Energy Estimates Research Articles

Cyclic cooperativity contributions determine the hydrogen bond strengths in molecular clusters.

In a recent communication (A. Shivhare, B. Dehariya, S. R. Gadre and M. M. Deshmukh, Phys. Chem. Chem. Phys., 2024, 26, 21332), we proposed a method for calculating the energy of a hydrogen bond (HB), which is common to two or more cyclic networks of HBs in water (H2O)n clusters. For this purpose, the sum of the cooperativity contributions (CCs) of these cyclic structures, estimated using the molecular tailoring approach (MTA)-based method, was added to the energy of this HB in the respective water dimer isolated from the cluster. The HB energies calculated in this fashion (ESynergeticHB) were in excellent agreement with their actual cluster counterparts (EclusterHB). In this work, we test the generality of this methodology. For this purpose, we employed clusters of ammonia (NH3)n, hydrogen sulphide (H2S)n, mixed (H2S)m(H2O)n, (NH3)m(H2O)n, methanol-water (CH3OH)m(H2O)n, and hydrogen fluoride-water (HF)m(H2O)n exhibiting HBs of variable strength (1 to 19 kcal mol-1). The HB energies in all these molecular clusters calculated using the present method were found to be accurate. The absolute difference between the ESynergeticHB and EclusterHB values in these clusters is found to be less than 0.5 kcal mol-1. Importantly, the present method not only enables accurate HB energy estimation in molecular clusters, but also offers qualitative guidelines for this purpose. The latter are based on the nature of cyclic cooperativity, exhibiting either full cyclic cooperativity (FCC), partial cyclic cooperativity (PCC) or anti-cooperativity (AC).

Thermal properties and non-bonded interactions in human PMP2 variants: A molecular dynamics study

Charcot-Marie-Tooth disease has been known for a long time, affecting population worldwide. Many studies on this disease have been conducted, including clinical as well as computational, but no effective medical cure has yet been found. This disease is caused due to mutation in the human peripheral myelin protein 2 (PMP2) responsible for myelin formation and maintenance. In this research work, we have done a comparative molecular dynamics study on the PMP2 protein and its M114T mutant variant, focusing on non-bonded interactions and thermal properties in a biologically relevant environment. The primary objective was to explore the structural differences between the two proteins at temperatures of 305 K, 310 K, and 315 K. Using the NAMD software for molecular dynamics simulations, various analyses were performed, including RMSD estimation, counting number of hydrogen bond formation, salt bridge occupancy assessment, estimation of vdW and electrostatic interaction energies in the system, and estimation of thermal diffusivity of the proteins as well as specific heat capacity (Cv) of the system. The results suggested slight difference between the structure of the two proteins. In the mutant, a slight increase in RMSD values was observed, suggesting a minor reduction in structural stability compared to the wild-type protein. The number of hydrogen bonds formed was generally higher in the wild-type protein, with slightly more differences observed at 305 K and 315 K than at 310 K. Analysis of salt bridge occupancy revealed notable differences in the formation patterns between the two proteins. The mutation was found to have a greater impact on salt bridge formation. In addition to this, the vdW and electrostatic interaction energies were found to be lower in the system with mutant variant, with both energies decreasing gradually as temperature increased, indicating a more robust interaction profile in the system with wild-type protein. Also, the estimated value of thermal diffusivity indicated that the mutant was slightly more efficient at conducting heat than the wild-type protein; the thermal diffusivity of both proteins was much lower than that of water. Finally, a non-linear (concave nature) change in Cv with temperature was observed in both protein systems. The work was further extended to verify the Maxwell-Boltzmann distribution law which confirmed the proper distribution of kinetic energy among particles, and the temperature fluctuation was found to follow a Gaussian distribution during the NVE simulation, which was as expected.

