3D Chemical Structures Research Articles

An Updated Structure of Oxybutynin Hydrochloride.

Oxybutynin (Ditropan), a widely distributed muscarinic antagonist for treating the overactive bladder, has been awaiting a definitive crystal structure for ≈50 years due to the sample and technique limitations. Past reports used powder X-ray diffraction (PXRD) to shed light on the possible packing of the molecule however their model showed some inconsistencies when compared with the 2D chemical structure. These are largely attributed to X-ray-induced photoreduction. Here microcrystal electron diffraction (MicroED) is used to successfully unveil the experimental 3D structure of oxybutynin hydrochloride showing marked improvement over the reported PXRD structure. Using the improved model, molecular docking is applied to investigate the binding mechanism between M3 muscarinic receptor (M3R) and (R)-oxybutynin, revealing essential contacts/residues and conformational changes within the protein pocket. A possible universal conformation is proposed for M3R antagonists, which is valuable for future drug development and optimization. This study underscores the immense potential of MicroED as a complementary technique for elucidating unknown pharmaceutical structures, as well as for protein-drug interactions.

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter.

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

Characterizing the 3D Structure of Molecular Cloud Envelopes in the Cloud Factory Simulations

We leverage recent numerical simulations of highly resolved star-forming regions in a Milky Way–like galaxy to explore the nature of extended gaseous envelopes around molecular clouds. We extract a sample of two dozen star-forming clouds from the feedback-dominated suite of Cloud Factory simulations. With the goal of exploring the 3D thermal and chemical structure of the gas, we measure and fit the clouds’ radial profiles with multiple tracers, including nHI , nH2 , nHtot , n CO, and gas temperature. We find that while solar neighborhood clouds recently detected via 3D dust mapping have radially symmetric, low-density envelopes that extend ∼10–15 pc, the simulated cloud envelopes are primarily radially asymmetric with low-density envelopes that extend only ∼2–3 pc. One potential explanation for the absence of extended envelopes in the simulated clouds may be the lack of magnetic fields, while a stronger local feedback prescription compared to solar neighborhood conditions may drive the radially asymmetric cloud morphologies. We make the pipeline used to extract and characterize the radial profiles of the clouds publicly available, which can be used in complementary and future simulations to shed additional light on the key physics shaping the formation and evolution of star-forming structures in the Milky Way.

ProTox 3.0: a webserver for the prediction of toxicity of chemicals.

Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.

Structural Characterization and Molecular Model Construction of Lignite: A Case of Xianfeng Coal

The object of the study is lignite. Analytical testing techniques, such as elemental analysis, 13C nuclear magnetic resonance (13C NMR) spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and high-resolution transmission electron microscopy (HRTEM), were used to acquire information on the structural parameters of lignite. The aromaticity of Xianfeng lignite is 43.57%, and the aromatic carbon structure is mainly naphthalene and anthracene/phenanthrene. The aliphatic carbon structure is dominated by cycloalkanes, alkyl side chains, and hydrogenated aromatics. Oxygen is mainly present in ether oxygen, carboxyl, and carbonyl groups. Nitrogen is mainly in the form of pyrrole nitrogen and quaternary nitrogen. Sulfur is mainly thiophene sulfur. According to the analysis results, the molecular structure model of XF lignite was constructed. The molecular formula is C184H172O39N6S2. The 2D structure was converted to a 3D structure using computer simulation software and optimized. The optimized model has a remarkable stereoconfiguration, and the aromatic lamellae are irregularly arranged in space. The aromatic rings were mainly connected by methylene, hypomethylene, methoxy, and aliphatic rings. In addition, the simulated 13C NMR spectra are in good agreement with the experimental spectra. This shows the rationality of the 3D chemical structure model.

In Silico Studies on Natural Products and Derivatives Against Different Types of Cancer.

