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Record W4390585168 · doi:10.56042/ijct.v31i1.4596

Graphene Oxide-Enhanced Aerosol Forming Composites: A Study for Fire Extinguishing Applications

2024· article· en· W4390585168 on OpenAlexaboutno aff

Bibliographic record

VenueIndian Journal of Chemical Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
FundersDefence Research and Development Organisation
KeywordsGrapheneOxideCombustionAerosolGraphiteGraphite oxideBurn rate (chemistry)Chemical engineeringChemistryComposite numberComposite materialCatalysisNanotechnologyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Condensed aerosol-based fire extinguishing system (CAFES) has emerged as the most proficient fire extinguishing system since the implementation of Montreal protocol 1987.  Aerosol forming composite (AFC) is the key constituent of CAFES. For the first time, graphene oxide-based AFCs have been prepared and characterized for use in extinguishing fires. Catalytic activity of bulk graphite, graphite oxide & graphene oxide (1, 3 & 5 %, w/w) on combustion characteristics of AFC was examined by incorporating them in the base AFC. Graphene additives were synthesized and characterized using instrumental techniques such as XRD, FTIR, Raman, SEM and TEM. AFCs with catalysts were also assessed for performance using parameters such as combustion efficiency, minimum fire extinguishing concentration (MEC), burn rate, combustion temperatures and activation energies. Maximum reduction in combustion temperature from 455 to 409 oC was observed with 5 % graphene oxide containing AFC. Addition of 1 % graphene oxide to base AFC remarkably augmented the performance of AFC by enhancing the burn rate by 12.89 %. Prepared high burn rate AFC is under further investigations for potential use in cutting-edge aerosol-based firefighting systems.

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 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 score0.141
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.267
Teacher spread0.259 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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