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The Zeno line is the locus of points on the temperature–density plane where the compressibility factor of the fluid is equal to one. It has been observed to be straight for a broad variety of real fluids, although the underlying reasons for this are still unclear. In this work, a detailed study of the Zeno line and its relation to the vapor–liquid coexistence curve is performed for two simple model pair-potential fluids: attractive square-well fluids with varying well-widths λ and Mie n-6 fluids with different repulsive exponents n. Interestingly, the Zeno lines of these fluids are curved, regardless of the value of λ or n. We find that for square-well fluids, λ ≈ 1.8 presents a Zeno line, which is the most linear over the largest temperature range. For Mie n-6 fluids, we find that the straightest Zeno line occurs for n between 8 and 10. Additionally, the square-well and Mie fluids with the straightest Zeno line showed the closest quantitative agreement with the vapor–liquid coexistence curve for experimental fluids that follow the principle of corresponding states (e.g., argon, xenon, krypton, methane, nitrogen, and oxygen). These results suggest that the Zeno line can provide a useful additional feature, in complement to other properties, such as the phase envelope, to evaluate molecular models. [ABSTRACT FROM AUTHOR]

3D printers have facilitated a wealth of 3D printed molecular models illustrating key structural concepts for student learning. However, general adoption of 3D printed models in the organic chemistry classroom proceeds slowly as the majority of consumer-grade 3D (fused deposition modeling (FDM) and resin) printers are inherently monochromatic, limited to printing a single color at a time. Here, we report a general procedure for 3D printed multicolor space-filling molecular models and molecular orbital models using freely available software that can be printed and assembled using accessible and affordable consumer-grade 3D printers.

The quadrupole moment of a non-fullerene acceptor (NFA) generated by the constituent electron donor (D) and acceptor (A) units is a significant factor that affects the charge separation (CS) and charge recombination (CR) processes in organic photovoltaics (OPVs). However, its impact on p-type polymer domains remains unclear. In this study, we synthesized p-type molecules, namely acceptor–donor–acceptor (ADA) and donor–acceptor–donor (DAD), which are components of the benchmark PM6 polymer (D: benzodithiophene and A: dioxobenzodithiophene). Planar heterojunction films, a model of bulk heterojunction, were prepared using ADA, DAD, and PM6 as the bottom p-type layers and Y6 NFA as the top n-type layer. Flash-photolysis time-resolved microwave conductivity, femtosecond transient absorption spectroscopy, and quantum mechanical calculations were employed to probe the charge carrier dynamics. Our findings reveal that while the subtle difference in quadrupole moment and energy gradient of the p-type materials has a minimal influence on CS, the molecular type (ADA or DAD) significantly affects the bulk CR. This study expands the understanding of how the p-type component and its conformation at the p/n interface impact the CS and CR in OPVs, highlighting the critical role of molecular donors in optimizing device performance. [ABSTRACT FROM AUTHOR]

The study diagnosed chemistry teacher trainees' difficulties in naming and writing structures of spiro and bicyclic compounds. The case study design was conducted in a constructivist environment to enhance chemistry teacher trainees' ability to construct, represent, and interpret the structural formulae of spiro and bicyclic compounds. Purposive sampling technique was employed to select 126 1st-year chemistry teacher trainees from University of Education, Winneba for the study. The results revealed that chemistry teacher trainees had difficulties in naming and writing structural formulae of spiro and bicyclic compounds. However, through the effective use of molecular model kits in teaching naming and writing structures of spiro and bicyclic compounds, these chemistry teacher trainees became conscious of IUPAC rules for naming and writing spiro and other cyclic compounds. At-test conducted indicated that a statistical significance difference existed between students' performance before the implementation of the intervention and after the intervention. It is recommended that chemistry teachers should adopt the use of molecular model kits in teaching concepts of organic nomenclature.