According to the World Health Organization (WHO), cancer is the second cause of death worldwide, responsible for almost 10 million deaths and accounting for one in every six deaths. It is a disease that can affect any organ or tissue with rapid progression to the final stage, which is metastasis, in which the disease spreads to different regions of the body. Many studies have been carried out to find a cure for cancer. Early diagnosis contributes to the individual achieving the cure; however, deaths are increasing considerably due to late diagnosis. Thus, this bibliographical review discussed several scientific research works pointing to in silico analyses in the proposition of new antineoplastic agents for glioblastoma, breast, colon, prostate, and lung cancer, as well as some of their respective molecular receptors involved in molecular docking simulations and molecular dynamics. This review involved articles describing the contribution of computational techniques for the development of new drugs or already existing drugs with biological activity; thus, important data were highlighted in each study, such as the techniques used, results obtained in each study, and the conclusion. Furthermore, 3D chemical structures of the molecules with the best computational response and significant interactions between the tested molecules and the PDB receptors were also presented. With this, it is expected to help new research in the fight against cancer, the creation of new antitumor drugs, and the advancement of the pharmaceutical industry and scientific knowledge about studied tumors.

Chem2Side: A Deep Learning Model with Ensemble Augmentation (Conventional + Pix2Pix) for COVID-19 Drug Side-Effects Prediction from Chemical Images

Drug side effects (DSEs) or adverse drug reactions (ADRs) are a major concern in the healthcare industry, accounting for a significant number of annual deaths in Europe alone. Identifying and predicting DSEs early in the drug development process is crucial to mitigate their impact on public health and reduce the time and costs associated with drug development. Objective: In this study, our primary objective is to predict multiple drug side effects using 2D chemical structures, especially for COVID-19, departing from the conventional approach of relying on 1D chemical structures. We aim to develop a novel model for DSE prediction that leverages the CNN-based transfer learning architecture of ResNet152V2. Motivation: The motivation behind this research stems from the need to enhance the efficiency and accuracy of DSE prediction, enabling the pharmaceutical industry to identify potential drug candidates with fewer adverse effects. By utilizing 2D chemical structures and employing data augmentation techniques, we seek to revolutionize the field of drug side-effect prediction. Novelty: This study introduces several novel aspects. The proposed study is the first of its kind to use 2D chemical structures for predicting drug side effects, departing from the conventional 1D approaches. Secondly, we employ data augmentation with both conventional and diffusion-based models (Pix2Pix), a unique strategy in the field. These innovations set the stage for a more advanced and accurate approach to DSE prediction. Results: Our proposed model, named CHEM2SIDE, achieved an impressive average training accuracy of 0.78. Moreover, the average validation and test accuracy, precision, and recall were all at 0.73. When evaluated for COVID-19 drugs, our model exhibited an accuracy of 0.72, a precision of 0.79, a recall of 0.72, and an F1 score of 0.73. Comparative assessments against established transfer learning and machine learning models (VGG16, MobileNetV2, DenseNet121, and KNN) showcased the exceptional performance of CHEM2SIDE, marking a significant advancement in drug side-effect prediction. Conclusions: Our study introduces a groundbreaking approach to predicting drug side effects by using 2D chemical structures and incorporating data augmentation. The CHEM2SIDE model demonstrates remarkable accuracy and outperforms existing models, offering a promising solution to the challenges posed by DSEs in drug development. This research holds great potential for improving drug safety and reducing the associated time and costs.

Versatile Surface Chemistry of Carbon-Based Nanoplatforms by Covalent Bonding, Non-Covalent Linking, Crosslinking, and Self-Assembling

This mini-review discussed the best-known chemical and synthetic methodologies used in the last years to modify carbon allotropes, with an interest in nanotechnology. In this perspective, chemistry with optoelectronics applications and recent trends within bio-applications focusing mainly on graphene and its derivatives were considered. So, the mini-review intended to focus on methodologies to add functional groups with varied reactivities, polymer chemistry, and nanoscale control. These methodologies provide insight for further developments. In this manner, traditional methods using strong acid media to convert simple carbon bonds into carboxylic acid and aldehydes organic functional groups were shown and discussed. Hence, chemical modifications in a variety of solvents could be developed. Notably, many organic chemical reactions, such as bimolecular nucleophilic substitution (SN2), click chemistry, and photochemical reactions, showed essential insights in designing the carbon-based material modifications and the bottom-up method. Moreover, incorporating atomic entities within graphene material defects led to interesting spectroscopic and quantum properties. The atomic change added blemishes to this homogeneous structure, which was tuned to modify optical properties. In addition, the review was also oriented towards the discussion on incorporating polymeric films, such as boron- and silicon-based monomers, to form polymeric-modified carbon-based slides. In this way, organoboranes and organosilanes permitted chemical functionalization because their chemical modification was more accessible on nanosurfaces. Moreover, emphasis was placed on exploiting non-covalent bonding with ions and polarized molecules with the highly electronic densities of graphene and its derivatives. In this manner, the manuscript intends to summarize the main types of reactions and synthetic pathways reported until today. Therefore, particular focus was given to chemical composition, 2D and 3D chemical structures, and their properties related to non-covalent interactions. Thus, based on the properties and reactivity of carbon allotropes, the review was intended to open the analysis and discussion, considering the design of new carbon-based materials, hybrid nanocomposites, and metamaterials.

