Multiscale investigation of the interfacial bonding mechanisms of magnesium potassium phosphate cement and cement concrete
Bibliographic record
Abstract
The interface bonding between magnesium potassium phosphate cement (MPC) and Portland cement concrete (PCC) is critical for ensuring long-term durability and structural integrity in pavement rehabilitation. However, the underlying interfacial bonding mechanism remains poorly understood at different scales. This paper investigates the mechanism of MPC/PCC interface properties by multiscale methods with macro-microscopic tests and molecular dynamic (MD) simulation. The results demonstrated that the flexural strength of the MPC/PCC specimen was highly sensitive to both crack lengths and fracture modes. Mode II of the specimen consistently showed the superior shear-dominated resistance behaviour. The high porosity and microcracks in sub-ITZs corresponded closely with the gradient change in elastic modulus and fracture toughness, underscoring the critical role of pore distribution in interface mechanical performance. Excellent bonding behaviour and fracture resistance of MPC/PCC interface driven by the formation of phosphate-based hydration products, dense interfacial microstructures and strong interfacial chemical bonding were validated by Raman spectroscopy.
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 | 0.000 | 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 | 0.000 | 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".