Molecular Density Functional Theory Research Articles

Molecular docking, DFT and antiproliferative properties of 4-(3,4-dimethoxyphenyl)-3-(4-methoxyphenyl)-1-phenyl-1H-pyrazolo[3,4-b]pyridine as potent anticancer agent with CDK2 and PIM1 inhibition potency.

Due to the limited effeteness and safety concerns associated with current cancer treatments, there is a pressing need to develop novel therapeutic agents. 4-(3,4-Dimethoxyphenyl)-3-(4-methoxyphenyl)-1-phenyl-1H-pyrazolo[3,4-b]pyridine (3) was synthesized and Initially screened on 59 cancer cell lines showed promising anticancer activity, so, it was chosen for a 5-dose experiment by the NCI/USA. The GI50 values ranged from 1.04 to 8.02 μM on the entire nine panels (57 cell lines), with a GI50 of 2.70 μM for (MG-MID) panel, indicating an encouraging action. To further explore the molecular attributes of compound 3, we optimized its structure using DFT with the B3LYP/6-31 + + G(d,p) basis set. We have considered vibrational analysis, bond lengths and angles, FMOs, and MEP for the structure. Additionally, pharmacokinetic assessments were conducted using various in-silico platforms to evaluate the compound safety. A molecular modeling study created a kinase profile on 44 different kinases. This allowed us to study our compound's binding affinity to these kinases and compare it to the co-crystallized one. Our findings revealed compound 3 exhibited better binding for half of the tested kinases, suggesting its potential as a multi-kinase inhibitor. To further validate our computational results, we tested compound 3 for its inhibitory effects on CDK2 and PIM1. Compound 3 exhibited an IC50 of 0.30 µM for CDK2 inhibition, making it five times less active than Roscovitine, which has an IC50 of 0.06 µM. However, compound 3 demonstrated slightly better inhibition of PIM1 compared to Staurosporine. These findings suggest that compound 3 is a promising anticancer agent with the potential for further development into a highly active compound.

Fast room-temperature and water-insensitive self-healing polyurethane for superior composite anti-corrosion coating on Mg alloys

Self-healing polyurethane is widely applied as the anti-corrosion coating to elongate the durability of substrate. However, it remains great challenge to achieve the rapid self-healing performance at room temperature for polyurethane. And it is even more difficult to accomplish the healing under the water. Herein, a novel self-healing polyurethane, PDMS-SS-Py, is prepared to achieve self-healing within 2 h at room temperature. Even under the water, the 97.1 % self-healing efficiency is kept. Molecular dynamics (MD) simulation and density function theory (DFT) demonstrate that hydrogen bonds are pioneers in the healing process of PDMS-SS-Py and elucidate the critical items to achieve the underwater self-healing. On the basis of PDMS-SS-Py, the composite coating, MAO/8-HQ/P, is fabricated on Mg alloy along with microarc oxidation (MAO) and inhibitor 8-hydroxyquinoline (8-HQ) inserted in the micropores of MAO. The self-healing performance of composite coating is almost the same as that of isolate PDMS-SS-Py. As compared with bare Mg alloy, the impedance modulus at the low frequency (|Z|0.01Hz) is increased by 8 orders of magnitude after covering MAO/8-HQ/P coating. Even after 12 days of immersion in 3.5 wt% NaCl solution, the MAO/8-HQ/P composite coating still maintains superior anti-corrosion performance. The excellent anti-corrosion performance is contributed by the self-healing property of PDMS-SS-Py layer, the active protection of 8-HQ, and the basic protection of MAO. This work provides a general strategy for constructing durable self-healing anti-corrosion coating on Mg alloy and deepens the mechanism of polyurethane repairing processes.

Photochemical and Collision-Induced Cross-Linking in Stereochemically Distinct Scaffolds of Peptides and Nitrile Imines in Gas-Phase Ions.

