Synthesis, Characterization and Process Evaluation of an Amine Grafted Monolith for Temperature Vacuum Swing Adsorption-based Direct Air Capture
Bibliographic record
Abstract
The development of efficient, scalable adsorbents is crucial to advancing Direct Air Capture (DAC) technologies. Here, we present the material and process characterization of an in-house developed amine-functionalized alumina adsorbent. To begin, a sensitivity analysis was conducted by varying the amine loading (low, medium, and high) on pristine alumina powder. Equilibrium and uptake measurements indicated a CO₂ loading of ~1.0 mmol/g (0.4 mbar, 30°C). A dual-site Langmuir-Freundlich isotherm model was used to describe the CO2 isotherms. Uptake measurements at different particle sizes and temperatures were used to identify the underlying mass-transfer resistances. A dual-kinetic model that incorporated film diffusion, macropore diffusion and a temperature-dependent amine resistance was employed to describe the system's breakthrough dynamics at different temperatures and concentrations. The functionalization method was scaled to a monolith that is washcoated with alumina up to a thickness of 90 μm. Subsequently, dynamic column breakthrough experiments were performed on the monolith. Additionally, tracer experiments using a 1% He-N2 mixture were conducted on the monolith at varying interstitial velocities to quantify axial dispersion. These experimental findings were incorporated into a 1-D process model, and key performance indicators, specifically, productivity and specific energy consumption, were determined for a 5-step temperature vacuum swing adsorption process for DAC. The breakdown of energy consumption into thermal and electrical contributions was also determined.
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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) | 0.000 | 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 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".