Full-nitro-nitroamino cooperative action: Climbing the energy peak of benzenes with enhanced chemical stability

Science Advances, Год журнала: 2022, Номер 8(12)

Опубликована: Март 23, 2022

More nitro groups accord benzenes with higher energy but lower chemical stability. Hexanitrobenzene (HNB) a fully nitrated structure has stood as the peak of organic explosives since 1966, it is very unstable and even decomposes in moist air. To increase limit strike balance between stability, we propose an interval full-nitro-nitroamino cooperative strategy to present new benzene, 1,3,5-trinitro-2,4,6-trinitroaminobenzene (TNTNB), which was synthesized using acylation-activation-nitration method. TNTNB exhibits high density (d: 1.995 g cm-3 at 173 K, 1.964 298 K) excellent heat detonation (Q: 7179 kJ kg-1), significantly exceed those HNB 6993 kg-1) state-of-the-art explosive CL-20 6534 kg-1); thus, represents for explosives. Compared HNB, also enhanced stability water, acids, bases.

Язык: Английский

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Toward the defect engineering of energetic materials: A review of the effect of crystal defects on the sensitivity

Chemical Engineering Journal, Год журнала: 2021, Номер 429, С. 132310 - 132310

Опубликована: Сен. 9, 2021

Язык: Английский

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Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials

Nature Communications, Год журнала: 2021, Номер 12(1)

Опубликована: Окт. 12, 2021

Abstract Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has a challenging task on experiments. Here we develop graph neural network (GNN) based deep learning framework quickly predict formation of the cocrystal. In order capture main driving force crystallization from 6819 positive 1052 negative samples reported by experiments, feasible GNN is explored integrate important prior knowledge into end-to-end molecular graph. The model strongly validated against seven competitive models three independent test sets involving pharmaceutical cocrystals, π–π cocrystals energetic exhibiting superior performance with accuracy higher than 96%, confirming its robustness generalization. Furthermore, one new cocrystal predicted successfully synthesized, showcasing high potential practice. All data source codes are available at https://github.com/Saoge123/ccgnet for aiding community.

Язык: Английский

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Predicting impact sensitivity of energetic materials: insights from energy transfer of carriers

Acta Materialia, Год журнала: 2022, Номер 236, С. 118137 - 118137

Опубликована: Июль 1, 2022

Язык: Английский

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Redefining the Term of “Cocrystal” and Broadening Its Intention

Crystal Growth & Design, Год журнала: 2019, Номер 19(3), С. 1471 - 1478

Опубликована: Янв. 23, 2019

"Cocrystal" is currently an increasingly popular term in the crystal community, as it has already been verified that cocrystallization to form new crystals can serve a promising strategy and efficient technology modulate improve properties performances of materials fields pharmaceuticals others. Nevertheless, definition intetsion cocrystal still remain debatable. In this Perspective, we redefine with broadened intention "a single-phase crystalline solid composed two or multiple components stoichiometric ratio, be atoms, molecules, anions cations pairs, and/or metallic free electrons shared". Thereby, cocrystals are classified into five types terms kinds their interactions entity, including atomic cocrystal, molecular ionic mixed-type cocrystal. The reasserted present uniform for all solids help avoid confusion numerous existing these solids. some narrow intentions expected updated applied less time goes on.

Язык: Английский

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Packing Structures of CL-20-Based Cocrystals

Crystal Growth & Design, Год журнала: 2018, Номер 18(11), С. 7065 - 7078

Опубликована: Окт. 2, 2018

The CL-20-based cocrystals (CCCs) are now the most active in field of energetic cocrystals, due to an advantage high energy density while a disadvantage low stability CL-20, which may be tuned with desired structures and properties by cocrystallization. This work presents comprehensive insight into packing 27 CCCs observed since 2017. First, it shows multiplicity coformer molecules various shapes sizes. Regarding conformers, β-, γ-, η-, ε-, ζ-forms appear CCCs, total above that CL-20 polymorphs; two forms can exist same CCC highlights difference conformers between single component crystals cocrystals; γ- β-forms govern population 87%. conformational diversity serves as reason for abundance CCCs. Meanwhile, stoichiometric ratios from 1:1 1:6 except 1:5 observed, lower ones predominate populations 48 40% 1:2, respectively. Moreover, exhibits wavelike, sandwich, channel, caged molecular stacking Among these stacking, O···H, O···N, O···O contacts dominate weak intermolecular interactions, feature hydrogen bonding H atoms acyl/ether O molecules, p (of on NO2 CL-20)−π big π-bonds molecules) interactions. interactions contribute small volume variations after cocrystallization, maximum relative error ∼3%. Besides, each mediates those related pure components; no outperforms ε-CL-20 density. Finally, we find contents N facilitate increase coefficients densities. All findings expected enrich knowledge both materials enhance rationalization crystal design.

Язык: Английский

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Fused rings with N-oxide and –NH₂: good combination for high density and low sensitivity energetic materials

Chemical Communications, Год журнала: 2019, Номер 55(61), С. 8979 - 8982

Опубликована: Янв. 1, 2019

Energetic materials with N-oxide and amino groups suggest a promising alternative for the design of high-energy low sensitivity.

Язык: Английский

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A high density and insensitive fused [1,2,3]triazolo–pyrimidine energetic material

Chemical Engineering Journal, Год журнала: 2020, Номер 404, С. 126514 - 126514

Опубликована: Авг. 2, 2020

Язык: Английский

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Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning

Journal of Chemical Information and Modeling, Год журнала: 2021, Номер 61(5), С. 2147 - 2158

Опубликована: Апрель 26, 2021

To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest priori synthesis from chemical structure alone. In this work, we demonstrate that machine learning methods can indeed be used directly learn relationship between structures and crystalline molecules, even in absence any crystal information or quantum mechanical calculations. We focus specifically on class organic compounds categorized as energetic materials called high explosives (HE) predicting their density. An ongoing challenge is deciding how best featurize molecules inputs into models—whether expert handcrafted features learned representations via graph-based neural network models—yield better results why. evaluate both types combination with number models densities HE-like curated Cambridge Structural Database, report performance pros cons our methods. Our message passing (MPNN) based generally perform best, outperforming current state-of-the-art at density performing well when testing data set not representative training data. However, these are traditionally considered black boxes less easily interpretable. address common challenge, also provide comparison analysis MPNN-based model fixed feature provides insights what by MPNN accurately

Язык: Английский

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Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

Journal of Materials Chemistry A, Год журнала: 2021, Номер 9(38), С. 21723 - 21731

Опубликована: Янв. 1, 2021

Potential energetic melt-castable materials were screened out by machine-learning assisted high-throughput virtual screening from a generated chemical space, then eight compounds synthesized and characterized.

Язык: Английский

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