Diffusion of nitrogen in solid titanium at elevated temperature and the influence on the microstructure
Bibliographic record
Abstract
Nitrogen introduction to solid commercially pure titanium has been carried out at 1650 °C in an electric induction furnace using two different methods. An effective way to avoid the formation of the hard and brittle nitride layer (TiN and Ti2N) is reported. Microstructure and microhardness were examined on the cross-section of the nitrided samples. Multiple phase layers can be observed, and the phases in each layer were identified using X-ray Diffraction. The effects of the experimental conditions such as temperature and nitriding time on the kinetics of nitrogen diffusion were investigated. The nitrogen content within the samples was increased with increasing temperature and nitriding time. Correlations between microhardness and the nitrogen concentration have been developed for the phase(s) present in the core and the outer layers. The diffusion of nitrogen in solid titanium was simulated numerically, and the predicted nitrogen concentration profile in the rods and displacement of Ti–N phase interfaces show good agreement with the experimental observations. Energy-dispersive X-ray spectroscopy and the numerical simulation results suggest that β phase boundary composition is 2.8 wt. % N, the α phase exists within the compositional range of 5.5–6.5 wt. % and the Ti–N phase within the compositional range 7.0–15.0 wt. %, which differs from data extracted from a published phase diagram.
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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 | 0.000 | 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".