Investigation of the decomposition mechanism of MTNP melt-cast explosive at different temperatures and pressures through ReaxFF/lg molecular dynamics simulations.

CONTEXT: Thermal decomposition of 1-methyl-3,4,5-trinitropyrazole (MTNP), a melt-cast explosive, was investigated at different temperatures (2500, 2750, 3000, 3250, and 3500 K) and pressures (3000 K/0.5 GPa, 3000 K/1 GPa) using the ReaxFF/lg force field. The study aimed to analyze the changes in reactant quantities, initial reaction pathways, and final product yields. The results demonstrated that complete decomposition of MTNP molecules occurred within a timeframe of 200 ps, with shorter decomposition times observed as the temperature increased. The high-temperature thermal decomposition of MTNP was found to follow two primary reaction pathways. Reaction 1 involved denitration, while reaction 2 proceeded with nitro group isomerization. DFT calculations indicated that nitro group isomerization was the most favorable reaction. During the initial stages, higher quantities of NO2, NO, and N2 were observed compared to other species. This can be attributed to the relatively higher nitrogen and oxygen content in the MTNP structure. Among the five reaction temperatures, it was observed that the quantities of small molecules followed the order of NO2 > NO > N2 > CO. Moreover, with increasing temperature, the quantities of all four small molecules increased, indicating that higher temperatures promoted the progression of the reactions. However, as the pressure increased, there was a trend of initially increasing and then decreasing to zero for the quantities of NO2 and NO. This suggests that high temperature accelerated the high-temperature thermal decomposition of NO2 and NO, leading to a significant increase in the content of N2. METHODS: A 3 × 5 × 5 supercell model of MTNP was constructed in Materials Studio, consisting of 75 unit cells and 300 MTNP molecules. The model was then subjected to a 20 ps geometric optimization using the Polak-Ribiere version of the conjugate gradient (CG) algorithm in the large-scale atomic/molecular massively parallel simulator (LAMMPS) under the isothermal-isobaric (NPT) ensemble at 1 atm pressure and 300 K temperature. Following the optimization, molecular dynamics simulations were performed on the model at five temperatures (2500, 2750, 3000, 3200, and 3500 K) under 1 atm using the NPT ensemble for a total duration of 1 ns. During the simulations, atomic trajectories, as well as information on atomic and molecular species, were output every 500 steps. Subsequently, a custom script was utilized to analyze the thermal decomposition pathways and products. A time step of 0.1 fs was employed for the calculations, and periodic boundary conditions were applied to eliminate boundary effects.

Molecular dynamics simulation of CL20/DNDAP cocrystal-based PBXs.

CONTEXT: CL-20/DNDAP cocrystal is a promising new type of explosive with exceptional energy density and detonation parameters. However, compared to TATB, FOX-7 and other insensitive explosives, it still has higher sensitivity. In order to decrease the sensitivity of CL20/DNDAP cocrystal explosive, in this article, a CL20/DNDAP cocrystal model was established, and six different types of polymers, including butadiene rubber (BR), ethylene-vinyl acetate copolymer (EVA), polyethylene glycol (PEG), hydroxyl-terminated polybutadiene (HTPB), fluoropolymer (F2603), and polyvinylidene difluoride (PVDF), were added to the three cleaved surfaces of (1 0 0), (0 1 0) and (0 0 1) to obtain polymer-bonded explosives (PBXs). Predict the effects of different polymers on the stability, trigger bond length, mechanical properties, and detonation performance of PBXs. Among the six PBX models, CL-20/DNDAP/PEG model exhibited the highest binding energy and the lowest trigger bond length, indicating that CL-20/DNDAP/PEG model had the best stability, compatibility, and the least sensitivity. Furthermore, although the CL-20/DNDAP/F2603 model demonstrated superior detonation capabilities, it should be noted that this model displayed low levels of compatibility. Overall, CL-20/DNDAP/PEG model exhibited the superior comprehensive properties, thereby demonstrating that PEG is a more suitable binder option for PBXs based on the CL20/DNDAP cocrystal. METHODS: The properties of CL-20/DNDAP cocrystal-based PBXs were predicted by molecular dynamics (MD) method under Materials Studio software. The MD simulation time step was set at 1fs and the total MD simulation time was 2ns. The Isothermal-isobaric (NPT) ensemble was used for the 2ns of MD simulation. The COMPASS force field was used, and the temperature was set at 295K.

Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs.

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004-17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.