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Record W199250851

Analysis of Asphalt Binders for Recycled Engine Oil Bottoms by X-ray Fluorescence Spectroscopy

2015· article· en· W199250851 on OpenAlexaboutno aff
Terence S. Arnold, Anant Shastry

Bibliographic record

VenueTransportation Research Board 94th Annual MeetingTransportation Research Board · 2015
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltWaste managementEnvironmental scienceOil sandsSulfurMotor oilChemistryMetallurgyMaterials scienceEngineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.373
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Board 94th Annual MeetingTransportation Research BoardSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207