Evidence map›Paper›PMID 42787700›Full record

ReviewFrontiers in chemistry2026

Sustainable zeolite synthesis: advances in green strategies and AI-assisted design.

Beidi Li, Nur Hidayatul Nazirah Kamarudin, Wan Nor Roslam Wan Isahak, Noorashikin Md Saleh, Yaqin Tian

Abstract readReview
In one paragraph

Review in Frontiers in chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Beidi LiDepartment of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Nur Hidayatul Nazirah KamarudinDepartment of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Wan Nor Roslam Wan IsahakDepartment of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Noorashikin Md SalehDepartment of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Yaqin TianCollege of Energy and Building Engineering, Shandong Huayu University of Technology, Dezhou, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Zeolites are porous materials with regular nanochannels, high surface area, tunable acid sites, excellent thermal and hydrothermal stability, and outstanding catalytic performance. These properties make them indispensable in energy and chemical engineering, environmental remediation, selective adsorption, and clean energy technologies. However, conventional hydrothermal synthesis suffers from high energy consumption, strong alkali corrosion, excessive template usage, and the generation of salt containing and organic waste liquids, limiting its sustainability. Consequently, green chemistry based synthesis strategies have become a major research focus, aiming to optimize resource utilization, environmental impact, and material performance simultaneously. This review summarizes recent advances in green zeolite synthesis, including solvent free or low solvent methods, low template or template free strategies, directed agent synthesis, ionothermal synthesis, dry gel conversion, and the use of renewable silicon and aluminum resources. The underlying reaction mechanisms and performance control principles are critically discussed. Through representative case studies, the review highlights how these approaches reduce activation energy, improve atom efficiency, minimize waste generation, and enable precise control of crystal morphology and pore structures. Future perspectives include AI-driven materials design, accurate topology prediction, machine learning-assisted structural characterization and generation, and the optimization of synthesis conditions.

Indexed as

artificial intelligence designgreen synthesis chemistryionothermal synthesisrenewable silicon and aluminumzeolite

Identifiers

PMID42787700
PMCPMC13601927

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.