Exploring the Conformational Equilibrium of Mefenamic Acid Released from Silica Aerogels via NMR Analysis

Khodov, Ilya A.; Sobornova, V.V.; Mulloyarova, Valeriia V.; Belov, Konstantin V.; Dyshin, A. A.; Carvalho, Luís A. E. Batista de; Tolstoy, Peter M.; Киселев, М. Г.

doi:10.3390/ijms24086882

Cited by 9 publications

(4 citation statements)

References 95 publications

(120 reference statements)

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“…Solid-state 29 Si NMR spectra of the MK/SiO 2 aerogels (Figure c) exhibit typical characteristic peaks of SiO 2 , including Q 3 ((SiO) 3 SiOH) at −102 ppm and Q 4 ((SiO) 4 ) at −112 ppm, and these units are formed by the hydroxyl condensation of TEOS. Meanwhile, the strong peaks located at around −66 and −64 ppm correspond to T 3 (R(SiO 3/2 )) n , and T 2 (CH 3 (SiO) 2 OR), , respectively, similar to the pure MK aerogel, which are mainly formed by the reaction of hydroxyl groups between APTES and MK. The small peaks at ∼−30 ppm correspond to D 2 (CH 3 CH 3 (SiO) 2 ) species in the structure of MK resin itself, and the absence of the peak of D 2 species in the MK/SiO 2 aerogel may be caused by less D 2 units in MK/SiO 2 -0.33 or strong background signal.…”

Section: Resultsmentioning

confidence: 98%

Double-Network MK Resin-Modified Silica Aerogels for High-Temperature Thermal Insulation

Xu,

Zhu,

Chen

et al. 2023

ACS Appl. Mater. Interfaces

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Polymer-reinforced SiO 2 aerogel materials exhibit excellent thermal insulation, flame resistance, and mechanical properties; however, the poor thermal stability of organic components limits their application in high-temperature environments. Herein, a double-network MK/SiO 2 aerogel was synthesized by direct copolymerization of a methyl-containing silicone resin (MK) and tetraethoxysilane (TEOS) under the cross-coupling of (3-aminopropyl) triethoxysilane (APTES) followed by an atmospheric drying method. The resulting MK/SiO 2 aerogel, presenting a double-cross-linked MK and SiO 2 network, shows a low density of 0.18 g/cm 3 , a high specific surface area of 716.6 m 2 /g, and a low thermal conductivity of 0.030 W/(m K). Especifically, the compressive strength of the MK/SiO 2 aerogel (up to 3.24 MPa) is an order of magnitude higher than that of the pristine SiO 2 aerogel (0.39 MPa) due to the introduction of the strong MK network and enhanced neck connections of SiO 2 nanoparticles. Furthermore, the mutually supportive network endows the MK/SiO 2 aerogels with significant resistance to ablation and oxidation up to 1000 °C, showing a high residual rate (89%), a high specific surface area (235.2 m 2 /g), and structural stability after thermal treatment under air atmosphere. These superior mechanical and thermal properties of the MK/SiO 2 aerogels lead to attractive practical applications in energy transportation, thermal insulation, or aviation.

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Section: Resultsmentioning

confidence: 98%

Double-Network MK Resin-Modified Silica Aerogels for High-Temperature Thermal Insulation

Xu,

Zhu,

Chen

et al. 2023

ACS Appl. Mater. Interfaces

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“…With more than one pyrimidylamino group bound per Si atom, these compounds may form highly linked networks through hydrogen bonds. In this regard, the compound Si(NHPyr) 4 (compound 4) may become interesting for high-temperature applications as the melting point is higher than 300 • C. Moreover, functional groups with hydrogen bridge donor or acceptor qualities may be adsorbed in silica aerogels (e.g., the sorption of mefenamic acid [55]), a feature which is of importance for drug delivery investigations. Vice versa, functional groups with hydrogen bridge donor or acceptor qualities may alter the sorbent properties of porous materials.…”

Section: Discussionmentioning

confidence: 99%

Molecular Structures and Intermolecular Hydrogen Bonding of Silylated 2-Aminopyrimidines

2023

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A series of silylated 2-aminopyrimidines Me(4−n)Si(NHpyr)n (Me = methyl, NHpyr = pyrimid-2-ylamino, n = 1, 2, 3, 4), i.e., compounds 1, 2, 3, and 4, respectively, was prepared from a series of the respective chlorosilanes Me(4−n)SiCln and 2-aminopyrimidine. Triethylamine was used as a sacrificial base. Compounds 1, 2, 3, and 4 are solid at room temperature. They were analyzed using 1H, 13C, 29Si NMR, and Raman spectroscopy, and their molecular structures were confirmed by single-crystal X-ray diffraction analyses. All structures exhibit intramolecular van der Waals contacts between the silicon atom and one nitrogen atom of the pyrimidine moiety. Thus, their Si coordination spheres can be interpreted as [4+n] coordinated capped tetrahedra. Intermolecular hydrogen bonds (N–H···N bridges between the Si-bound amino groups and the non-Si-capping pyrimidine N atoms) are a constant contributor to the solid-state structures of these compounds. Furthermore, compounds 2 and 4 exhibit N–H···N bridges which involve 50% of their Si-capping N atoms as hydrogen bridge acceptors. Consequently, 50% of the non-Si-capping pyrimidine N atoms are stabilized by C–H···N contacts. As a result of a particularly dense network of intermolecular hydrogen bridges, the melting point of Si(NHpyr)4 (compound 4) is higher than 300 °C.

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“…Supercritical carbon dioxide (scCO 2 ) has demonstrated significant efficacy in various fields including extraction of essential oil, − seed oil, − nanoparticle formation, − impregnation, − control of polymorphic forms, − polymer synthesis, etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

Makarov,

Kalikin,

Budkov

2024

Ind. Eng. Chem. Res.

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Supercritical carbon dioxide (scCO 2 ) plays an essential role in various technological procedures, making the solubility of drugs in scCO 2 a crucial aspect of the drug formulation process. This study focuses on utilizing theoretical approaches to predict the solubility of drug-like compounds in scCO2 in order to select the optimum parameters for subsequent experimental procedures. Several machine learning models were developed and compared with a previously established theoretical approach based on classical density functional theory (cDFT). The CatBoost model, utilizing alvaDesc descriptors, demonstrated reasonably accurate predictions for the solubility of 187 drugs (AARD = 1.8%). Meanwhile, the CatBoost model, incorporating CDK descriptors and melting points of drugs as input parameters, exhibited satisfactory accuracy (AARD = 14.3%) in extrapolating predictions for new compounds. Comparing the results between the machine learning approach and the cDFT-based one revealed, on average, a higher accuracy and faster prediction speed for the former. However, cDFT demonstrated a more physical behavior of solubility isotherms compared with the machine learning models. This was particularly evident when the ML models struggled to accurately extrapolate solubility values beyond the experimental range of parameters in the supercritical state. Model CatBoost/CDK is freely accessible at http://chem-predictor.isc-ras.ru/ individual/scco/.

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Exploring the Conformational Equilibrium of Mefenamic Acid Released from Silica Aerogels via NMR Analysis

Cited by 9 publications

References 95 publications

Double-Network MK Resin-Modified Silica Aerogels for High-Temperature Thermal Insulation

Double-Network MK Resin-Modified Silica Aerogels for High-Temperature Thermal Insulation

Molecular Structures and Intermolecular Hydrogen Bonding of Silylated 2-Aminopyrimidines

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

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