The Effect of Impurities (H<sub>2</sub>O, O<sub>2</sub>, SO<sub>2</sub>, NO, and NO<sub>2</sub>) on Supercritical CO<sub>2</sub> Structures in Relation to CO<sub>2</sub> Pipeline Transport
Bibliographic record
Abstract
Supercritical CO 2 (s-CO 2 ) pipeline transport is a critical component of the carbon capture and storage system. One primary safety concern of the pipeline structural integrity is corrosion and stress corrosion cracking induced by the presence of aggressive impurities in the transported high-pressure s-CO 2 streams. Although a considerable number of studies have been conducted to address s-CO 2 corrosion, fundamental knowledge gaps, particularly the influence of these corrosive impurities on s-CO 2, remain to be addressed. This study employs molecular dynamics simulations to investigate the effects of representative impurities (H 2 O, O 2, SO 2, NO, and NO 2 ) on s-CO 2 structures under the designed s-CO 2 pipeline transportation conditions. The results indicate that the self-interactions among s-CO 2 molecules shall be enhanced with the introduction of trace amounts of impurities, reaching a plateau value, and then weaken with further increases in impurity concentrations. For the impurities investigated, s-CO 2 exhibits an affinity in the order of NO > NO 2 > SO 2 > O 2 > H 2 O. In the s-CO 2, H 2 O molecules tend to aggregate locally, while other impurity molecules are uniformly distributed. Similar to the pure s-CO 2 scenario, s-CO 2 molecules can still form T-shapes with neighboring s-CO 2 molecules in the presence of impurities. Besides, s-CO 2 molecules show the tendency to form T-shape structures with all the examined impurities except H 2 O. There is no preferential structure presented between CO 2 and H 2 O due to the H 2 O aggregation. These findings advance the understanding of how the impurities affect s-CO 2 structures and consequently lead to different corrosion damage to s-CO 2 pipeline steels.
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 | 0.000 | 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".