Polyphosphoric Acid–Modified Binders and Mixtures: Aggregate and Binder Interactions, Rutting, and Moisture Sensitivity of Mixtures
Bibliographic record
Abstract
Polyphosphoric acid (PPA) has been increasingly used as a means of producing modified binders for the past 10 to 15 years in North America. Reports of isolated or regional use of phosphoric acid and PPA prior to the advent of Superpave performance grade (PG) binders have been published, but the increased demand for high-performance binders resulting from the adoption of PG binders stimulated more widespread research into the means by which PPA could effectively and economically enable binder suppliers to meet these demands. Consequently asphalt suppliers in all regions of the United States and Canada turned to PPA to meet the new specifications. It was found that PPA, when used at levels as low as 0.5% by weight of binder, could increase the high-temperature PG of some binders by one full grade. Most binders required approximately 0.8% to 1.2% PPA by weight of binder and some required considerably more; sometimes more than 2%. Still other asphalt suppliers found that the addition of low levels, typically less than 0.5% by weight, of PPA to polymer-modified binders enabled them to reduce polymer loading without negatively impacting mixture performance and in some reported cases enhancing mixture performance. Almost simultaneously with the onset of PPA usage, concerns were raised by a cross section of individuals, organizations, and agencies associated with the asphalt production and supply, bituminous paving and governmental sectors. These concerns were manifested by fears of mixture stripping because of the hygroscopic nature of PPA and fears of accelerated aging and adverse effects on low-temperature properties of both binders and their mixtures because of the well-known use of phosphoric acids, PPA, and phosphorus pentoxide to catalyze the production of roofing asphalt during the blowing process. There was also the often unstated but ever-present belief that purchasers of PPA-modified binders were being cheated because they were not receiving polymer when purchasing some premium PG grades. Some of these concerns were justified, many were not. The information in this study endeavors to put some perspective around these concerns, to show where there might be cause for concern and where there is not. This document does not provide an answer to all questions and it raises a few questions that still need to be answered.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".