Effect of O<sub>2</sub> on Microcarbon Residue Standards Analysis
Bibliographic record
Abstract
The effect of the presence of controlled amounts of O 2 upon microcarbon residue (MCR) determination is presented. The amount of O 2 leading to an increase in MCR that is mistakenly considered as trace or small has been quantified by performing experiments using a thermogravimetric analyzer (TGA) and heating the samples in a muffle furnace under different O 2 concentrations. For a fixed sample amount, MCR may increase or decrease depending upon the O 2 concentration in the atmosphere above the sample. TGA results indicate that, in addition to pyrolysis that occurs at all times during heating of the sample, there are two competitive reactions, namely, partial oxidation and combustion that dominate each other depending upon the O 2 concentration above the sample. If the O 2 content appears limiting, there would be an increase in MCR because partial oxidation would dominate over the combustion. If the amount of the sample is limiting, then the O 2 concentration would be high enough for the combustion reaction to dominate over partial oxidation, leading to low MCR values. Commercially available MCR standards are studied in this work. Their Fourier transform infrared (FTIR) spectra determined before and after heating in an inert or oxidative atmosphere show that the presence of small amounts of O 2 affect these samples to varying levels depending upon their chemical composition. The study carried out with few standard samples may not appear appropriate to apply on a broad range of oil samples; however, the achieved results suggest that MCR determinations should better be conducted under truly inert conditions.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".