Introduction to the Principles of Electrochemistry through an Affordable Hands-on Electrochemical CO<sub>2</sub> Reduction Experiment
Bibliographic record
Abstract
We are grateful for the invitation to this session by Prof. Alice H. Suroviec. Electrochemical conversion of CO2 to useful chemicals and synthetic electrochemistry in general are rapidly developing and expanding areas of research and industrial application, which necessitates developing undergraduate curricula that will help students build and retain a strong foundational knowledge in this area of chemistry. Currently, there is a limited set of electrochemistry experiments that undergraduate students are typically exposed to in the laboratory, and synthetic electrochemistry is generally limited to water electroreduction to generate hydrogen gas. As a result, students develop limited knowledge, interest and appreciation of synthetic electrochemistry. At the same time, students today are more inclined to learn about methods and concepts that are important for climate action and sustainable development. With this motivation in mind, we developed a laboratory experiment designed to actively engage students in the learning process and help them understand synthetic electrochemistry through a hands-on experience involving CO2 electroreduction. In general, CO2 electroreduction can yield many different chemicals and requires complex and expensive electrochemical workstation and analytical instrumentation to identify and quantify the products, making it prohibitive for wide adaptation in undergraduate laboratories. Here, we propose a simple and affordable setup that still allows students to directly experience all the necessary steps of the process. The proposed laboratory experiment involves testing the performance of different cathodic electrocatalysts in CO2 reduction reaction conducted in a DIY divided electrochemical cell by measuring the produced CO gas with a CO meter using affordable and broadly available supplies. The students learn the importance of the electrocatalyst composition by changing the material of the cathode and observing different amounts of CO produced or the absence of CO when using the electrode selective for only water reduction. They learn about the influence of the applied potential on the reaction rate by changing the battery voltage and observing the quantitative difference in the produced gas. The experiment is designed to be safe when conducted on a standard laboratory bench (i.e., carbon monoxide concentrations outside of the cell are below the threshold of a standard CO detector).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".