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Record W4401092269 · doi:10.1039/d4nr01688b

Nanoporous air filtering systems made from renewable sources: benefits and challenges

2024· review· en· W4401092269 on OpenAlexafffund
Arnab Dutta, Solmaz Karamikamkar, Mohammadreza Nofar, Ehsan Behzadfar

Bibliographic record

VenueNanoscale · 2024
Typereview
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsToronto Metropolitan University
KeywordsNanoporousRenewable energyFilter (signal processing)Environmental scienceProcess engineeringAir filterMaterials scienceNanotechnologyBiochemical engineeringComputer scienceEngineeringMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

There is a crucial need for air purification systems due to increasing air contamination, while conventional air-filtering materials face challenges in eliminating gaseous and particulate pollutants. This review examines the development and characteristics of nanoporous polymeric materials developed from renewable resources, which have rapidly advanced in recent years. These materials offer more sustainable alternatives for nanoporous structures made out of conventional polymers and significantly impact the properties of porous polymers. The review explores nanoporous materials' production from renewable sources, filtering mechanisms, physicochemical makeup, and sensing capabilities. The recent advancements in this field aim to enhance production techniques, lower pressure drop, and improve adsorption efficiency. Currently, supporting approaches include using adsorbent layers and binders to immobilize nanoporous materials. Furthermore, the prospects and challenges of nanoporous materials obtained from renewable sources used for air purification are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.047
GPT teacher head0.252
Teacher spread0.206 · 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.

Study designNot applicable
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

Citations10
Published2024
Admission routes2
Has abstractyes

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