Intramolecular cross-linking between peptides and nitrile-imine intermediates was studied in stereochemically distinct conjugates in which the reacting components were mounted on cis-1,2-cyclohexane and trans-1,4-cyclohexane scaffolds that we call 1,2-s-peptides and 1,4-s-peptides, respectively. The nitrile-imine intermediates were generated by N2 loss from 2,5-diaryltetrazole tags upon UV-photodissociation at 213 and 250 nm or by collision-induced dissociation, and further interrogated by CID and UVPD-MS3. Peptide fragment ion series originating from linear structures and macrocyclic cross-links were distinguished and used to quantify the cross-linking yields. The yields in MS2 varied between 27% for AAAG conjugates to 78% for GAAAK conjugates, depending on the peptide sequence. The CID-MS3 yields were in a 57-97% range, depending on the peptide sequence. Structures of 1,2-s-peptide and 1,4-s-peptide ions as well as several of their nitrile-imine intermediates and cross-links were investigated by high-resolution cyclic ion mobility in combination with Born-Oppenheimer molecular dynamics and density functional theory calculations. Matches between the experimental and calculated collision cross sections and ion relative Gibbs energies were used to assign peptide structures. Peptide conjugates C-terminated with Gly and Lys residues underwent cross-linking by the carboxyl group, as established by MS3 sequencing and corroborated by carboxyl blocking experiments that lowered the cross-linking yields. Peptide conjugates C-terminated with Arg also cross-linked via the side-chain guanidine group. A notable feature of the 1,4-s-peptide ions was the participation of low-energy twist-boat cyclohexane conformers that was enforced by strong hydrogen bonds between the peptide and nitrile imine.

Exploring the anticancer potential of Lasia spinosa rhizomes: insights from molecular docking and DFT investigations on chlorogenic acid and beyond

Lasia spinosa (L.) Thwaites rhizomes (LSR), historically utilised in traditional medicine for various health sufferings, including cancer, represent an intriguing yet underexplored reservoir of bioactive constituents. Our study focused on exploring the anticancer potential of LSR and its phytoconstituents. The methanol extract of LSR exhibited significant cytotoxicity against various cancer cell lines compared to other extracts. The fractionation of methanol extract resulted in the isolation of chlorogenic acid (CA), oleanolic acid, β-amyrin, and lyonoresinol. Molecular docking analysis of these isolated compounds targeted at the active sites of CDK-2, VEGFR-2, and ToP-2A enzymes revealed the superior docking score of CA compared to the other constituents. Additionally, density functional theory studies indicated the strong electrophilic nature of CA and its potential for robust enzyme binding interactions. Subsequent MTT assays focusing on CA exhibited significant activity against all tested cell lines, with IC50 values ranging from 21.56 to 72.60 μg/ml, comparable to quercetin.

Designing the Next Generation of Polymers with Machine Learning and Physics-Based Models

Abstract The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset (<200 unique polymers), we developed quantitative structure-property relationships (QSPR) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ~10,000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ~10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

High-Energy LiNiO2 Li Metal Batteries Enabled by Hybrid Electrolyte Consisting of Ionic Liquid and Weakly Solvating Fluorinated Ether.

In pursuit of the highest possible energy density, researchers shift their focus to the ultimate anode material, lithium metal (Li0), and high-capacity cathode materials with high nickel content (Ni > 80%). The combination of these aggressive electrodes presents unprecedented challenges to the electrolyte. Here, wereport a hybrid electrolyte consisting of a highly fluorinated ionic liquid and a weakly solvating fluorinated ether, whose hybridization structure enables the reversible operation of a battery chemistry based on Li0 and LiNiO2 (Ni = 100%), delivering nearly theoretical capacity of the latter (up to 249mAhg-1) for >300cycles with retention of 78.6% and in absence of unwanted morphological changes in both electrodes. Extensive characterization assisted by molecular dynamic simulation and density functional theory calculations reveals the function of the fluorinated ether to be far more profound than simple dilution and viscosity reduction. Instead, it induces drastic changes in Li+-solvation environment, the consequence of which engenders simultaneous stabilization of electrode/electrolyte and interfacing via formation of respective interfacial chemistries. This study further unlocks fundamental knowledge underneath the prevailing "diluent strategy" that is extensively applied by the electrolyte researchers and opens more design space for the next-generation electrolytes and interphases for these coveted battery chemistries.