Investigation of the structure, stability, and relative solubility of psilocybin in water and pure organic solvents: A molecular simulation study

Psilocybin is an indole-based secondary metabolite found naturally in mushrooms which possesses several pharmacological effects. Recently, a large number of experimental investigations have been conducted to characterize the pharmacology of psilocybin and its derivatives, and to develop synthetic pathways to manufacture psilocybin. Nonetheless, current research on the physical characterization of psilocybin is limited in part due to legal restrictions. In the present study, we investigate two unique tautomers of psilocybin as depicted in the 2D chemical structure of psilocybin presented in the recent literature. Using a combination of electronic structure calculations and molecular simulation, we are able to identify and characterize the thermodynamically preferred tautomer. We additionally computed the solvation free energy and investigated the solvation structure of psilocybin in water and 35 organic solvents. We find that hydrogen bonding between psilocybin and the solvent dominates the solvation process. Considering the thermodynamically preferred tautomer, we find that the solubility in water is greater than all of the studied organic solvents.

Advanced biotechnology strategies for detoxification of persistent organic pollutants and toxic elements in soil

This paper aims to comprehensively examine and present the current state of persistent organic pollutants (POPs) and toxic elements (TEs) in soil. Additionally, it seeks to assess the viability of employing advanced biotechnology, specifically phytoremediation with potent microbial formulations, as a means of detoxifying POPs and TEs. In the context of the "global treaty," which is known as the Stockholm Convention, we analyzed the 3D chemical structures of POPs and its prospects for living organisms which have not been reviewed up to date. The obstacles associated with the phytoremediation strategy in biotechnology, including issues like slow plant growth and limited efficiency in contaminant uptake, have also been discussed and demonstrated. While biotechnology is recognized as a promising method for detoxifying persistent organic pollutants (POPs) and facilitating the restoration of contaminated and degraded lands, its full potential in the field is constrained by various factors. Recent advances in biotechnology, such as microbial enzymes, designer plants, composting, and nanobiotechnology techniques, have opened up new avenues for mitigating persistent organic pollutants (POPs) and toxic elements (TEs). The insights gained from this review can contribute to the development of innovative, practical, and economically viable approaches for remediating and restoring soils contaminated with persistent organic pollutants (POPs) and toxic elements (TEs). The ultimate aim is to reduce the risks to both human and environmental health.

Identification of Potential Aldose Reductase Inhibitors Using Convolutional Neural Network-Based in Silico Screening.

Aldose reductase (ALR2) is a notable enzyme of the polyol pathway responsible for aggravating diabetic neuropathy complications. The first step begins when it catalyzes the reduction of glucose to sorbitol with NADPH as a coenzyme. Elevated concentrations of sorbitol damage the tissues, leading to complications like neuropathy. Though considerable effort has been pushed toward the successful discovery of potent inhibitors, its discovery still remains an elusive task. To this end, we present a 3D convolutional neural network (3D-CNN) based ALR2 inhibitor classification technique by dealing with snapshots of images captured from 3D chemical structures with multiple rotations as input data. The CNN-based architecture was trained on the 360 sets of image data along each axis and further prediction on the Maybridge library by each of the models. Subjecting the retrieved hits to molecular docking leads to the identification of the top 10 molecules with high binding affinity. The hits displayed a better blood-brain barrier penetration (BBB) score (90% with more than four scores) as compared to standard inhibitors (38%), reflecting the superior BBB penetrating efficiency of the hits. Followed by molecular docking, the biological evaluation spotlighted five compounds as promising ALR2 inhibitors and can be considered as a likely prospect for further structural optimization with medicinal chemistry efforts to improve their inhibition efficacy and consolidate them as new ALR2 antagonists in the future. In addition, the study also demonstrated the usefulness of scaffold analysis of the molecules as a method for investigating the significance of structurally diverse compounds in data-driven studies. For reproducibility and accessibility purposes, all of the source codes used in our study are publicly available.

