Study on thermal stability and biocompatibility of bimodal microstructure in Cr–Mn–N austenitic stainless steel
Bibliographic record
Abstract
Heterostructured austenitic stainless steels (ASS) are becoming a significant research area because of their outstanding mechanical properties and considerable potential for various applications. However, the thermal stability of heterogeneous microstructures, particularly the bimodal microstructure, has received limited attention and lacks systematic investigation. Additionally, there is a scarcity of reports regarding the biocompatibility of bimodal microstructures. Herein, the thermal stability and biocompatibility of the bimodal microstructure prepared by cold rolling and annealing in Cr-Mn-N series ASS (without and with Nb microalloying) are studied. The findings demonstrate that the bimodal microstructures of ASS are formed after annealing at 700 °C, 800 °C, and short-time annealing at 900 °C. Moreover, the addition of Nb significantly enhances the thermal stability of the bimodal microstructure and maintains the bimodal feature up to 1000 °C. The thermal stability of bimodal microstructures depends on the competition in coarse and fine grains growth. The good thermal stability of Nb(C, N) at high temperatures leads to consistently higher pinning force within the fine-grained zone. As a result, the growth of fine grains lags behind that of coarse grains, which leads to the persistence of bimodal microstructure at higher temperatures. The bimodal microstructure of ASS demonstrates superior biocompatibility, attributed to its ability to promote higher cell viability, exhibit stronger fibronectin intensity, and facilitate a wider fibronectin expression network in osteoblasts. These characteristics make the bimodal ASS more favorable for osteoblast attachment and proliferation compared to its coarse-grained counterpart. This study significantly enhances our understanding of the thermal stability and cellular functionality of bimodal ASS, highlighting its potential for various biomedical applications.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".