The use of molecular models in chemistry and biochemistry classes is very effective in helping students understand covalent bonds and the chemical structure of molecules. However, conventional molecular models cannot represent intermolecular interactions such as hydrogen bonds and electrostatic interactions. Herein, we describe 3D printed molecular models of nucleobases with the S and N poles of neodymium magnets placed as the donor and acceptor atoms of hydrogen bonds, respectively. The molecular models, called BasePairPuzzle, allow the user to experience manually how molecular recognition occurs when molecules attract or repel each other. This helps to understand the concept of hydrogen bonding and electrostatic interactions. By playing with two or more molecular parts, by attaching and detaching them, users can understand and realize various biological phenomena at the molecular level, such as why DNA forms complementary A-T and G-C base pairs to store genetic information, how DNA mutations can occur, and how nucleic acids can form various noncomplementary base pairs to have complex structures and functions other than storing and transmitting genetic information. In addition, for professional research scientists, the molecular models can support the discovery of new molecular design ideas in drug discovery and nanotechnology.

Augmented reality (AR) filters are digital interactions applied to users' faces or environment to add to or change what they are seeing in the real world. By using free software, it is possible to easily make many AR filters for learning chemistry. Here I show how to make AR filters of molecular models and post them on social media in just two steps.

Visuo-semiotic models, such as Lewis structures and ball & stick models, are widely used to enhance students' learning. However, there is limited research about the impact of these models on specific visuo-semiotic reasoning skills. In the current research, we aimed to determine the extent to which physical molecular models could enhance specific visuo-semiotic reasoning skills among students. The research question that we explored was, "what is the impact of physical molecular models on Grade 11 students' visuo-semiotic reasoning skills related to Lewis structures and ball & stick models of ammonia?" In this mixed-methods research, we collected data from purposively selected Grade 11 chemistry students aged between 15 and 18 from an under-resourced school in South Africa. Through a quasi-experimental design, participants in the experimental group (n = 101) used physical molecular models to learn about Lewis structure and ball & stick models of ammonia while participants in the control group (n = 100) did not. We subsequently tested students' visuo-semiotic reasoning skills. Results show that using physical molecular models significantly improved students' visuo-semiotic reasoning skills and reduced associated learning difficulties. We, therefore, recommend that these models should be used as an instructional tool to enhance learning.

Toxoplasma gondii (T. gondii) is an obligate intracellular, zoonotic protozoan parasite of interest to physicians and veterinarians with its highly complex structure. It is known to infect about one-third of the world’s population. Since it is a zoonotic disease, it is necessary to keep the animal population under control in order to prevent human exposure. Many studies have been conducted on the detection of T. gondii and it has been determined that there are three clonal groups consisting of types 1, 2, 3. Developments in molecular studies have led to changes in the taxonomy and new developments in parasitic diseases. It has helped in diagnosis, treatment, development of antiparasitic drugs and research on resistance. They also provided research on vaccine studies, genetic typing and phylogenetics of parasitic diseases. Conventional polymerase chain reaction (PCR), real-time PCR and genotyping studies conducted today increase our knowledge about T. gondii. Methods such as B1, SAG1, SAG2, GRA1, 529-bprepeat element, OWP genes and 18S rRNAs are mostly used in PCR, and methods such as MS, MLST, PCR-RFLP, RAPD-PCR and HRM are used in genotyping. Toxoplasmosis is a disease that is within the framework of the concept of one health and must attract attention, has not yet been eradicated in the world and needs joint studies for humans, animals and ecosystems to be eradicated. This can only be possible by establishing interdisciplinary groups, conducting surveys and training.

This paper describes an innovative method to evaluate students' learning after teaching the topic of stereochemistry using molecular models in an organic chemistry course for second-year pharmacy students at Princess Nourah bint Abdulrahman University. This method is practical and relevant and directly measures the intended learning skills. It is also easy to structure, standardized, and less time-consuming to grade. The students' outcomes using this method are presented and compared to outcomes of a conventional assessment method using a two-dimensional (2D) paper-based exam.

