A new spin on electrochemistry in the undergraduate lab
Bibliographic record
Abstract
Abstract The growing interest in electrochemistry over recent years has sparked an increase in the popularity of various electrochemical techniques, including more advanced methods, that have previously been overlooked in academia and industry. This makes comprehensive hands-on experience in electrochemistry a highly demanded addition to chemistry graduates. However, many students do not receive sufficient training in the theory and experimental design to confidently use and apply various electrochemical techniques throughout their undergraduate, and sometimes even in graduate studies. Here we summarize the theory and practical applications for both rotating disk electrode (RDE) and rotating ring disk electrode (RRDE) techniques. The different modes of operation of rotating ring disk voltammetry, methodologies of data analysis and interpretation as well as the scope of the information that can be extracted from the RDE/RRDE are discussed. Proposed modifications of the laboratory curriculum will allow students to examine and learn valuable information about the reactions on the surface of the electrode/liquid interface. This information will allow chemists to confidently use RDE and RRDE techniques for a wide range of research and development targets. Furthermore, incorporating these techniques into existing chemistry laboratories will help chemistry educators to enrich the undergraduate chemistry curriculum and improve students’ learning outcomes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".