A Study of the Star Clusters’ Population in the Giant Molecular Cloud G174+2.5

We study the structure, interstellar absorption, color–magnitude diagrams (CMDs), kinematics, and dynamical state of embedded star clusters in the star-forming region associated with the giant molecular cloud G174+2.5. Our investigation is based on photometric data from the UKIDSS Galactic Plane Survey catalog and astrometric data from the Gaia DR3 catalogs. First, we recover all the known embedded clusters and candidate clusters in the region using surface density maps. Then, for the detected clusters, we determine their general parameters: the center positions, radii, number of stars, and reddening. To evaluate the reddening, we use both the NICEST algorithm and the Q-method. Both methods produce consistent extinction maps in the regions of the four studied clusters. However, the Q-method yields a much smaller color scatter in the CMD. For four clusters in particular (S235 North-West, S235 A-B-C, S235 Central, and S235 East1+East2), we were able to compute individual membership probabilities, the cluster distances, the cluster masses, and their average proper motions. By building on these results, we have studied the clusters’ kinematics and dynamics. Moreover, we estimate the mass of the gas component and the star formation efficiency in the regions of these four clusters. Finally, we provide an estimate of the total energy of the stellar and gas components in the area of these four clusters to determine whether the clusters are bound (here, we consider a “cluster” as the system “stars + gas”). The gravitational bound strongly depends on the region for which we estimate the gas mass. If we consider the mass of the entire cloud, all these four clusters turn out to be bound.

Enhancing HDAC Inhibitor Screening: Addressing Zinc Parameterization and Ligand Protonation in Docking Studies.

Precise binding free-energy predictions for ligands targeting metalloproteins, especially zinc-containing histone deacetylase (HDAC) enzymes, require specialized computational approaches due to the unique interactions at metal-binding sites. This study evaluates a docking algorithm optimized for zinc coordination to determine whether it could accurately differentiate between protonated and deprotonated states of hydroxamic acid ligands, a key functional group in HDAC inhibitors (HDACi). By systematically analyzing both protonation states, we sought to identify which state produces docking poses and binding energy estimates most closely aligned with experimental values. The docking algorithm was applied across HDAC 2, 4, and 8, comparing protonated and deprotonated ligand correlations to experimental data. The results demonstrate that the deprotonated state consistently yielded stronger correlations with experimental data, with R2 values for deprotonated ligands outperforming protonated counterparts in all HDAC targets (average R2 = 0.80 compared to the protonated form where R2 = 0.67). These findings emphasize the significance of proper ligand protonation in molecular docking studies of zinc-binding enzymes, particularly HDACs, and suggest that deprotonation enhances predictive accuracy. The study's methodology provides a robust foundation for improved virtual screening protocols to evaluate large ligand libraries efficiently. This approach supports the streamlined discovery of high-affinity, zinc-binding HDACi, advancing therapeutic exploration of metalloprotein targets. A comprehensive, step-by-step tutorial is provided to facilitate a thorough understanding of the methodology and enable reproducibility of the results.

Assessing the Future ODYSEA Satellite Mission for the Estimation of Ocean Surface Currents, Wind Stress, Energy Fluxes, and the Mechanical Coupling Between the Ocean and the Atmosphere

