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Monolithic carbon aerogels from bioresources and their application for CO2 adsorption

2021· article· en· W3165994352 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroporous and Mesoporous Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAdsorptionChemical engineeringDesorptionCarbon fibersPorosityMaterials scienceSelectivityFlexibility (engineering)Activated carbonChemistryOrganic chemistryComposite materialCatalysisComposite number

Abstract

fetched live from OpenAlex

Monolithic binder-free CO2 adsorbents with high adsorption capacity, selectivity, adsorption-desorption kinetics, and regenerability are highly desired to both reduce the environmental impact of anthropogenic CO2 emissions and purify valuable gases from CO2. Herein, we report a strategy to prepare monolithic carbonaceous CO2 adsorbents from low-cost and underutilized bioresources, which enabled the formation of a delicate anisotropic, hierarchical porous structure. With optimized material composition and processing conditions, the biobased carbon adsorbent demonstrated a CO2 adsorption capacity of 4.49 mmol g-1 at 298 K and 100 kPa, relatively weak adsorbent-adsorbate affinity, good CO2/N2 selectivity, and advantageous hydrophobicity against water vapor. Moreover, the unique anisotropic porous structure provided high stiffness and good flexibility to the adsorbent in the axial and radial directions, respectively. We confirmed that this type of carbon adsorbent could be packed in a column for dynamic CO2 capture independent of any binders, indicating its promising future for further development toward widespread utilization.

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.

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 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 score0.012
Threshold uncertainty score0.908

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)0.0000.000

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 head0.005
GPT teacher head0.181
Teacher spread0.175 · 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