Sustainable self‐curing epoxy adhesives from Chios natural Mastic (Pistacia lentiscus L.)

AbstractThis study outlines a method for the epoxidation of poly‐beta‐myrcene, a biopolymer that can be easily extracted from the natural resin of the Chios Mastic tree (Pistacia lentiscus L.). The resulting epoxidation products are self‐curing, and have a nearly 100% green carbon content. These Mastic Epoxide products (MASTEP) have the potential to be useful thermosetting resins. One specific product (MASTEP A), was used as an adhesive to create Single Lap Joints, which were then subjected to tensile shear stress tests. MASTEP A was found to be 25%–83% stronger than three commonly used conventional or “semi‐green” epoxy adhesives. Another specific product (MASTEP B), was found able to be thermally converted into a semi‐crystalline form. In order to clarify the 3D chemical structure of MASTEP, IR, and DSC investigations were conducted. The entire production process was designed to be environmentally friendly. The results of this research represent a significant advancement in the development of environmentally friendly epoxies and pave the way to a wide range of potential further research.

PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction.

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

Dynamically coupled kinetic chemistry in brown dwarf atmospheres – I. Performing global scale kinetic modelling

ABSTRACT The atmospheres of brown dwarfs have been long observed to exhibit a multitude of non-equilibrium chemical signatures and spectral variability across the L, T, and Y spectral types. We aim to investigate the link between the large-scale 3D atmospheric dynamics and time-dependent chemistry in the brown dwarf regime, and to assess its impact on spectral variability. We couple the miniature kinetic chemistry module ‘mini-chem’ to the Exo-FMS general circulation model (GCM). We then perform a series of idealized brown dwarf regime atmospheric models to investigate the dynamical 3D chemical structures produced by our simulations. The GCM output is post-processed using a 3D radiative transfer model to investigate hemisphere-dependent spectral signatures and rotational variability. Our results show the expected strong non-equilibrium chemical behaviour brought on by vertical mixing and global spatial variations due to zonal flows. Chemical species are generally globally homogenized, showing variations of ±10 per cent or less, dependent on pressure level, and follow the dynamical structures present in the atmosphere. However, we find localized storm regions and eddies can show higher contrasts, up to ±100 per cent, in mixing ratio compared to the background global mean. This initial study represents another step in understanding the connection between 3D atmospheric flows in brown dwarfs and their rich chemical inventories.

A mini-chemical scheme with net reactions for 3D general circulation models

Context. The chemical inventory of hot Jupiter (HJ) exoplanet atmospheres continues to be observed by various ground- and space-based instruments in increasing detail and precision. It is expected for some HJs to exhibit strong non-equilibrium chemistry characteristics in their atmospheres, which might be inferred from spectral observations. Aims. We aim to model the 3D thermochemical non-equilibrium chemistry in the atmospheres of the HJs WASP-39b and HD 189733b. Methods. We coupled a lightweight, reduced chemical network ‘mini-chem’ that utilises net reaction rate tables to the Exo-FMS general circulation model (GCM). We performed GCM models of the exoplanets WASP-39b and HD 189733b as case studies of the coupled mini-chem scheme. The GCM results were then post-processed using the 3D radiative-transfer model gCMCRT to produce transmission and emission spectra to assess the impact of non-equilibrium chemistry on their observable properties. Results. Both simulations show significant departures from chemical equilibrium (CE) due to the dynamical motions of the atmosphere. The spacial distribution of species generally closely follows the dynamical features of the atmosphere rather than the temperature field. Each molecular species exhibits a different quench level in the simulations, which is also dependent on the latitude of the planet. Major differences are seen in the transmission and emission spectral features between the CE and kinetic models. Conclusions. Our simulations indicate that considering the 3D kinetic chemical structures of HJ atmospheres has an important impact on the physical interpretation of observational data. Drawing bulk atmospheric parameters from fitting feature strengths may lead to an inaccurate interpretation of chemical conditions in the atmosphere of HJs. Our open source mini-chem module is simple to couple with contemporary HJ GCM models without substantially increasing required computational resources.