Over the past decade, several studies based on coupled ocean–atmosphere simulations have shown that the oceanic surface current feedback to the atmosphere (CFB) leads to a slow-down of the mean oceanic circulation and, overall, to the so-called eddy killing effect, i.e., a sink of kinetic energy from oceanic eddies to the atmosphere that damps the oceanic mesoscale activity by about 30%, with upscaling effects on large-scale currents. Despite significant improvements in the representation of western boundary currents and mesoscale eddies in numerical models, some discrepancies remain when comparing numerical simulations with satellite observations. These discrepancies include a stronger wind and wind stress response to surface currents and a larger air–sea kinetic energy flux from the ocean to the atmosphere in numerical simulations. However, altimetric gridded products are known to largely underestimate mesoscale activity, and the satellite observations operate at different spatial and temporal resolutions and do not simultaneously measure surface currents and wind stress, leading to large uncertainties in air–sea mechanical energy flux estimates. ODYSEA is a new satellite mission project that aims to simultaneously monitor total surface currents and wind stress with a spatial sampling interval of 5 km and 90% daily global coverage. This study evaluates the potential of ODYSEA to measure surface winds, currents, energy fluxes, and ocean–atmosphere coupling coefficients. To this end, we generated synthetic ODYSEA data from a high-resolution coupled ocean–wave–atmosphere simulation of the Gulf Stream using ODYSIM, the Doppler scatterometer simulator for ODYSEA. Our results indicate that ODYSEA would significantly improve the monitoring of eddy kinetic energy, the kinetic energy cascade, and air–sea kinetic energy flux in the Gulf Stream region. Despite the improvement over the current measurements, the estimates of the coupling coefficients between surface currents and wind stress may still have large uncertainties due to the noise inherent in ODYSEA, and also due to measurement capabilities related to wind stress. This study evidences that halving the measurement noise in surface currents would lead to a more accurate estimation of the surface eddy kinetic energy and wind stress coupling coefficients. Since measurement noise in surface currents strongly depends on the square root of the transmit power of the Doppler scatterometer antenna, noise levels can be reduced by increasing the antenna length. However, exploring other alternatives, such as the use of neural networks, could also be a promising approach. Additionally, the combination of wind stress estimation from ODYSEA with other satellite products and numerical simulations could improve the representation of wind stress in gridded products. Future efforts should focus on the assessment of the potential of ODYSEA in quantifying the production of eddy kinetic energy through horizontal energy fluxes and air–sea energy fluxes related to divergent and rotational motions.

Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping

Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. In particular, efficient energy estimation may be crucial in achieving a quantum advantage for a practically relevant problem. For instance, the measurement effort poses a critical bottleneck for variational quantum algorithms. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. As a central tool, we establish tail bounds for empirical estimators of the energy. They are helpful for identifying measurement settings that improve the energy estimate the most. This task constitutes an NP-hard problem. However, we are able to circumvent this bottleneck and use the tail bounds to develop a practical, efficient estimation strategy, which we call ShadowGrouping. As the name indicates, it combines shadow estimation methods with grouping strategies for Pauli strings. In numerical experiments, we demonstrate that ShadowGrouping improves upon state-of-the-art methods in estimating the electronic ground-state energies of various small molecules, both in provable and practical accuracy benchmarks. Hence, this work provides a promising way, e.g., to tackle the measurement bottleneck associated with quantum many-body Hamiltonians.

Cordycepin and Its Structural Derivatives Effectively Suppress the High Expression of Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase in Breast Carcinomas: A Computational Drug Development Approach.

Breast cancer is a frequently diagnosed malignant disease and the primary cause of mortality among women with cancer worldwide. The therapy options are influenced by the molecular subtype due to the intricate nature of the condition, which consists of various subtypes. By focusing on the activation of receptors, Epidermal Growth Factor Receptor (EGFR) tyrosine kinase can be utilized as an effective drug target for therapeutic purposes of breast cancer. The objective of this study is to compare the underlying pharmacological properties of several modified agents to the parental Cordycepin to target and inhibit the EGFR tyrosine kinase high expression, and to discover the inhibitor with the highest affinity for this drug target to treat the breast cancer patients. The Maestro Application of Schrödinger Suite Paid Software was initially employed for conducting extra precision (XP) structure-based virtual screening to evaluate the binding affinity of the Cordycepin and its 500 structural derivatives with the EGFR tyrosine kinase protein structure. In addition, the anti-breast cancer activity of the chosen compounds was assessed by looking at their drug-likeness and ADMET characteristics using Lipinski's rule of five along with Quantitative structure- activity relationship (QSAR) validation, the prediction of cell line anti-cancer, as well as anti- breast cancer activity of top docked scored compounds. Subsequently, the Desmond paid software- based molecular dynamics simulations (MDS) were conducted for a duration of 100 nanoseconds on the promising candidates followed by the binding free energy estimation was performed utilizing MM-GBSA analysis. To determine the stability of the protein-ligand complex, root-meansquare deviation (RMSD), root-mean-square fluctuation (RMSF), protein-ligand interactions, and other necessary parameters were evaluated from the 100 ns MDS Trajectory. Based on the overall analysis of our study, N (6)-octylamine adenosine (CID-194932) reported the optimum inhibitory potential against the EGFR tyrosine kinase protein, followed by Adenosine 5-monophosphate (CID-83862) and Cordycepin (CID-6303), which compared favorably to the control drug Vandetanib (CID-3081361). Consequently, these derivative compounds Cordycepin have the potential to be utilized as lead molecules in the development of highly effective and potent EGFR tyrosine kinase inhibitors for the treatment of breast cancer patients.