Entropy scaling relates dynamic and thermodynamic properties by reducing the viscosity to a function of only the residual entropy. Molecular simulations are used to investigate the entropy scaling of the viscosity of three models of sodium chloride and five monovalent salts. Even though the correlation between the potential energy and the virial is weak, entropy scaling applies at liquid densities for all models and salts investigated. At lower densities, entropy scaling breaks down due to the formation of ion pairs and chains. Entropy scaling can be used to develop more extendable correlations for the dynamic properties of molten salts. [ABSTRACT FROM AUTHOR]

Highly oriented and crystalline polyetheylene (PE) fibers have a large failure stress under rapid tensile loading but exhibit significant creep at much smaller stresses that limits applications. A possible mechanism is slip of chains due to stress-enhanced, thermally activated nucleation of dislocations at chain ends in crystalline regions. Molecular dynamics simulations are used to parameterize a Frenkel–Kontorova model that provides analytic expressions for the limiting stress and activation energy for dislocation nucleation as a function of stress. Results from four commonly used hydrocarbon potentials are compared to show that the qualitative behavior is robust and estimate quantitative uncertainties. In all cases, the results can be described by an Eyring model with values of the zero-stress activation energy E a 0 ≈ 1.5 eV and activation volume V* ≈ 45 Å3 that are consistent with the experimental results for increasingly crystalline materials. The limiting yield stress is ∼8 GPa. These results suggest that activated dislocation nucleation at chain ends is an important mechanism for creep in highly oriented PE fibers. [ABSTRACT FROM AUTHOR]

New-particle formation from condensable gases is a common atmospheric process that has significant but uncertain effects on aerosol particle number concentrations and aerosol–cloud–climate interactions. Assessing the formation rates of nanometer-sized particles from different vapors is an active field of research within atmospheric sciences, with new data being constantly produced by molecular modeling and experimental studies. Such data can be used in large-scale climate and air quality models through parameterizations or lookup tables. Molecular cluster dynamics modeling, ideally benchmarked against measurements when available for the given precursor vapors, provides a straightforward means to calculate high-resolution formation rate data over wide ranges of atmospheric conditions. Ideally, the incorporation of such data should be easy, efficient and flexible in the sense that same tools can be conveniently applied for different data sets in which the formation rate depends on different parameters. In this work, we present a tool to generate and interpolate lookup tables of formation rates for user-defined input parameters. The table generator primarily applies cluster dynamics modeling to calculate formation rates from an input quantum chemistry data set defined by the user, but the interpolator may also be used for tables generated by other models or data sources. The interpolation routine uses a multivariate interpolation algorithm, which is applicable to different numbers of independent parameters, and gives fast and accurate results with typical interpolation errors of up to a few percent. These routines facilitate the implementation and testing of different aerosol formation rate predictions in large-scale models, allowing the straightforward inclusion of new or updated data without the need to apply separate parameterizations or routines for different data sets that involve different chemical compounds or other parameters. [ABSTRACT FROM AUTHOR]

The asphalts were aged at different ageing levels to simulate oxidative ageing evolution. The changes in the four components and chemical functional groups of the asphalts at different ageing stages were analysed, and their corresponding asphalt molecular models were established. Then, the self-healing molecular models during ageing evolution were established based on the asphalt molecular models. The self-healing characteristics of the virgin, fast-aging, slow-aging and full-aging stages were studied using the density, radial distribution function (RDF), energy, relative concentration and diffusion coefficients. The results show that the characteristic peak values of RDF curves at the same distance increase with ageing. The bond energy increases less in the fast-ageing stage but greatly in the slow-ageing stage, due to limited sulphur atoms in the asphalt molecule, while the non-bond energy increases greatly in the fast-ageing stage, and less in the slow-ageing stage, due to the stronger polarity of sulphoxide. The relative concentration of saturate changes most, and asphaltene changes less, aromatic and resins change evenly during ageing. The decrease rate of the diffusion coefficient of saturate, aromatic, resin and asphaltene is 12.6%, 11.12%, 10.42% and 9.2% in the fast-ageing stage and 0.62%, 0.60%, 0.55% and 0.62% in the slow-ageing stage. [ABSTRACT FROM AUTHOR]

