Organic Coating for Oxide Removal on Copper Surfaces
Bibliographic record
Abstract
The oxidation of copper surface decreases the surface functional quality, which can be detrimental for several industrial applications. The objective of my research project is the one-pot reduction of copper oxides on copper surfaces, followed by the formation of a stable coating composed of organic molecules that prevent further oxidation. The organic coating will be deposited using four approaches: immersion, resonant acoustic mixing, vapor phase deposition, and electrochemical deposition. By applying mentioned deposition methods, preliminary tests were performed and further characterized by using X-ray photoelectron spectroscopy (XPS) and matrix assisted laser desorption ionization (MALDI), which confirmed the presence of organic molecules on the copper surface. To obtain best coating, optimization is being done by varying various parameters such as concentration of organic molecule solution, time of immobilization, solvent type and influence of temperature. After achieving an optimal coating, next step forward is to conduct bulk corrosion studies to understand the real effect of the coating. Protecting copper surfaces in such a manner can extend the lifetime of copper surfaces, thereby saving resources, time and money in target industrial applications. Figure 1
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".