Fine-tuning molecular mechanics force fields to experimental free energy measurements.

Alchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via free energy calculations that can estimate quantities relevant for drug discovery such as affinities, selectivities, the impact of target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types and parameters, modern approaches based on graph neural networks (GNNs) learn continuous embedding vectors that represent chemical environments from which MM parameters can be generated. Excitingly, GNN parameterization approaches provide a fully end-to-end differentiable model that offers the possibility of systematically improving these models using experimental data. In this study, we treat a pretrained GNN force field-here, espaloma-0.3.2-as a foundation simulation model and fine-tune its charge model using limited quantities of experimental hydration free energy data, with the goal of assessing the degree to which this can systematically improve the prediction of other related free energies. We demonstrate that a highly efficient "one-shot fine-tuning" method using an exponential (Zwanzig) reweighting free energy estimator can improve prediction accuracy without the need to resimulate molecular configurations. To achieve this "one-shot" improvement, we demonstrate the importance of using effective sample size (ESS) regularization strategies to retain good overlap between initial and fine-tuned force fields. Moreover, we show that leveraging low-rank projections of embedding vectors can achieve comparable accuracy improvements as higher-dimensional approaches in a variety of data-size regimes. Our results demonstrate that linearly-perturbative fine-tuning of foundation model electrostatic parameters to limited experimental data offers a cost-effective strategy that achieves state-of-the-art performance in predicting hydration free energies on the FreeSolv dataset.

Dual Targeting of MEK1 and Akt Kinase Identified SBL-027 as a Promising Lead Candidate to Control Cell Proliferations in Gastric Cancer.

Dual inhibition of Akt and MEK1 pathways offers a promising strategy to enhance treatment efficacy in gastric cancer. In this study, we employed computational approaches followed by in vitro validations. Our results demonstrate that SBL-027 exhibits robust and enduring interactions with Akt and MEK1 kinases, as evidenced by atomistic molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) based binding free energy estimates. The predicted Gibbs binding free energies indicate highly favorable interactions between SBL-027 and both Akt and MEK1 kinases. In vitro, SBL-027 displayed an IC50 value of 195.20nM against Akt and 239.10nM against MEK1 enzymes. The compound exhibited potent inhibition of cell proliferation in KATOIII and SNU-5 cells, with GI50 values of 490.70 and 615.14nM, respectively. Moreover, SBL-027 induced an increase in the sub G0/G1 population during the cell cycle of KATOIII and SNU-5 cells, while facilitating early and late-phase apoptosis in these cell lines. Notably, the compound significantly reduced the percentage of dual-positive cells expressing both MEK1 and Akt in gastric cancer cells. The strong binding affinity, stability, and favorable thermodynamics of SBL-027 along with the established in vitro efficacy highlight its potential as a lead compound for further preclinical and clinical development.

Binding mechanism of oligopeptides on solid surface: assessing the significance of single-molecule approach.