Computational Multiscale Study of the Interaction Between the PDMS Polymer and Sunscreen-Related Pollutant Molecules

Sunscreen molecules play a critical role in protecting skin from ultraviolet radiation, yet their efficient detection and separation pose challenges in environmental and analytical contexts. In this work, we employ a multilevel modeling approach to investigate the molecular interactions between representative sunscreen molecules and the polydimethylsiloxane (PDMS) polymer, a material widely recognized for its sorbent properties. Our goal is to explore how these interactions can be fine-tuned to facilitate the effective separation of sunscreen molecules in portable membrane inlet mass spectrometry (MIMS) systems, potentially leading to the development of new membrane materials. Using a combination of advanced computational techniques—force field molecular dynamics simulations, semiempirical GFN2-xTB, and density functional theory calculations—we assess the interaction strength and noncovalent interactions of sunscreen molecules, namely oxybenzone, naphthalene, benzo[a]anthracene, avobenzone, and 1,3,5-trichlorobenzene, with PDMS. Additionally, the effect of temperature on the interaction dynamics is evaluated, with the aim of extending the sorbent capacities of PDMS beyond light polar molecules to larger, polar sunscreen compounds. This study provides critical insights into the molecular-level interactions that may guide the design of novel membrane materials for efficient molecular separation.

On-demand imidazolidinyl urea-based tissue-like, self-healable, and antibacterial hydrogels for infectious wound care

Bacterial wound infections are a growing challenge in healthcare, posing severe risks like systemic infection, organ failure, and sepsis, with projections predicting over 10 million deaths annually by 2050. Antibacterial hydrogels, with adaptable extracellular matrix-like features, are emerging as promising solutions for treating infectious wounds. However, the antibacterial properties of most of these hydrogels are largely attributed to extrinsic agents, and their mechanisms of action remain poorly understood. Herein we introduce for the first time, modified imidazolidinyl urea (IU) as the polymeric backbone for developing tissue-like antibacterial hydrogels. As-designed hydrogels behave tissue-like mechanical features, outstanding antifreeze behavior, and rapid self-healing capabilities. Molecular dynamics (MD) simulation and density functional theory (DFT) calculation were employed to well-understand the extent of H-bonding and metal-ligand coordination to finetune hydrogels’ properties. In vitro studies suggest good biocompatibility of hydrogels against mouse fibroblasts & human skin, lung, and red blood cells, with potential wound healing capacity. Additionally, the hydrogels exhibit good 3D printability and remarkable antibacterial activity, attributed to concentration dependent ROS generation, oxidative stress induction, and subsequent disruption of bacterial membrane. On top of that, in vitro biofilm studies confirmed that developed hydrogels are effective in preventing biofilm formation. Therefore, these tissue-mimetic hydrogels present a promising and effective platform for accelerating wound healing while simultaneously controlling bacterial infections, offering hope for the future of wound care.

Elucidation of the Decomplexation and Reverse Migration Mechanism of Uranyl Ions from the Oil Phase to the Aqueous Phase Using Microsecond (0.2 μs) Molecular Dynamics Simulations.

Interphase ion transfer is ubiquitous in chemistry, physics, biology, and various engineering sciences. Ion transfer from the aqueous phase to the oil phase or vice versa is a complex chemical phenomenon, and its fundamental understanding is crucial for efficient and economical mass transfer. This ion transfer is much more complex for radionuclide metal ions. Therefore, an attempt has been made to elucidate the decomplexation and mechanism of reverse migration of uranyl ions from the loaded oil phase to the aqueous phase using large time-scale molecular dynamics simulations (microseconds) and density functional theory (DFT). The strength of the metal-ligand complex is the key factor for stripping among other mass transfer parameters. Stronger the metal-ligand complex, the lower the tendency for reverse migration into the aqueous phase, which was demonstrated by three different ligands for reverse migration using water as the stripping agent. The interaction energy of the metal-ligand complex has been calculated using DFT. The reverse migration is validated by the distance between the centers of mass. Higher interface thickness and lower interfacial tension belong to N,N,N',N'-tetra-octyldiglycolamide (TODGA), which are favorable for mass transfer across the liquid-liquid interface. The computed distribution coefficient (KD) is favorable for tributyl phosphate (TBP) and tri-iso-amyl phosphate (TiAP), whereas TODGA shows a higher KD, indicating a less favorable value for reverse migration. The distance between the uranyl ion and the TODGA molecule confirms that the entrapment of uranyl ions in the TODGA phase might be attributed to aggregation. Further, the aggregation tendency of TODGA molecules reduces the back extraction and the recovery of uranyl ions into the aqueous phase. The present MD results might be important for predicting an efficient back-extracting agent for the recovery of the metal ions from the oil phase.

