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Record W4283069587 · doi:10.1002/cjce.24507

A comprehensive review on the synthesis techniques of porous materials for gas separation and catalysis

2022· review· en· W4283069587 on OpenAlexvenueno aff
Vikrant Sharma, Ankit Agrawal, Omvir Singh, Reena Goyal, Bipul Sarkar, Navin Gopinathan, Sarang P. Gumfekar

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typereview
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsPorosityGas separationPorous mediumNanotechnologyMaterials scienceCatalysisAdsorptionChemical engineeringProcess engineeringMembraneChemistryEngineeringOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Intrinsic structural characteristics of porous materials have found significant applications in selective separation of gases and heterogeneous catalysis. While using porous materials for gas separation and catalysis, some of the challenging issues are the strength of catalytic sites, hydrothermal stability, crystalline order of solids, and selectivity. Researchers' ability to engineer the synthesis strategies rationally can help overcome technological challenges. In recent years, breakthroughs in porous materials are focused on developing different chemistries to control the pore architecture. This review focuses on recent advances made in synthesis strategies of porous materials and their impact on gas separation and catalysis applications. A significant part of this review is devoted to various synthesis methods, such as various types of templating methods, molecular layer deposition, sol–gel technique, and polymerization methods. Improvement in various catalytic reactions and gas separations due to different functionalization methods is also summarized. Lastly, we have discussed the applications of porous materials in the form of adsorbents and membranes in commercial processes. We hope that this review will serve as a quick reference for beginners who want to synthesize porous materials with control of pore structure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.506
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.292
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2022
Admission routes1
Has abstractyes

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