Solar-driven photothermal desorption of CO₂ from PEI-infused silica gel
Bibliographic record
Abstract
In light of the pressing global demand for effective carbon capture technologies to combat climate change, it is crucial to explore carbon dioxide (CO 2 ) adsorbent regeneration strategies that can be scaled and driven by renewables. Photothermal regeneration emerges as a viable approach for enhancing the efficiency and scalability of CO 2 capture systems. This study conducts a systematic investigation into laboratory-scale photothermal regeneration applied to polyethyleneimine (PEI)-infused silica gel. We evaluated three regeneration techniques: traditional thermal heating, direct illumination, and indirect photothermal heating, wherein solar-simulated light was incident onto an adsorbent bed coated with solar-selective paint. The results show that indirect photothermal regeneration substantially improves desorption kinetics, shortening the desorption time to ∼3058 s (with a desorption capacity of ∼0.28 mmol/g), compared to ∼3892 s (for traditional thermal heating, with a desorption capacity of ∼0.23 mmol/g) and ∼ 6025 s (with a desorption capacity of ∼0.28 mmol/g) for the direct illumination configuration. Surface analyses validated that indirect photothermal heating preserves the sorbent's integrity and composition, whereas direct illumination leads to discoloration and localized degradation. This laboratory-scale investigation presents the first geometrically matched comparative analysis of thermal, direct, and indirect photothermal regeneration modalities within a PEI–silica fixed-bed system. Additionally, it introduces novel thermal-uniformity metrics that provide a quantitative explanation for the approximately 22 % reduction in desorption time observed in the indirect regeneration mode. Furthermore, this work highlights the potential integration of PEI-infused silica gel systems with flat-plate solar collectors, supporting the development of fully renewable, solar-powered carbon capture technologies.
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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 | 0.000 | 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 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".