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Record W4311589579 · doi:10.1002/adma.202209327

Direct CO<sub>2</sub> Capture by Alkali‐Dissolved Cellulose and Sequestration in Building Materials and Artificial Reef Structures

2022· article· en· W4311589579 on OpenAlexafffund
Guillermo Reyes, Mabel Vega‐Coloma, Anna Antonova, Rubina Ajdary, Solène Jonveaux, Colleen Flanigan, Nathalie Lautenbacher, Orlando J. Rojas

Bibliographic record

VenueAdvanced Materials · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of British ColumbiaUniversité de Sherbrooke
FundersCanada Foundation for Innovation
KeywordsCelluloseMaterials scienceAbsorption of waterChemical engineeringCellulose fiberAbsorption (acoustics)FiberComposite material

Abstract

fetched live from OpenAlex

Abstract Current carbon capture and utilization (CCU) technologies require high energy input and costly catalysts. Here, an effective pathway is offered that addresses climate action by atmospheric CO 2 sequestration. Industrially relevant highly reactive alkali cellulose solutions are used as CO 2 absorption media. The latter lead to mineralized cellulose materials (MCM) at a tailorable cellulose‐to‐mineral ratio, forming organic‐inorganic viscous systems (viscosity from 10 2 to 10 7 mPa s and storage modulus from 10 to 10 5 Pa). CO 2 absorption and conversion into calcium carbonate and associated minerals translate to maximum absorption of 6.5 gCO 2 g cellulose −1 , tracking inversely with cellulose loading. Cellulose lean gels are easily converted into dry powders, shown as a functional component of ceramic glazes and cementitious composites. Meanwhile, cellulose‐rich gels are moldable and extrudable, yielding stone‐like structures tested as artificial substrates for coral reef restoration. Life Cycle Assessment (LCA) suggests new CCU opportunities for building materials, as demonstrated in underwater deployment for coral reef ecosystem restoration.

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 categoriesInsufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.246
Teacher spread0.237 · 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 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

Citations27
Published2022
Admission routes2
Has abstractyes

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