Analysis of Asphalt Binders for Recycled Engine Oil Bottoms by X-ray Fluorescence Spectroscopy
Bibliographic record
Abstract
This research, which started in 2010, resulted from discussions with the Illinois Department of Transportation. They had received asphalt binders which appeared to have been modified with phosphoric acid. Subsequent testing showed that they contained a residue from recycling used engine oil. It is also part of the responsibility of the chemistry laboratory staff at the Turner Fairbank Highway Research Center (TFHRC) to develop and provide test methods for use by State agencies and industry for the analysis of pavement materials. Since 2010, the increased use of these residues in paving asphalt binders coupled with negative research findings associated with low temperature cracking published in Canada has generated great interest and controversy in their use, particularly in the New England States. In the U.S. there is a practice of recovering lubricating oils from waste engine oils. The processing generates a residue, recycled engine oil bottoms (REOB). Engine oils contain performance enhancing additives. The sulfur, zinc, calcium, phosphorous and molybdenum from these, and engine wear metals (mainly copper and iron), all end up in the REOB. Their presence can be readily measured using X-ray Fluorescence Spectroscopy (XRF). This paper presents a methodology to detect and measure the presence of REOB in asphalt binders. The analysis is not simple since some of these metals also occur in asphalt, many are found in ground tire rubber and some are found in scavengers sometimes added to asphalt to control emissions of hydrogen sulfide. The variable composition of REOBs imposes some accuracy limitations.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".