AODB: A comprehensive database for antioxidants including small molecules, peptides and proteins

Antioxidants are widely used in the fields of food, medicine, nutraceuticals, and cosmetics. Given their important roles in promoting and maintaining human health, a large number of antioxidants have been reported. Some antioxidant-related databases have been developed; however, the annotation of antioxidants and related information stored in existing databases is incomplete and requires more efficient retrieval methods. This study aimed to develop a manually curated comprehensive antioxidant database (AODB). Currently, it stores 56,666 small molecules tested for antioxidant activity, 1480 antioxidant peptides, and 998 antioxidant proteins, including their structures, names, antioxidant assay records, computable physicochemical and ADMET properties, and sources. AODB supports text search and mining, 2D and 3D chemical structure search, and BLAST-based protein sequence search, enabling users to retrieve antioxidant data quickly and easily. AODB, as a one-stop antioxidant database, can facilitate the exploration of antioxidants and potential applications. AODB is publicly available and updated annually at https://aodb.idruglab.cn/.

Advances in Quantum Properties of Graphene and Derivatives Applied to Functional Nanomaterials and Metamaterials

In the present work we describe the most important quantum properties of graphene and derivatives recently published. We discuss how these properties were incorporated into varied hybrid materials such as substrates for specific tuning of matter to track varied quantum signals. Their potential applications were analyzed from nanomaterial and nanotechnology. In particular graphene, its derivatives and other carbon-based allotropes were chosen due to their special chemical structure and properties from the nanoscale to larger lengths, according to specific applications. As expected, these carbon-based and related materials’ highly ordered and condensed electronic configuration showed particular electronic properties below the nanoscale. Thus, we discussed the generation of pseudo-electromagnetic fields and conduction bands. This particular property could also interact with different quantized energy levels and quantum properties, such as those focused on: i) Dirac electron interaction and conduction, ii) anomalous quantized hall effects, iii) magnetic effects, iv) excitons, v) polaron generations, d vi) Fermi and Landau levels. These different phenomena were discussed about the particular topological states of graphene by tuning their 3D chemical structures. Therefore, quantum phenomena and their possible modifications such as quantum interference, potential improvements and encrypted signal transduction were considered for applications.

3D chemical structure of the diffuse turbulent interstellar medium

Aims. The high abundances of CH+ in the diffuse interstellar medium (ISM) are a long-standing issue of our understanding of the thermodynamical and chemical states of the gas. We investigate here the formation of CH+ in turbulent and multiphase environments, where the heating of the gas is almost solely driven by the photoelectric effect. Methods. The diffuse ISM is simulated using the magnetohydrodynamic (MHD) code RAMSES which self-consistently computes the dynamical and thermal evolution of the gas along with the time-dependent evolutions of the abundances of H+, H, and H2. The rest of the chemistry, including the abundance of CH+, is computed in post-processing, at equilibrium, under the constraint of out-of-equilibrium H+, H, and H2. The comparison with the observations is performed taking into account an often neglected yet paramount piece of information, namely the length of the intercepted diffuse matter along the observed lines of sight. Results. Almost all of the mass of CH+ originates from unstable gas, in environments where the kinetic temperature is higher than 600 K, the density ranges between 0.6 and 10 cm−3, the electronic fraction ranges between 3 × 10−4 and 6 × 10−3, and the molecular fraction is smaller than 0.4. Its formation is driven by warm and out-of-equilibrium H2 initially formed in the cold neutral medium (CNM) and injected in more diffuse environments, and even the warm neutral medium (WNM) through a combination of advection and thermal instability. The simulation that displays the closest agreement with the HI-to-H2 transition and the thermal pressure distribution observed in the solar neighborhood is found to naturally reproduce the observed abundances of CH+, the dispersion of observations, the probability of occurrence of most of the lines of sight, the fraction of nondetections of CH+, and the distribution of its line profiles. The amount of CH+ and the statistical properties of the simulated lines of sight are set by the fraction of unstable gas rich in H2, which is controlled on Galactic scales by the mean density of the diffuse ISM (or, equivalently, its total mass), the amplitude of the mean UV radiation field, and the strength of the turbulent forcing. Conclusions. This work offers a new and natural solution to an 80-yr-old chemical riddle. The almost ubiquitous presence of CH+ in the diffuse ISM likely results from the exchange of matter between the CNM and the WNM induced by the combination of turbulent advection and thermal instability, without the need to invoke ambipolar diffusion or regions of intermittent turbulent dissipation. Through two-phase turbulent mixing, CH+ might thus be a tracer of the H2 mass loss rate of CNM clouds.