A versatile inhibitory approach and molecular mechanism on cancer cells and cholinesterase enzymes: Synthesis, DFT, ADMET and molecular docking of thiadiazole and oxadiazole derivatives

This study presents a multi-routed research design featuring thiadiazole/ oxadiazole based Schiff base derivatives as potential inhibitors, targeting cancer and Alzheimer. Among thiadiazole based Schiff base derivatives, the excellent inhibition was exhibited by compound 1 (IC50 = 20.10 ± 1.1, 22.21 ± 1.03 and 26.23 ± 2.2 µM for HepG2 (liver cancer), MCF-7 (breast cancer) and W138 (lung cancer), respectively) in comparison to the standard Doxorubicin. Oxadiazole based compound 9 was also found with spellbinding efficacy (IC50 =15.20 ± 2.5, 19.13 ± 2.2 and 12.33 ± 3.4 µM for HepG2, MCF-7 and W138, respectively). Both compounds 1(IC50=3.10 ± 0.20 and 3.70 ± 0.10 µM against AChE and BuChE) and 9 (IC50=2.20 ± 0.10 and 2.80 ± 0.50 µM against AChE and BuChE) were also found as excellent anti-Alzheimer agents in comparison to reference donepezil. The outcomes of in vitro biological activity were further studied in silico via molecular docking, DFT and ADMET to elucidate the binding interactions, stability, reactivity and pharmacokinetic profiles of potent compounds.

Computational Drug Design Approaches for the Identification of Novel Antidiabetic Compounds from Natural Resources through Molecular Docking, ADMET, and Toxicological Studies.

Type 2 diabetes mellitus (T2DM) is usually depicted by relative insulin deficiency, raised blood glucose levels, and the predominant risk factor, insulin resistance. Hence, the development of insulin sensitizer drugs targeting PPAR-γ receptors has expanded enormous interest as an attractive choice for T2DM treatment. Thiazolidinediones (TZD) enhance insulin sensitivity either by directly functioning on gene transcription of the PPARγ receptor related to glucose homeostasis or by systemic sensitization of insulin and, therefore, improved hyperglycemia in a wide range of patients. However, severe complications and adverse effects of TZDs necessitate the development of an efficacious and reliable insulin sensitizer from alternative resources. On the contrary, Nature is a rich source of anticipated effective and safer medicine; more than fifty percent of drugs on the market are developed from natural products. Hence, searching for a new PPAR-γ agonist from bioactive secondary compounds of medicinal plants along with greater efficacy and safety is a recognized and consistent tactic for developing novel antidiabetic agents. Pulicaria jaubertii is a fragrant perennial aromatic plant with anti-inflammatory, antidiabetic, antimicrobial, antimalarial, and insecticidal properties. The current study was designed to use a computer-aided drug design to explore the best antidiabetic compounds from P. jaubertii. Herein, the molecular docking study of 80 investigated ligands against the PPAR-γ receptor identifies the highest docking score for five ligands ranging from -8.9 kcal/mol to 8.0 kcal/mol, which is also more significant than the standard drug pioglitazone (-7.7 kcal/mol) determined by the PyRx 8.0 virtual screening software. GLN286, CYS285, SER289, TYR473, MET364, ARG288, ILE341, and LEU333 residues are found to be significant contributors to the non-bonded interaction between ligands and receptors. Molecular electrostatic potential (MEP), DFT, molecular orbital (MO), ADMET, and toxicological analyses were performed on the selected five high-scored ligands of P. jaubertii. Results documented that all investigated ligands, especially L4, show considerably excellent profiles in molecular docking, MEP, DFT, MO, ADMET, and toxicological predictions, suggesting our drug-designing approaches may contribute to the development of a novel antidiabetic drug for the treatment of T2DM from natural resources.

Computational exploration of acefylline derivatives as MAO-B inhibitors for Parkinson's disease: insights from molecular docking, DFT, ADMET, and molecular dynamics approaches.

