Matrix Dissolution Techniques Applied to Extract and Quantify Precipitates from a Microalloyed Steel
Bibliographic record
Abstract
Microalloyed steels possess good strength and toughness, as well as excellent weldability; these attributes are necessary for oil and gas pipelines in northern climates. These properties are attributed in part to the presence of nanosized carbide and carbonitride precipitates. To understand the strengthening mechanisms and to optimize the strengthening effects, it is necessary to quantify the size distribution, volume fraction, and chemical speciation of these precipitates. However, characterization techniques suitable for quantifying fine precipitates are limited because of their fine sizes, wide particle size distributions, and low volume fractions. In this article, two matrix dissolution techniques have been developed to extract precipitates from a Grade100 (yield strength of 690 MPa) microalloyed steel. Relatively large volumes of material can be analyzed, and statistically significant quantities of precipitates of different sizes are collected. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) are combined to analyze the chemical speciation of these precipitates. Rietveld refinement of XRD patterns is used to quantify fully the relative amounts of the precipitates. The size distribution of the nanosized precipitates is quantified using dark-field imaging in the TEM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".