SIFT-MS For Carbon Dioxide Reduction Reaction
Bibliographic record
Abstract
Electrochemical CO2 reduction reaction (CO2R) is a promising route to help reduce greenhouse gas emissions and reach carbon dioxide net zero emissions to combat global warming. Currently, in order to investigate catalytically produced products from CO2R offline methods such as gas chromatography (GC) and nuclear magnetic resonance (NMR) are used. These offline methods have a time resolution on the minutes to hours scale which leads to uncertainty of evaluating how products are produced from CO2R, such as knowing if a product is produced from electrochemical means or chemical conversion, and if a product is being produced in a linear rate of production or a different rate. This is where the ability to have real-time analysis of the products generated from CO2R is desirable, as it can more definitively answer many of these questions. Yet few analytical techniques have been developed in detail so far to achieve real-time analysis. Herein, we show the use of selected-ion flow-tube mass spectrometry (SIFT-MS) that quantitatively measures in realtime an array of 10 C1, C2, and C3 products from CO2R such as ethanol, ethylene or methane. The custom-developed SIFT-MS selected ion mode scan measures the concentration of gas and liquid-phase products of CO2R at the same time and is compatible with any electrolyzer cell. We demonstrate that the SIFT-MS technique can reliably and accurately determine product concentration in real-time through the evaluation of Cu foil and its comparison to traditional techniques. Considering the narrow range of developed and deployed techniques for real-time quantitative product analysis for CO2R, this study on SIFT-MS is a critical tool for future research in accelerating and optimizing catalyst design for electrochemical CO2R applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.006 |
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".