Accurate rovibrational molecular models are employed to gain insight in high-resolution into the collective effects and intermolecular processes arising when molecules in the gas phase interact with a resonant infrared (IR) radiation mode. An efficient theoretical approach is detailed, and numerical results are presented for the HCl, H2O, and CH4 molecules confined in an IR cavity. It is shown that by employing a rotationally resolved model for the molecules, revealing the various cavity-mediated interactions between the field-free molecular eigenstates, it is possible to obtain a detailed understanding of the physical processes governing the energy level structure, absorption spectra, and dynamic behavior of the confined systems. Collective effects, arising due to the cavity-mediated interaction between molecules, are identified in energy level shifts, in intensity borrowing effects in the absorption spectra, and in the intermolecular energy transfer occurring during Hermitian or non-Hermitian time propagation. [ABSTRACT FROM AUTHOR]

Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free Energy Path Reweighting (ReSolv) framework to parameterize an implicit solvent ML potential for small organic molecules that accurately predicts the hydration free energy, an essential parameter in drug design and pollutant modeling. Learning on a combination of experimental hydration free energy data and ab initio data of molecules in vacuum, ReSolv bypasses the need for intractable ab initio data of molecules in an explicit bulk solvent and does not have to resort to less accurate data-generating models. On the FreeSolv dataset, ReSolv achieves a mean absolute error close to average experimental uncertainty, significantly outperforming standard explicit solvent force fields. Compared to the explicit solvent ML potential, ReSolv offers a computational speedup of four orders of magnitude and attains closer agreement with experiments. The presented framework paves the way for deep molecular models that are more accurate yet computationally more cost-effective than classical atomistic models. [ABSTRACT FROM AUTHOR]

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer, a web application built using the Mol* framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

The reactivity of Zn 2+ tetrahedral complexes with H 2 O 2 was investigated in silico, as a first step in their disruption process. The substrates were chosen to represent the cores of three different zinc finger protein motifs, i. e., a Zn 2+ ion coordinated to four cysteines (CCCC), to three cysteines and one histidine (CCCH), and to two cysteines and two histidines (CCHH). The cysteine and histidine ligands were further simplified to methyl thiolate and imidazole, respectively. H 2 O 2 was chosen as an oxidizing agent due to its biological role as a metabolic product and species involved in signaling processes. The mechanism of oxidation of a coordinated cysteinate to sulfenate-κS and the trends for the different substrates were rationalized through activation strain analysis and energy decomposition analysis in the framework of scalar relativistic Density Functional Theory (DFT) calculations at ZORA-M06/TZ2P ae // ZORA-BLYP-D3(BJ)/TZ2P. CCCC is oxidized most easily, an outcome explained considering both electrostatic and orbital interactions. The isomerization to sulfenate-κO was attempted to assess whether this step may affect the ligand dissociation; however, it was found to introduce a kinetic barrier without improving the energetics of the dissociation. Lastly, ligand exchange with free thiolates and selenolates was investigated as a trigger for ligand dissociation, possibly leading to metal ejection; molecular docking simulations also support this hypothesis., (© 2024 Wiley-VCH GmbH.)

Physical molecular models have played a valuable role in our understanding of the invisible nano-scale world. We discuss 3D printing and its use in producing models of the molecules of life. Complex biomolecular models, produced from 3D printed parts, can demonstrate characteristics of molecular structure and function, such as viral self-assembly, protein folding, and DNA structure. Advances in computer and user interface technology have enabled physical molecular models to combine with augmented reality to bridge the physical with the computational world. [This article was published in "Journal of Biocommunication," v40 n1 p15-21 2016.]

