Advanced characterisation of 3D structure and porosity of ordinary portland cement (OPC) mortar using plasma focused ion beam tomography and X‐ray computed tomography
Bibliographic record
Abstract
The visualisation and quantification of pore networks and main phases have been critical research topics in cementitious materials as many critical mechanical and chemical properties and infrastructure reliability rely on these 3D characteristics. In this study, we realised the mesoscale serial sectioning and analysis up to ∼80 μm by ∼90 μm by ∼60 μm on portland cement mortar using plasma focused ion beam (PFIB) for the first time. The workflow of working with mortar and PFIB was established applying a prepositioned hard silicon mask to reduce curtaining. Segmentation with minimal human interference was performed using a trained neural network, in which multiple types of segmentation models were compared. Combining PFIB analysis at microscale with X-ray micro-computed tomography, the analysis of capillary pores and air voids ranging from hundreds of nanometres (nm) to millimetres (mm) can be conducted. The volume fraction of large capillary pores and air voids are 11.5% and 12.7%, respectively. Moreover, the skeletonisation of connected capillary pores clearly shows fluid transport pathways, which is a key factor determining durability performance of concrete in aggressive environments. Another interesting aspect of the FIB tomography is the reconstruction of anhydrous phases, which could enable direct study of hydration kinetics of individual cement phases.
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 | 0.001 | 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".