Steam adsorption equilibrium data at 110°C on an activated carbon, Lewatit VP OC 1065, and CALF-20 using a microscale dynamic column breakthrough apparatus
Bibliographic record
Abstract
Steam adsorption equilibrium data is important for many carbon capture applications, including direct-air capture, point-source, and pre-combustion carbon capture. However, there is a dearth of concentrated unary equilibrium data for H2O at temperatures greater than 100°C. In this study, unary steam adsorption equilibrium data was measured at 110°C and up to approximately 1.0 bar partial pressure with a steam microscale dynamic column breakthrough (μDCB) apparatus using a milligram quantities of adsorbent. These partial pressures are equivalent to those used in industrial carbon capture processes. The construction of the apparatus is detailed, along with the necessary calibrations and validations to ensure accurate equilibrium measurement. Three adsorbents were considered in this study: an activated carbon (Calgon BPL), an amine-functionalized polymer (Lewatit VP OC 1065), and a metal-organic framework (CALF-20). It was observed that steam adsorbs strongly on all three materials at 110°C. Activated carbon and Lewatit displayed type-3 isotherm shapes, and CALF-20 displayed a type-5 isotherm shape. Activated carbon and Lewatit were modeled with the GAB isotherm, and CALF-20 was modeled with the cubic-Langmuir isotherm. The 110°C data was compared against H2O equilibrium data collected at 25°C as a function of relative pressure. An in-house dynamic column breakthrough simulator coded in MATLAB was able to predict the μDCB composition breakthrough curves well for all three materials. The data obtained from the steam breakthrough experiments has important process implications and highlights the need to measure steam adsorption data for any proposed carbon capture material.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".