Amidoximated absorbents (AO‐PAN) effectively remove U(VI) from aqueous solution, but previous studies reported more variability for complex natural waters that contain additional confounding ions and molecules. Ternary phases containing U(VI), M(III) (M=Fe(III), Al(III), Ga(III)), and organic molecules exist under these conditions and cause heterogeneous U(VI) uptake on AO‐PAN. The goal of the current study is to provide additional insights into the structural features ternary complexes using N‐(2‐hydroxyethyl)‐iminodiacetic acid (HEIDI) as the model organic chelator and explore the relevance of these species on U(VI) capture. Three model compounds ([(UO2)(Fe)2(μ3‐O)(C6NO5H8)2(H2O)4] (UFe2), ([(UO2)(Al)2(μ2‐OH)(C6NO5H8)2(H2O)3] (UAl2) and [(UO2)(Ga)2(μ2‐OH)(C6NO5H8)2(H2O)3] (UGa2)) were characterized by single‐crystal X‐ray diffraction. Raman spectra of the model compounds were compared with solution data and the ternary phases were noted in the case of Al(III) and Ga(III), but not in the Fe(III) system. U(VI) adsorption onto AO‐PAN was not impacted by the presence of HEIDI or the trivalent metal species. [ABSTRACT FROM AUTHOR]

Molybdenum disulfide (MoS2) is a layered material with outstanding electrical and optical properties. Numerous studies evaluate the performance in sensors, catalysts, batteries, and composites that can benefit from guidance by simulations in all-atom resolution. However, molecular simulations remain difficult due to lack of reliable models. We introduce an interpretable force field for MoS2 with record performance that reproduces structural, interfacial, and mechanical properties in 0.1% to 5% agreement with experiments. The model overcomes structural instability, deviations in interfacial and mechanical properties by several 100%, and empirical fitting protocols in earlier models. It is compatible with several force fields for molecular dynamics simulation, including the interface force field (IFF), CVFF, DREIDING, PCFF, COMPASS, CHARMM, AMBER, and OPLS-AA. The parameters capture polar covalent bonding, X-ray structure, cleavage energy, infrared spectra, bending stability, bulk modulus, Youngs modulus, and contact angles with polar and nonpolar solvents. We utilized the models to uncover the binding mechanism of peptides to the MoS2 basal plane. The binding strength of several 7mer and 8mer peptides scales linearly with surface contact and replacement of surface-bound water molecules, and is tunable in a wide range from -86 to -6 kcal mol-1. The binding selectivity is multifactorial, including major contributions by van-der-Waals coordination and charge matching of certain side groups, orientation of hydrophilic side chains towards water, and conformation flexibility. We explain the relative attraction and role of the 20 amino acids using computational and experimental data. The force field can be used to screen and interpret the assembly of MoS2-based nanomaterials and electrolyte interfaces up to a billion atoms with high accuracy, including multiscale simulations from the quantum scale to the microscale.

We develop fully glycosylated computational models of ACE2-Fc fusion proteins which are promising targets for a COVID-19 therapeutic. These models are tested in their interaction with a fragment of the receptor-binding domain (RBD) of the Spike Protein S of the SARS-CoV-2 virus, via atomistic molecular dynamics simulations. We see that some ACE2 glycans interact with the S fragments, and glycans are influencing the conformation of the ACE2 receptor. Additionally, we optimize algorithms for protein glycosylation modelling in order to expedite future model development. All models and algorithms are openly available.

