Extraction and Characterization of Nano Precipitates in Microalloyed Steels
Bibliographic record
Abstract
Microalloyed steels are widely used in oil and gas pipelines. They are a class of high strength, low carbon steels containing small additions (in amounts less than 0.1 wt%) of Nb, Ti and/or V. The steels may contain other alloying elements, such as Mo, in amounts exceeding 0.1wt%. Precipitation in these steels can be controlled through thermomechanical controlled processing, leading to precipitates with sizes ranging from several microns to a few nanometers. The larger precipitates are essentially TiN, with partial substitution of Nb for Ti, while the smaller precipitates are based on NbC, with Ti, Mo and V partially substituting for Nb and N partially substituting for C. Microalloyed steels have good strength, good toughness and excellent weldability, which are attributed in part to the presence of the nano-sized carbides and carbonitrides. Because of their fine sizes and low volume fraction, conventional microscopic methods are not satisfactory for quantifying these precipitates. Matrix dissolution is a promising alternative to extract the precipitates for quantification. Relatively large volumes of material can be analyzed, so that statistically significant quantities of precipitates of different sizes are collected. In this paper, matrix dissolution techniques have been developed to extract the precipitates from a series of microalloyed steels. Transmission electron microscopy (TEM) and x-ray diffraction (XRD) are combined to analyze the chemical speciation of these precipitates. Rietveld refinement of the XRD pattern is used to fully quantify the relative amounts of the precipitates. The size distribution of the nano-sized precipitates is quantified using dark field imaging in the TEM.© 2008 ASME
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".