability scope of the chalcogen composite underlayer.Electrochemical nanotechnologies can utilize PDEIS for monitoring layer-by-layer deposition by leveraging the self-descriptive nature of potential dependences of circuit variables..
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".