Single atoms of uranium supported on molybdenum sulfide surfaces (U@MoS 2 ) have been recently demonstrated to facilitate the hydrogen evolution reaction (HER) through electrocatalysis. Theoretical calculations have predicted uranium hydroxide moieties bound to edge-sulfur atoms of MoS 2 as a proposed transition state involved in the HER process. However, the isolation of relevant intermediates involved in this process remains a challenge, rendering mechanistic hypotheses unverified. The present work describes the isolation and characterization of a uranium-hydroxide intermediate on molybdenum sulfide surfaces using [(Cp* 3 Mo 3 S 4 )UCp*], a molecular model of a reduced uranium center supported at MoS 2 . Mechanistic investigations highlight the metalloligand cooperativity with uranium involved in the water activation pathway. The corresponding uranium-oxo analogue, [(Cp* 3 Mo 3 S 4 )Cp*U(═O)], was also accessed from the hydroxide cluster via hydrogen atom transfer and from [(Cp* 3 Mo 3 S 4 )UCp*] through an alternative direct oxygen atom transfer. These results provide an atomistic perspective on the reactivity of low-valent uranium at molybdenum sulfide surfaces toward water, modeling key intermediates associated with the HER of U@MoS 2 catalysts.

The relationship between saturate, aromatic, resin, and asphaltene (SARA) contents and asphalt properties remains unclear. This study aimed to propose a high‐throughput molecular dynamics simulation framework and demonstrate its application in rapidly building asphalt molecular models of various SARA ratios and predicting their properties, using density as an example. Based on the framework, 400 models with varying SARA ratios with different aging degrees were generated to calculate their densities and used to train machine learning algorithms. The ordinary least squares model achieved R2 values exceeding 80%, and quantitative formulas linking asphalt density to SARA ratios were derived. It was found that saturate content negatively correlates with asphalt density, while resin content positively correlates with asphalt density. Additionally, asphalt density and viscosity increase with aging, influenced simultaneously by the SARA ratio and aging degree. Overall, this paper creates a rapid, high‐throughput molecular simulation pathway to predict asphalt behavior. [ABSTRACT FROM AUTHOR]

Recent proposals that the substrate of memory is molecular raise questions about where this molecular model stands in relation to the dominant synaptic model of memory. In this article, we address the perceived rivalry between these models and ask whether they can be integrated. We argue that addressing rivalry or integration requires delineating the explananda of synaptic and molecular models, as well as revisiting assumptions about how these models account for their explananda. The perceived rivalry between these models exemplifies epistemic costs that arise when we try to pit explanatory models as rivals or integrate them. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) changes the composition of the CSF. Here the authors use patient samples and aggregate CSF biomarkers into models that predict disability across all MS phenotypes, and identify potentially causal mechanisms and molecular disease heterogeneity.

Abstract Background The multistep molecular model of breast carcinogenesis is based on the oestrogen receptor(ER) status of the tumour. Its two main arms comprise ER-positive and ER-negative breast carcinomas(BCa), which are associated with Nottingham grade(NG) of the tumour and different proliferative epithelial changes. According to the model, columnar cell lesions(CCL), lobular carcinoma in-situ(LCIS) and atypical ductal hyperplasia(ADH), low-grade ductal carcinoma in-situ (LG-DCIS) are associated with low grade ER-positive tumours and microglandular adenosis (MGA), pleomorphic LCIS(PLCIS), high-grade DCIS(HG-DCIS) are associated with ER-negative high grade tumours. This study aims to describe the association between proliferative epithelial changes in breast tissue adjacent to tumour, in relation to the ER status and NG of the tumour. Methods This descriptive cross-sectional study included 420, wide local excision and mastectomy specimens of BCa from National Hospital of Sri Lanka, between 2017–2019. The histopathological features of the tumour and proliferative epithelial changes in tumour adjacent tissue within 10 mm distance from the tumour-host interface were evaluated independently by two pathologists. The ER, PR(Progesterone receptor) and HER2 status assessed by immunohistochemistry(IHC) was reviewed. The associations between above epithelial lesions and ER status and NG{categorised as low grade (NG1 and NG2) and high grade (NG3)} of the tumour were analyzed. Results ER positive BCa showed significant associations with CCH (p = 0.04), FEA (p = 0.035) and LGDCIS (p