Monoamine oxidase B (MAO-B) plays a pivotal role in the deamination process of monoamines, encompassing crucial neurotransmitters like dopamine and norepinephrine. The heightened interest in MAO-B inhibitors emerged after the revelation that this enzyme could potentially catalyze the formation of neurotoxic compounds from endogenous and exogenous sources. Computational screening methodologies serve as valuable tools in the quest for novel inhibitors, enhancing the efficiency of this pursuit. In this study, 43 acefylline derivatives were docked against the MAO-B enzyme for their chemotherapeutic potential and binding affinities that yielded GOLD fitness scores ranging from 33.21 to 75.22. Among them, five acefylline derivatives, namely, MAO-B14, MAO-B15, MAO-B16, MAO-B20, and MAO-B21, displayed binding affinities comparable to the both standards istradefylline and safinamide. These derivatives exhibited hydrogen-bonding interactions with key amino acids Phe167 and Ile197/198, suggesting their strong potential as MAO-B inhibitors. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the examined acefylline derivatives over time. The simulations demonstrated that among the examined acefylline derivatives and standards, MAO-B21 stands out as the most stable candidate. Density functional theory (DFT) studies were also performed to optimize the geometries of the ligands, and molecular docking was conducted to predict the orientations of the ligands within the binding cavity of the protein and evaluate their molecular interactions. These results were also validated by simulation-based binding free energies via the molecular mechanics energies combined with generalized Born and surface area solvation (MM-GBSA) method. However, it is necessary to conduct in vitro and in vivo experiments to confirm and validate these findings in future studies.

Synthesis, antiproliferative activity, and in silico studies of quinoline-based pyrimidinedione and thiazolidinedione derivatives

Cancer affects millions of people worldwide. PDK1 enzyme (co-crystallized with BIM-1) controls the proliferation of breast cancer cells. Aiming to resemble BIM-1’s binding, quinoline-based pyrimidinediones and thiazolidinediones were synthesized starting from 2-chloro-3-formylquinoline. Compared with doxorubicin (reference), in vitro antiproliferative activity against MCF7 and HCT116 cancer cell lines showed the most potency of thiobarbiturate 3 and thiazolidinedione 4. In silico molecular docking, DFT, and pharmacokinetics simulations supported the findings. The docking analysis toward PDK1 enzyme showed that most amino acids interacting with co-crystallized ligand (BIM-1) were successfully bonded to our docked substances, especially thiobarbiturate 3 with highest S-score closer to BIM-1. In DFT calculations, this compound exhibited the lowest energy gap and highest softness leading to more response to radical surface interactions. The compounds with significant antiproliferative activity exhibited high electrophilicity values. ADME analysis showed its desirable drug-likeness and oral bioavailability. This work may contribute to developing new potent antiproliferative agents.

Atomistic simulations of polysaccharide materials for insights into their crystal structure, nanostructure, and dissolution mechanism

AbstractCrystalline polysaccharides are abundant in nature and can be transformed into highly functional materials. However, the molecular basis for the formation of higher-order structures remains unclear. Computer simulation is an advanced tool for modeling macromolecular structures, and the atomistic simulations provide valuable information on the crystalline polysaccharides. Fiber deformation, crystalline transition, and novel nanostructures of cellulose were characterized through molecular dynamics simulations and density functional theory calculations of models of molecular chain sheets extracted from the crystal structure of the cellulose polymorphs. Extended ensemble molecular dynamics simulations were applied to analyze the artificial crystal structure of non-natural amylose analog polysaccharides, revealing the hexagonal packing of double helices through the self-assembly of molecular chains dispersed in aqueous solution. Dissolution simulations of the cellulose and chitin crystalline fibers revealed that the anions of ionic liquids, with their solvation power, played a key role in the cleavage of intermolecular hydrogen bonds in the crystal structure, whereas the cations contributed to irreversible molecular chain dispersion. The good correlation between the actual solubility of polysaccharides and the predicted number of intermolecular hydrogen bonds prompted the development of a platform that combined simulations and machine learning for high-throughput screening of solvents for cellulose and chitin.

CO oxidation on single Pt atom supported by two-dimensional ZnO monolayer: Reaction mechanism and precursor activation

CO oxidation is an effective solution for controlling CO emissions, and supported single-atom catalysts have shown excellent catalytic reactivity for chemical reactions. CO oxidation on a single Pt atom supported by a two-dimensional ZnO monolayer is investigated by combing ab initio molecular dynamics and density functional theory calculations. Seven reaction mechanisms are identified, including two new trimolecular Elie-Riedel mechanisms; six of them, except for the Mars-van Krevelen mechanism, have rate-limiting step energy barriers between 0.31 and 0.47 eV, which is lower than the experimental activation barrier of 0.81 eV on single Pt atoms supported by bulk ZnO, and is much lower than that of about 1.0 eV on Pt(111) surface. Furthermore, two preferred reaction mechanisms are discovered, an Eley-Rideal mechanism and a trimolecular Eley-Rideal mechanism, both of which have rate-limiting step energy barriers as low as 0.31 eV. Our results show that the more antibonding orbitals are filled in the reaction precursor, the higher its activation level. Activation of precursors plays an important role in CO oxidation and leads to different mechanisms. These results provide insights into the design of efficient single-atom catalysts supported by two-dimensional materials for the removal of CO pollutants.

