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Record W2798217456 · doi:10.3390/pr6040035

Structure Manipulation of Carbon Aerogels by Managing Solution Concentration of Precursor and Its Application for CO2 Capture

2018· article· en· W2798217456 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

VenueProcesses · 2018
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Ottawa
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsAerogelFourier transform infrared spectroscopyAdsorptionScanning electron microscopeMicroporous materialCarbon fibersResorcinolMaterials scienceRaman spectroscopyDesorptionChemical engineeringFormaldehydeSelectivityChemistryNanotechnologyCatalysisOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A series of carbon aerogels were synthesized by polycondensation of resorcinol and formaldehyde, and their structure was adjusted by managing solution concentration of precursors. Carbon aerogels were characterized by X-ray diffraction (XRD), Raman, Fourier transform infrared spectroscopy (FTIR), N2 adsorption/desorption and scanning electron microscope (SEM) technologies. It was found that the pore structure and morphology of carbon aerogels can be efficiently manipulated by managing solution concentration. The relative micropore volume of carbon aerogels, defined by Vmicro/Vtol, first increased and then decreased with the increase of solution concentration, leading to the same trend of CO2 adsorption capacity. Specifically, the CA-45 (the solution concentration of precursors is 45 wt%) sample had the highest CO2 adsorption capacity (83.71 cm3/g) and the highest selectivity of CO2/N2 (53) at 1 bar and 0 °C.

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 score0.048
Threshold uncertainty score0.389

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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