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Record W4415204714 · doi:10.1139/cjc-2025-0059

The structure, binding mechanism, and properties of hollow layered and sandwiched TATB@C <sub>18</sub> and TNT@C <sub>18</sub> composites: a DFT study

2025· article· en· W4415204714 on OpenAlexvenueno aff
Qiong Wu, Zhihao Zheng, Zusheng Hang, Weihua Zhu

Bibliographic record

VenueCanadian Journal of Chemistry · 2025
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
Fundersnot available
KeywordsTATBExplosive materialBinding energyCarbon fibersFullerene

Abstract

fetched live from OpenAlex

In this work, to develop new advanced energetic composites with better safety performance, composites with different proportions and sizes including TATB@C 18 , TNT@C 18 , 2TATB@C 18 , 2TNT@C 18 , TATB@2C 18 , and TNT@2C 18 were designed and constructed by an all carbon compound named cyclo[18]carbon (C 18 ) and two famous and widely used energetic compounds 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) and 2,4,6-trinitrotoluene (TNT). Then, the molecular and electronic structures, binding mechanism, and properties of them were investigated theoretically. The results showed that both TATB and TNT can form stable hollow layered and sandwiched composites with C 18 through the C…O, C…N, and C…C interactions, while the binding strength of former composites is much better than that of later one. When compared to TATB and TNT, the safety performance of composites was much better. On the one hand, their impact sensitivity is lower to that of TATB or TNT slightly. On the other hand, the energy gap of TATB and TNT can be significantly reduced about 30% by C 18 . The safety performance could be flexibly adjusted by changing the proportion of TATB, TNT, or C 18 . In addition, the short recovery time and the big difference in the UV–Vis spectrum between sole TATB or TNT and composites may indicate the high potential to use C 18 as a new sensor for the explosive trace detection.

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 score0.003
Threshold uncertainty score0.565

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 head0.007
GPT teacher head0.179
Teacher spread0.172 · 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
Published2025
Admission routes1
Has abstractyes

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