Harnessing high potential benzothiazole chalcones against dengue virus NS5 protein: A multi-faceted theoretical study through molecular docking, ADME, and DFT

Chalcones bearing tetralone, indanone and benzothiazole cores were synthesized successfully using a general Claisen-Schmidt condensation protocol. The prepared compounds were purified and structurally analyzed by 1H, 13C NMR, and FT-IR techniques. A multi-faceted theoretical approach, combining Density Functional Theory (DFT), molecular docking, and ADME predictions, was employed to evaluate their therapeutic potential. DFT calculations at the B3LYP/def2-TZVP level revealed key electronic properties, with TD3 compound demonstrating the highest chemical reactivity. Molecular Electrostatic Potential (MEP) and Reduced Density Gradient (RDG) analyses provided insights into the compounds' non-covalent interactions and charge distributions. Molecular docking studies against the NS5 protein (PDB: 6KR2) showed superior binding affinities for all three compounds compared to the control ligand SAH, with TD3 exhibiting the lowest binding energy (−8.41 kcal/mol) and theoretical inhibition constant (689.31 nM). ADME predictions indicated favorable drug-like properties with concerns regarding aqueous solubility and potential P-glycoprotein interactions. Toxicity evaluations highlighted challenges, particularly in hepatotoxicity and carcinogenicity. The study identified TD3 as a promising lead compound for Dengue Virus NS5 inhibition, while also emphasizing the need for targeted modifications to address toxicity concerns. This research not only contributes to anti-dengue drug discovery efforts but also provides a robust methodological framework for the theoretical evaluation of similar small compounds in future investigations.

Exploring the Potential of Beta-Cyclodextrin-Based MIL-101(Cr) for Pharmaceutical Removal from Wastewater: A Combined Density Functional Theory and Molecular Simulations Study.

Pharmaceutical contaminants pose significant risks to ecosystems and human health, necessitating effective removal strategies. This research focuses on developing advanced adsorbents for removing pharmaceutical pollutants from the environment. Metal-organic frameworks (MOFs), specifically MIL-101(Cr) functionalized with biodegradable beta-cyclodextrin (β-CDex), were investigated as potential nanocomposite adsorbents for the removal of ketorolac (KTRK), naproxen (NPXN), and tramadol (TRML). The study employed molecular simulations and density functional theory (DFT) calculations to explore the interactions between the pollutants and adsorbents. Analyses of DFT results, including electrostatic potential, ionization energy, density of states, and molecular orbital analysis, provided insights into the reactivity of pollutants and adsorbents. Additionally, the structural properties of the adsorbents, such as fractional free volume, radius of gyration, and system energies, were thoroughly examined. Molecular dynamics (MD) and Monte Carlo (MC) simulations were used to evaluate the adsorption capacities of MIL-101(Cr) for the target pharmaceutical pollutants. The results demonstrated the superior adsorption performance of the nanocomposite adsorbent, particularly for KTRK, with an adsorption energy of -1934 kcal/mol, compared to the pristine MIL-101(Cr), which had an adsorption energy of -1916 kcal/mol. This enhanced adsorption is attributed to the optimal molecular fit, guest-host solid interactions, and the selective encapsulation capabilities of β-CDex. This research highlights the potential of MOF-based nanocomposites as effective and sustainable solutions for pharmaceutical pollution. By advancing the understanding of molecular interactions through simulations, this study contributes to developing innovative adsorbents for wastewater treatment and the protection of water resources.

