High Temperature Electrooxidation of Glycerol on Nickel
Bibliographic record
Abstract
This work involves the long term electrooxidation of glycerol in a custom autoclave setup. Experiments are usually performed over approximately 48 hours. The custom autoclave setup was adapted from work done by Thomas Holm [1,2]. Within the autoclave a typical three electrode cell is built, wherein all glass pieces have been replaced with alkaline stable counterparts. This is largely done with Teflon, however a Nafion membrane is used to separate the working and reference electrode volumes from the bulk volume at the counter electrode to avoid contamination at the reference, and to keep the oxidation volume small. A small oxidation volume leads to higher concentrations of products, allowing them to be more easily detected using HPLC. Analysis of the oxidized products is performed via HPLC using an Aminex HPX-87H column. Using these techniques we have been able to show that the temperature at which electrooxidation occurs significantly alters the selectivity of products, as well as causing some new products to form (See Figure 1). Calibration curves have been made up for each of these products using known standards, and so we are able to quantitatively determine how much of each product is formed. However, the carbonate peak is not quantitative, and so carbonate was separately analysed by gravimetric or titrimetric methods. With this information we have been attempting to carry out a carbon balance between products and reactants to ensure all products has been accounted for. Though some reasonable values have been attained for this, problems arise when an electron balance is attempted. Much more oxidation is carried out than is expected from the number of electrons used during the experiment, and so we can conclude that some chemical interactions are occurring in solution. It is likely that some products are reacting with the alkaline solution, perhaps through chemical disproproportionation reactions. The existance of such reactions can be confirmed for glyceraldehyde, which has been shown to exist on a short time scale using PM-IRRAS [3] even though no glyceraldehyde is detected by HPLC. We find that HPLC glyceraldehyde standards decompose into other compounds that we see here. Work is ongoing to establish the identity and distribution of products as a function of the reaction conditions. This research was conducted as part of the Engineered Nickel Catalysts for Electrochemical Clean Energy project administered from Queen’s University and supported by Grant No. RGPNM 477963-2015 under the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Frontiers Program. 1. T. Holm, P.K. Dahlstrøm, S. Sunde, D. A. Harrington, F. Seland, ECS Trans, 75, 1055, (2016). 2. T. Holm, P.K. Dahlstrøm, O.S. Burheim, S. Sunde, D.A. Harrington, and F. Seland, Electrochim. Acta., 222, 1792 (2016). 3. M.S.E. Houache, E. Cossar, S. Ntais, E.A. Baranova, J. Power Sources, on the web, doi:10.1016/j.jpowsour.2017.08.089. Figure 1
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".