This paper addresses the complementarity and potential disparities between single-molecule and ensemble-average approaches to probe the binding mechanism of oligopeptides on inorganic solids. Specifically, we explore the peptide/gold interface owing to its significance in various topics and its suitability to perform experiments both in model and real conditions. Experimental results show that the studied peptide adopts a lying configuration upon adsorption on the gold surface and interacts through its peptidic links and deprotonated thiolate extremities, in agreement with theoretical predictions. Single-molecule force spectroscopy (SMFS) measurements revealed the existence of a wide panel of adhesion forces, resulting from the interaction between individual peptide moieties and the abundant surface sites. We therefore propose methodological developments for sorting the events of interest to understand the peptide adsorption mechanism. Thermodynamic and kinetic aspects of the peptide adsorption are probed using both static and dynamic force spectroscopy measurements. Specifically, we show the possibility of providing a reasonable estimate of the peptide free energy of adsorption ΔadsG° by exploring the fluctuations of the adhesion work, based on the Jarzynski equality, and by using a parametric Gamma estimator. The proposed approach offers a relevant method for studying the different factors influencing the peptide adsorption and evaluating their impact on ΔadsG° as an alternative to exploring adhesion forces that may lead to misinterpretations. This is illustrated by the comparison of the adsorption of two peptides with specific amino acids substitution. Our method provides insights into the overall mechanism by which peptides interact with the surface and allows an integration of the single-molecule versus ensemble-average points of view.

SBL-JP-0004: A promising dual inhibitor of JAK2 and PI3KCD against gastric cancer.

Gastric cancer (GC) remains a global health burden and is often characterized by heterogeneous molecular profiles and resistance to conventional therapies. The phosphoinositide 3-kinase and PI3K and Janus kinase (JAK) signal transducer and activator of transcription (JAK-STAT) pathways play pivotal roles in GC progression, making them attractive targets for therapeutic interventions. This study applied a computational and molecular dynamics simulation approach to identify and characterize SBL-JP-0004 as a potential dual inhibitor of JAK2 and PI3KCD kinases. KATOIII and SNU-5 GC cells were used for in vitro evaluation. SBL-JP-0004 exhibited a robust binding affinity for JAK2 and PI3KCD kinases, as evidenced by molecular docking scores and molecular dynamics simulations. Binding interactions and Gibbs binding free energy estimates confirmed stable and favorable interactions with target proteins. SBL-JP-0004 displayed an half-maximal inhibitory concentration (IC50) value of 118.9 nM against JAK2 kinase and 200.9 nM against PI3KCD enzymes. SBL-JP-0004 exhibited potent inhibition of cell proliferation in KATOIII and SNU-5 cells, with half-maximal growth inhibitory concentration (GI50) values of 250.8 and 516.3 nM, respectively. A significant elevation in the early phase apoptosis (28.53% in KATOIII cells and 26.85% in SNU-5 cells) and late phase apoptosis (17.37% in KATOIII cells and 10.05% in SNU-5 cells) were observed with SBL-JP-0004 treatment compared to 2.1% and 2.83% in their respective controls. The results highlight SBL-JP-0004 as a promising dual inhibitor targeting JAK2 and PI3KCD kinases for treating GC and warrant further preclinical and clinical investigations to validate its utility in clinical settings.

Energy Estimation Method for Power-frequency Arc in Transformer Oil Based on Gap Length and Pressure

Energy Estimation Method for Power-frequency Arc in Transformer Oil Based on Gap Length and Pressure

A geometry-based decomposition method for energy prediction in early design stages of residential buildings

PurposeThe purpose of the study is to develop a geometry-based energy estimation method for surrogate and metamodels to be used in the early design phase of buildings.Design/methodology/approachOptimizing building form and design variables in the early stages of the architectural design process, particularly during the conceptual phase, can significantly enhance overall design performance and energy efficiency at minimal cost. This study introduces a novel decomposition method for evaluating building energy performance by simplifying complex building forms into basic geometric shapes.FindingsThe developed method is applied to certain cases of design variation under specified boundary conditions, and the accuracy of heating and cooling energy loads is calculated with simulated energy models of these cases. As a result of the calculation, accuracy rates between 84.30 and 99.98% were founded.Originality/valueThis paper proposes a prediction model with a geometric identification method for an innovative geometry-based surrogate modeling method. This method also provides a way for artificial intelligence-based prediction models used in surrogate models to create a dataset and can be used in the training in future works.

