Impact of coordination on ion conduction and electroplating
Bibliographic record
Abstract
This thesis uses two industrially applicable examples to demonstrate the effects of complexation on the electrochemistry of the ion. This includes the study of carboxymethyl cellulose (CMC), an ionically conductive polymer that is an environmentally benign alternative to current polyvinylidene fluoride binders. The other example is an industrial partnership to determine the cause of premature aging in alkaline Cu-Sn electrodeposition baths. In the introduction, background information on coordination and electrochemistry is offered to help understand how these two relate. Some electrochemical techniques that were used are also introduced to explain what information was gained from the experimentation used. Subsequently, research objectives are stated relating the research done to complexation and explaining how it demonstrates the effects of ion complexation on the ion’s electrochemistry. The second chapter is a comprehensive study of the ionic conductivity of CMC. This research has shown that a dry, salt-doped CMC can achieve conductivities similar to the polymer in liquid electrolyte. This is an impressive feat that will allow for the use of CMC in next-generation all- solid-state batteries as a binder with minimal resistive impact on battery performance. Moreover, chapter 3 demonstrates an initial effort into modelling and optimizing an alkaline Cu-Sn electrodeposition bath. The cause for premature solution aging was determined to be caused by the polycarboxylate ligand binding to tin and precipitating out of solution, diminishing the amount of available tin. Lastly, the final chapter summarizes the research outcomes and suggests potential next steps in this research to further understand the impact of these complexing ligands on the electrochemical behaviour of the ions of interest.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".