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Record W3041664761 · doi:10.1139/cjc-2020-0191

Two-dimensional titanium carbide (Ti<sub>3</sub>C<sub>2</sub>) MXene towards enhancing thermal catalysis decomposition of dihydroxylammonium 5,5′-bistetrazole-1,1′-diolate (TKX-50)

2020· article· en· W3041664761 on OpenAlexvenueno aff
Shuaida Zhu, Yuqi Feng, Xiaoxia Li, Zhaobian Xie, Haixia Zhao, Shuangqi Hu, Xiong Cao

Bibliographic record

VenueCanadian Journal of Chemistry · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsThermal decompositionCatalysisDecompositionChemistryDifferential scanning calorimetryActivation energyTransmission electron microscopyTitaniumScanning electron microscopeCarbideChemical engineeringPhysical chemistryCrystallographyMaterials scienceNanotechnologyThermodynamicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

In this study, we demonstrated that two-dimensional (2D) MXene materials (Ti 3 C 2 ) were creatively introduced into the thermal catalysis fields of high energy density salts dihydroxylammonium 5,5′-bistetrazole-1,1′-diolate (TKX-50) and Ti 3 C 2 MXene materials play a significant catalytic role in the thermal decomposition of TKX-50. Scanning electron microscopy, X-ray diffraction, and transmission electron microscopy were used to characterize the morphology and structure of the Ti 3 C 2 MXene nanosheets. Differential scanning calorimetry was used to evaluate the thermal decomposition properties of pure TKX-50 with and without 2D Ti 3 C 2 added. The results showed that with adding 5 wt% MXene, the peak temperature of TKX-50 was reduced from 250.5 to 233.3 °C, which is a reduction of 17.2 °C. The reaction heat release increased from 2197 to 2907 J g −1 , which is an increase of 710 J g −1 . The E a was decreased by 44.8 kJ mol −1 , from 220.0 to 175.2 kJ mol −1 . Moreover, a synergistic catalytic mechanism for the thermal decomposition of TKX-50 was proposed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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

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

Citations13
Published2020
Admission routes1
Has abstractyes

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