Optimising Zeolite Synthesis for Efficient Carbon Capture and Conversion to Renewable Natural Gas
Bibliographic record
Abstract
The growing need for cleaner energy production has spurred the development of advanced materials capable of addressing environmental challenges. This has driven research into materials that can capture carbon dioxide while maintaining sustainability and cost-effectiveness. Zeolites, aluminosilicate materials synthesised from abundant elements, present a promising solution as dual-function materials (DFMs) for both CO2 capture and conversion to renewable natural gas (RNG) via methanation. To understand how structural differences can affect CO2 adsorption, this study investigates the synthesis and performance of three zeolite types: small (Chabazite, CHA), medium (MFI, ZSM-5), and large (Faujasite, FAU, Y-zeolite). Each was synthesised from scratch using silica and aluminium precursors, with fluoride and alkali promoters to facilitate framework formation. This encompassed a sequence of material addition, gel aging, crystallisation, and drying to produce the powder. Among these materials, the Y-zeolite was hypothesised as the optimal candidate due to its large pore structure—-providing the most abundant number of sites for CO2 adsorption, assessed by exposing the materials to simulated air streams (400 ppm CO2). Confirmed through X-ray Diffraction (XRD) and CO2 Temperature-Programmed Desorption (TPD), Y-zeolite demonstrated high CO2 adsorption capacity and structural stability across repeated cycles. However, CHA exhibited sensitivity to water, whereas ZSM-5 synthesis trials remained inconsistent, requiring further optimisation to be achievable in the laboratory. Future work will focus on refining synthesis procedures for repeatability, evaluating long-term performance under realistic conditions, and assessing candidacy for industrial scale-up. These findings propel zeolites as viable materials for power-to-gas (P2G) applications, contributing to carbon emission reduction measures.
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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 | 0.001 | 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".