Enhanced Sampling with Suboptimal Collective Variables: Reconciling Accuracy and Convergence Speed.

We introduce an enhanced sampling algorithm to obtain converged free energy landscapes of molecular rare events, even when the collective variable (CV) used for biasing is not optimal. Our approach samples a time-dependent target distribution by combining the on-the-fly probability enhanced sampling and its exploratory variant, OPES Explore. This promotes more transitions between the relevant metastable states and accelerates the convergence speed of the free energy estimate. We demonstrate the successful application of this combined algorithm on the two-dimensional Wolfe-Quapp potential, millisecond time-scale ligand-receptor binding in the trypsin-benzamidine complex, and folding-unfolding transitions in chignolin mini-protein. Our proposed algorithm can compute accurate free energies at an affordable computational cost and is robust in terms of the choice of CVs, making it particularly promising for the simulation of complex biomolecular systems.

Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.

Enhancing Initial State Overlap through Orbital Optimization for Faster Molecular Electronic Ground-State Energy Estimation

The phase estimation algorithm is crucial for computing the ground-state energy of a molecular electronic Hamiltonian on a quantum computer. Its efficiency depends on the overlap between the Hamiltonian’s ground state and an initial state, which tends to decay exponentially with system size. We showcase a practical orbital optimization scheme to alleviate this issue. Applying our method to four iron-sulfur molecules, we achieve a notable enhancement, up to 2 orders of magnitude, compared to localized orbitals. Furthermore, our approach yields improved overlaps in cytochrome P450 enzyme models. Published by the American Physical Society 2024

ENERGY ESTIMATES FOR A CLASS OF PSEUDO-HYPERBOLIC OPERATORS WITH VARIABLE COEFFICIENTS

A class of strictly pseudo-hyperbolic fourth-order operators with variable coefficients is considered. Energy estimates are established under certain conditions on the coefficients. These estimates imply the uniqueness of the solution to the Cauchy problem, as well as a priori estimates.

Novel Quinoline- and Naphthalene-Incorporated Hydrazineylidene-Propenamide Analogues as Antidiabetic Agents: Design, Synthesis, and Computational Studies.

Background: Type 2 diabetes has become a significant global health challenge. Numerous drugs have been developed to treat the condition, either as standalone therapies or in combination when glycemic control cannot be achieved with a single medication. As existing treatments often come with limitations, there is an increasing focus on creating novel therapeutic agents that offer greater efficacy and fewer side effects to better address this widespread issue. Methods: The methylene derivatives 3a,b were coupled with phenyl/ethyl isothiocyanate in the basic medium, and dimethyl sulfate was subsequently added. Further, 5a-d were reacted with the quinoline/naphthalene hydrazides 6a,b. The target compounds 7a-g were subjected to the in vitro enzyme inhibition studies on α-glucosidase, α-amylase, and aldose reductase. Results: 7g exerted remarkable inhibitory effects on α-glycosidase [Inhibitory Concentration (IC50): 20.23 ± 1.10 µg/mL] and α-amylase (17.15 ± 0.30 µg/mL), outperforming acarbose (28.12 ± 0.20 µg/mL for α-glycosidase and 25.42 ± 0.10 µg/mL for α-amylase), and exhibited a strong inhibition action on aldose reductase (12.15 ± 0.24 µg/mL), surpassing quercetin (15.45 ± 0.32 µg/mL) and the other tested compounds. In a computational study, 7g demonstrated promising binding affinities (-8.80, -8.91 kcal/mol) with α-glycosidase and α-amylase, compared to acarbose (-10.87, -10.38 kcal/mol) for α-glycosidase and α-amylase. Additionally, 7g had strong binding with aldose reductase (-9.20 kcal/mol) in comparison to quercetin (-9.95 kcal/mol). Molecular dynamics (MDs) simulations demonstrated that 7g remained stable over a 100 ns simulation period, and the binding free energy estimates remained consistent throughout this time. Conclusions: We reported the modification of quinoline and naphthalene rings to hydrazineylidene-propenamides 7a-g using various synthetic approaches. 7g emerged as a leading candidate, exhibiting greater inhibition of α-glycosidase, α-amylase, and aldose reductase. These findings underscore their potential as essential molecules for the development of innovative antidiabetic treatments.

