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Record W4399143435 · doi:10.1016/j.cej.2024.152672

Toward mechanically robust and highly recyclable adsorbents using 3D printed scaffolds: A case study of encapsulated carrageenan hydrogel

2024· article· en· W4399143435 on OpenAlexafffund
Yalda Majooni, Kazem Fayazbakhsh, Nariman Yousefi

Bibliographic record

VenueChemical Engineering Journal · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsAdsorptionMaterials scienceChemical engineeringDurabilityReusabilityNanocompositeSwellingIonic bondingBiopolymerComposite materialPolymerNanotechnologyChemistryPolymer chemistryOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

Biopolymeric hydrogels have emerged as promising materials for water treatment; however, they exhibit limitations in terms of mechanical robustness and durability. To address these shortcomings, we implemented several strategies, such as (i) incorporation of graphene oxide (GO) to expand the range of molecular interactions within the hydrogels, (ii) increasing the degree of carrageenan biopolymer crosslinking by elevating the temperature of the ionic crosslinking bath, thereby enhancing the mechanical robustness of the hydrogels, and lastly, (iii) encapsulation of the nanocomposite hydrogels within 3D printed scaffolds to enhance the hydrogel durability. This study introduces the first adsorbent based on encapsulated hydrogels for water treatment, demonstrating a remarkable increase in its reusability, surpassing previous reports by at least 400 %. Through the optimization of the 3D printed scaffold design, we achieved a 140 % increase in the mass of encapsulated hydrogel, and engineered the available surface area to enhance both the durability and the environmental performance of the hydrogels. The addition of GO increased the adsorption capacity to 166.1 mg/g and the storage modulus at 10 Hz to 12.48 kPa, representing a 55 % and 305 % enhancement compared to the neat hydrogel, respectively. Moreover, the higher degree of ionic crosslinking further increased the storage modulus of the hydrogel by 261 %. Increasing the degree of crosslinking resulted in a lower hydrogel swelling ratio, improved chemical stability, and increased the reusability of the hydrogel beads. Hydrogel encapsulation significantly increased the chemical stability and reusability of the adsorbents. More than 90 % of the initial mass of the encapsulated hydrogel remained intact after 20 regeneration cycles. The reported results present a promising avenue toward the industrial-scale application of sustainable and green hydrogels for water treatment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.238
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations12
Published2024
Admission routes2
Has abstractno

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