Zeolites: A Theoretical and Practical Approach with Uses in (Bio)Chemical Processes
Bibliographic record
Abstract
This review provides a state-of-the-art summary of distributed zeolite technology, as well as identifying strategies to further promote the absorption of these materials in various areas of study. Zeolites are materials that can be synthesized or found in natural rock deposits a with a basic composition consisting in Al, Si, and O. Zeolite’s consideration as a future material is due to many facile synthesis methods to obtain different structures with variations in pore size, surface area, pore volume and physical properties. These methods are developed using the control of relevant synthesis parameters that influences structure formation, such as crystallization temperature, time of aging and/or crystallization, stoichiometric relationships between components of synthesis gel, pH of the medium, and in some cases the type of structure-directing agent. Each method will lead to geometric changes in the framework formation, making possible the formation of typical chemical bonds that are the fingerprint of any zeolitic structure (O-Si-O and Al-O-Si), forming typical acid sites that give specificity in zeolite and allows it to act as a nanoreactor. The specificity is a characteristic that in some cases depends on selectivity, a fundamental property derived of the porosity, mostly in processes that occur inside the zeolite. In processes outside the structure, the surface area is the main factor influencing this property. Moreover, there are many natural sources with adequate chemical composition to be used as precursors. Some of these sources are waste, minimizing the deposition of potential hazardous materials that can be recalcitrant pollutants depending on the environment. Besides its uses as a catalyst, zeolite serves as a support for many bioprocesses; therefore, this review aims to explain relevant aspects in chemical nature, physical properties, main methods of synthesis, main precursors used for synthesis, and relevant applications of zeolites in chemical catalysis and biological processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".