DSSCs Sensitized with Phenothiazine Derivatives Containing 1H-Tetrazole-5-acrylic Acid as an Anchoring Unit.

Phenothiazine-based photosensitizers bear the intrinsic potential to substitute various expensive organometallic dyes owing to the strong electron-donating nature of the former. If coupled with a strong acceptor unit and the length of N-alkyl chain is appropriately chosen, they can easily produce high efficiency levels in dye-sensitized solar cells. Here, three novel D-A dyes containing 1H-tetrazole-5-acrylic acid as an acceptor were synthesized by varying the N-alkyl chain length at its phenothiazine core and were exploited in dye-sensitized solar cells. Differential scanning calorimetry showed that the synthesized phenothiazine derivatives exhibited behavior characteristic of molecular glasses, with glass transition and melting temperatures in the range of 42-91 and 165-198 °C, respectively. Based on cyclic and differential pulse voltammetry measurements, it was evident that their lowest unoccupied molecular orbital (LUMO) (-3.01--3.14 eV) and highest occupied molecular orbital (HOMO) (-5.28--5.33 eV) values were fitted to the TiO2 conduction band and the redox energy of I-/I3- in electrolyte, respectively. The experimental results were supported by density functional theory, which was also utilized for estimation of the adsorption energy of the dyes on the TiO2 and its size. Finally, the compounds were tested in dye-sensitized solar cells, which were characterized based on current-voltage measurements. Additionally, for the compound giving the best photovoltaic response, the efficiency of the DSSCs was optimized by a photoanode modification involving the use of cosensitization and coadsorption approaches and the introduction of a blocking layer. Subsequently, two types of tandem dye-sensitized solar cells were constructed, which resulted in an increase in photovoltaic efficiency to 6.37%, as compared to DSSCs before modifications, with a power conversion value of 2.50%.

CO adsorption on CeO2(111): A CCSD(T) benchmark study using an embedded-cluster model.

A benchmark model that combines an embedded-cluster approach for ionic surfaces with wavefunction-based methods to predict the vibrational frequencies of molecules adsorbed on surfaces is presented. As a representative case, the adsorption of CO on the lowest index non-polar and most stable facet of CeO2, that is, (111) was studied. The CO harmonic vibrational frequencies were not scaled semiempirically but explicitly corrected for anharmonic effects, which amount to about 25 cm-1 with all tested methods. The second-order Møller-Plesset perturbation method (MP2) tends to underestimate the CO harmonic frequency by about 40-45 cm-1 in comparison with the results obtained with the coupled-cluster singles and doubles with perturbational treatment of triple excitation method [CCSD(T)] and independently from the basis set used. The best estimate for the CO vibrational frequency (low-coverage case) differs by 12 cm-1 with the experimental value obtained by infrared reflexion absorption spectroscopy of 1 monolayer CO adsorbed on the oxidized CeO2(111) surface. In addition, a conservative estimate of the adsorption energy of about -0.22 ± -0.07eV obtained at the CCSD(T) level confirms the physisorption character of the adsorption of CO on the CeO2(111) surface.