Bibliographic record
Abstract
The densities and refractive indices of two binary systems (water + MEA and water + MDEA) and three ternary systems (water + MEA + CO2, water + MDEA + CO2and water + MEA + MDEA) used for carbon dioxide (CO2) capture were measured over the range of compositions of the aqueous alkanolamine(s) used for CO2absorption at temperatures from 295 to 338 K. Experimental densities were modeled empirically while the experimental refractive indices were modeled using well- established models from knowledge of their pure component densities and refractive indices. The density and Gladstone-Dale refractive index models were then used to obtain the compositions of unknown samples of the binary and ternary systems by simultaneous solution of the density and refractive index equations. The results from this technique have been compared with HPLC (high performance liquid chromatography) results, while a third independent technique (acid-base titration) was used to verify the results. The results show that the systems' compositions obtained from the simple and easy to use refractive index/density technique were very comparable to the expensive and laborious HPLC/titration techniques, implying that the refractive index/density technique can be used to replace existing methods for analysis of fresh or non degraded, CO2 loaded, single and mixed alkanolamine solutions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".