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Record W4221144481 · doi:10.3390/catal12020137

Controlled Transition Metal Nucleated Growth of Carbon Nanotubes by Molten Electrolysis of CO2

2022· article· en· W4221144481 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

VenueCatalysts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsMaterials scienceChemical engineeringElectrolysisCarbon nanotubeAnodeCarbon fibersNanotechnologyNanomaterialsElectrolyteElectrochemistryCathodeElectrodeChemistryComposite materialComposite number

Abstract

fetched live from OpenAlex

The electrolysis of CO2 in molten carbonate has been introduced as an alternative mechanism to synthesize carbon nanomaterials inexpensively at high yield. Until recently, CO2 was thought to be unreactive, making its removal a challenge. CO2 is the main cause of anthropogenic global warming and its utilization and transformation into a stable, valuable material provides an incentivized pathway to mitigate climate change. This study focuses on controlled electrochemical conditions in molten lithium carbonate to split CO2 absorbed from the atmosphere into carbon nanotubes (CNTs), and into various macroscopic assemblies of CNTs, which may be useful for nano-filtration. Different CNT morphologies were prepared electrochemically by variation of the anode and cathode composition and architecture, variation of the electrolyte composition pre-electrolysis processing, and variation of the current application and current density. Individual CNT morphologies’ structures and the CNT molten carbonate growth mechanisms are explored using SEM (scanning electron microscopy), TEM (transmission electron micrsocopy), HAADF (high angle annular dark field), EDX (energy dispersive xray), X-ray diffraction), and Raman methods. The principle commercial technology for CNT production had been chemical vapor deposition, which is an order of magnitude more expensive, generally requires metallo-organics, rather than CO2 as reactants, and can be highly energy and CO2 emission intensive (carries a high carbon positive, rather than negative, footprint).

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 score0.003
Threshold uncertainty score0.556

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 head0.004
GPT teacher head0.193
Teacher spread0.188 · 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