A Universal Roadmap for Quantification of Glycerol Electrooxidation Products Using Proton Nuclear Magnetic Spectroscopy (<sup>1</sup>H NMR)
Bibliographic record
Abstract
The electrochemical oxidation of glycerol, a byproduct of biofuel production, can transform this low-value material into a range of valuable compounds to foster a circular bioeconomy. However, developing catalysts and unraveling the mechanisms behind glycerol oxidation hinge on the precise quantification of reaction products, a task requiring analytical techniques. Our research introduces proton nuclear magnetic spectroscopy ( 1 H NMR) as a viable alternative to existing chromatographic and spectroscopic techniques, allowing a sensitive, rapid, and nondestructive detection and quantification of glycerol oxidation reaction products. Utilizing 1 H NMR, we outline a comprehensive framework for identifying the products formed during glycerol oxidation with a focus on the chemical transformations that occur within the reaction medium. As a proof of concept, we employ a platinum catalyst for glycerol electro-oxidation in an alkaline electrolyte, specifically examining the influence of pH and identifying potential discrepancies in calculating Faradaic efficiencies for the reaction. Comparison of the results from 1 H NMR with literature results obtained using high-performance liquid chromatography (HPLC) showed that both methods are coherent. To demonstrate the capabilities, a mixture containing known quantities of glycerol oxidation reaction products was analyzed by 1 H NMR. Concentrations quantified by 1 H NMR matched the real concentrations with an error margin of less than 8%, demonstrating the effectiveness of 1 H NMR for the analysis of a complex mixture of species. These findings lay the groundwork for the broader integration of 1 H NMR into complex liquid product analyses, particularly in organic mixtures.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".