Insights to the 3D internal morphology and metal oxidation states of single atmospheric aerosol particles by synchrotron-based methodology

The morphology and metal oxidation states of atmospheric aerosols are pertinent to their formation processes and ensuing interactions with surrounding gases, vapors and other environments upon deposition, such as human respiratory tract, soil and water. Although much progress has been made in recent years through single-particle techniques, considerably less is known with respect to the three-dimensional (3D) internal morphology of single atmospheric aerosol particles due to the limited penetration depth of electron microscopy. In this study, for the first time, a novel synchrotron-based transmission X-ray microscopy (TXM) methodology has been developed to visualize the 3D internal chemical mixing state and structure of single particles. The results show that the TXM is more applicable to the imaging of solid particles containing high-density elements, e.g., iron (Fe), aluminum (Al), silicone (Si), carbon (C) and sulfur (S), and/or solid particles of sizes larger than about 100 nm. In addition, the TXM is capable to reveal the fine 3D topographic features of single particles. The derived 3D internal and external information would be difficult to discern in the 2D images from electron microscopy. The TXM 3D images illustrate that aerosol particles exhibit complex internal mixing state and structure, e.g., homogeneously-, heterogeneously-mixed, multiple inclusions, fibrous, porous, and core-shell configuration. When coupled with the synchrotron-based X-ray fluorescence spectrometry (XRF) and absorption near-edge spectroscopy (XANES) of an X-ray nanoprobe in the energy range of 4–15 keV, the 3D morphology of single particles is further supplemented with the spatial distribution and oxidation sates of selected elements, including Fe, vanadium (V), manganese (Mn), chromium (Cr) and arsenic (As). The presented cross-platform, synchrotron-based methodology shows promise in complementing existing single-particle techniques and providing new insights to the heterogeneity of single-particle micro-physicochemical states relevant to the aerosol chemistry, optical properties, and their environmental and health impacts.

ETHNOPHARMACOLOGICAL AND PHYTOCHEMICAL STUDY OF MAJOR SPECIES FROM LABIATAE FAMILY IN SAUDI ARABIA: A SYSTEMATIC REVIEW

A review on a medicinal plant belong to family Labiatae has been made in the Kingdom of Saudi Arabia. Among the reported flora in the Kingdom of Saudi Arabia, Labiatae family has the highest species. A total of 23 species of Labiatae family has been reported with high medicinal value. A detail review on 12 species of Labiatae family had done to know their phytochemical constituents, medicinal uses and pharmacological properties. In this review we also explore the total chemical constituents present in each plant with a unique list of 3D chemical structures. (E)-2-Hexenal, 1,8-Cineole, Bicyclogermacrene, Borneol, Bornyl acetate, Bulnesol, Caffeic acid, Camphene, Camphor, Eugenol, Gallic acid, Quercetin, Terpinene, Thymol, etc are the important constituent in these 12 species of Labiatae family. This study also shows that Marrubium vulgare L, Ocimum basilicum L, Origanum majorana and Otostegia fruticosa plants had reported with many medicinal uses and pharmacological properties. Medicinal constituents include the highest phenolic, flavonoid content and strong aromatic essential oil. Further scientific investigations on these 12 species are needed to explore toxicity and clinical efficacy and as well as to explore the therapeutic effects of major secondary metabolites. The summarized information from the current review may be a useful tool for researchers to carry out further study and explore other scientific aspects of the herbs.