Biomimetic aerogels with hierarchical honeycomb architecture for superior CO2 adsorption, selectivity, and structural integrity
Bibliographic record
Abstract
In structured adsorbents, achieving mesoporosity, crucial for efficient gas adosorption, is challenging, which restricts mass transport and accessibility to active sites. Here, we address this limitation by developing the first hierarchically porous honeycomb aerogels that replicate hexagonal pores at both the macro-level and micro-level wall structure. This design, inspired by nature’s most efficient patterns, enables us to achieve CO₂ adsorption capacity (3.94 mmol g−¹ at 298 K and 1 bar), selectivity (65.2 CO₂/N₂), and high specific surface area (370 m² g−¹). The honeycomb aerogels are constructed from manganese dioxide (MnO₂) functionalized electrochemically exfoliated graphene (MEEG) and chitosan (CS). By optimizing the MnO₂ loading and the MEEG to CS weight ratio, we achieved dual-scale hexagonal porosity, enabling a hybrid physical and chemical adsorption mechanism. The hybrid adsorption leverages the rapid kinetics of chemisorption and ease of regeneration characteristic of physisorption, making these materials highly efficient. This highlights the synergy between enhanced surface accessibility of primary amine groups and selective adsorption properties, setting a new standard for hierarchically structured materials. Honeycomb structures maximize surface area, important for gas adsorption, but replicating them synthetically is challenging. Here, an aerogel with hexagonal porosity was synthesized with MnO₂-functionalized electrochemically exfoliated graphene for carbon dioxide adsorption and high selectivity over nitrogen
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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) | 0.001 | 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".