Speed of sound for understanding metals in extreme environments

Knowing material behavior is crucial for successful design, especially given the growing number of next-generation energy, defense, and manufacturing systems operating in extreme environments. Specific applications for materials in extreme environments include fusion energy, semiconductor manufacturing, metal additive manufacturing, and aerospace. With increased applications, awareness of foundational science for materials in extreme environments is imperative. The speed of sound provides insights into phase boundaries, like shock-induced melting. Thermodynamic integration of the speed of sound enables the deduction of other desirable properties that are difficult to measure accurately, like density, heat capacity, and expansivity. Metrology advancements enable the speed of sound to be measured at extreme conditions up to 15 000 K and 600 GPa. This comprehensive review presents state-of-the-art sound speed metrology while contextualizing it through a historical lens. Detailed discussions on new standards and metrology best practices, including uncertainty reporting, are included. Data availability for condensed matter speed of sound is presented, highlighting significant gaps in the literature. A theoretical section covers empirically based theoretical models like equations of state and CALPHAD models, the growing practice of using molecular dynamics and density functional theory simulations to fill gaps in measured data, and the use of artificial intelligence and machine learning prediction tools. Concluding, we review how a lack of measurement methods leads to gaps in data availability, which leads to data-driven theoretical models having higher uncertainty, thus limiting confidence in optimizing designs via numerical simulation for critical emerging technologies in extreme environments.

Antioxidant and anti-urease activities of Cardamine bulbifera: Insights from molecular docking and density functional theory studies

IntroductionCardamine bulbifera (L.) Crantz, commonly known as “Asian bittercress”, is a plant species widely distributed across Asia. It belongs to the Brassicaceae and has been traditionally used in medicinal applications. This study investigates the phytochemical composition, phenolic content, antioxidant properties, and anti-urease activity of C. bulbifera. MethodsIn this study, the aerial parts of the C. bulbifera were collected. The total polyphenol content of the extracts was determined using spectrophotometric methods, and its antioxidant and anti-urease activities were assessed. The chemical composition was characterised using gas chromatography-mass spectroscopic (GC-MS) analysis. Additionally, molecular docking studies explored potential interactions between the identified compounds and Helicobacter pylori CagA oncoprotein. Furthermore, Density Functional Theory (DFT) analysis provided valuable insights into the electronic properties of the main compounds. ResultThe total phenolic content of C. bulbifera extract was 225 ± 0.02 mg GAE/g extract DW, and the total flavonoid content was 62.64 ± 3.27 mg QE/g extract DW. The 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) assay revealed an IC50 value of 1.01 ± 0.04 mg/mL, indicating significant antioxidant activity. The IC50 value for anti-urease activity was determined to be 2.74 ± 0.03 µg/mL, suggesting potent inhibition of urease enzyme activity. GC-MS analysis identified 15 bioactive compounds in the extract. Molecular docking studies highlighted Phenol, 3,5-bis(1,1-dimethylethyl) and 1-dodecanol as compounds with the highest binding affinity for the H. pylori CagA oncoprotein, suggesting potential therapeutic implications against H. pylori-related cancers. Additionally, DFT analysis emphasised these compounds' electronic properties and chemical reactivities, indicating their potential role in future pharmaceutical developments. ConclusionC. bulbifera exhibits rich phenolic and flavonoid content and significant antioxidant and intense urease inhibition activities. These findings suggest that C. bulbifera may offer potential therapeutic options for conditions related to H. pylori infections, including cancer. Further research is needed to explore its mechanisms of action and clinical applications in more detail.

Atomistic insights into p-nitrotoluene combustion via the ReaxFF molecular dynamics and density functional theory study

p-Nitrotoluene is a toxic, flammable, and explosive nitro-hazardous chemical in process industries. However, the combustion reaction mechanism is still unclear, and kinetic data on chemical reactions is lacking. This work investigated the combustion reaction pathways of p-nitrotoluene through ReaxFF molecular dynamics simulations and calculated the reaction rate constants at the M062X/6-31+g**//CCSD(T)/cc-PVTZ theoretical level. A combustion reaction kinetic model for p-nitrotoluene was proposed, which included 367 species and 2509 reactions, thereby exploring the initial pathways and mechanisms of the p-nitrotoluene combustion reaction. Reaction force field molecular dynamics simulations and high-precision quantum chemical density functional theory calculations showed that the key intermediate products of p-nitrotoluene combustion are mainly C6H5CH3, C6H6, and C6H4CH3OH. Hydrogen extraction from CH3· was the main hydrogen extraction pathway initiating the chain reaction, while NO2· tended to depart from the benzene ring and participate in hydrogen abstraction reactions. Through sensitivity analysis of these three key intermediate products, the initial reaction pathways of combustion were elucidated. This research provides theoretical guidance for the process safety and risk engineering of nitro-hazardous chemicals.