Number Of Heavy Atoms Research Articles

AI-Driven Electrolyte Additive Selection to Boost Aqueous Zn-Ion Batteries Stability.

In tackling the stability challenge of aqueous Zn-ion batteries (AZIBs) for large-scale energy storage, the adoption of electrolyte additive emerges as a practical solution. Unlike current trial-and-error methods for selecting electrolyte additives, a data-driven strategy is proposed using theoretically computed surface free energy as a stability descriptor, benchmarked against experimental results. Numerous additives are calculated from existing literature, forming a database for machine learning (ML) training. Importantly, this ML model relies solely on experimental values, effectively addressing the challenge of large solvent molecule models that are difficult to handle with quantum chemistry computation. The interpretable linear regression algorithm identifies the number of heavy atoms in the additive molecule and the liquid surface tension as key factors. Artificial intelligence (AI) clustering categorizes additive molecules, identifying regions with the most significant impact on enhancing battery stability. Experimental verification successfully confirms the exceptional performance of 1,2,3-butanetriol and acetone in the optimal region. This integrated methodology, combining theoretical models, data-driven ML, and experimental validation, provides insights into the rational design of battery electrolyte additives.

Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials

We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed literature. The pipeline is applied to 74 reports published between 1957 and 2014 with experimentally-measured oxidation potentials for 592 organic molecules (−0.75 to 3.58 V). After data curation (solvents, reference electrodes, and missed data points), we trained multiple supervised machine learning (ML) models reaching prediction errors similar to experimental uncertainty (∼0.2 V). For experimental measurements of identical molecules reported in multiple studies, we identified the most likely value based on out-of-sample ML predictions. Using the trained ML models, we then estimated oxidation potentials of ∼132k small organic molecules from the QM9 (quantum mechanics data for organic molecules with up to 9 atoms not counting hydrogens) data set, with predicted values spanning 0.21–3.46 V. Analysis of the QM9 predictions in terms of plausible descriptor-property trends suggests that aliphaticity increases the oxidation potential of an organic molecule on average from ∼1.5 V to ∼2 V, while an increase in number of heavy atoms lowers it systematically. The pipeline introduced offers significant reductions in human labor otherwise required for conventional manual data collection of experimental results, and exemplifies how to accelerate scientific research through automation.

Targeting the Interactions of Small GTPase ARF with C9ORF72:SMCR8:WDR41 Complexes Implicated in Amyotrophic Lateral Sclerosis/Frontotemporal Dementia

AbstractBackgroundC9ORF72 plays important roles in many cellular processes, including autophagy, membrane trafficking and immune response. The C9ORF72 mutation is the most common cause of familial amyotrophic lateral sclerosis/frontotemporal dementia (ALS/FTD) which is attributed to both gain‐ and loss‐of‐function mechanisms. C9ORF72 forms a complex with SMCR8 and WDR41, which was reported to regulate signaling of small GTPases such as Rab and ARF proteins. Developing small molecules that modulate interfacial C9ORF72:SMCR8:WDR41 (CSW) and small GTPase interactions will aid in understanding their mode of actions and the potential roles of GTP‐dependent signaling in ALS/FTD. Since the C9ORF72 complex has been shown to elicit prominent GTPase activating protein (GAP) activity on ARF, a major regulator of membrane traffic, we aimed to develop novel small molecules that interfere with the binding between the C9ORF72 complex and ARF1.MethodTo identify novel ligands to interfere with the binding between CSW complex and ARF1, we applied in silico virtual screening methods. To this end, the crystal structure of the CSW in complex with ARF1 (PDB: 7MGE) was used for screening of 40,183,714 chemicals available in Mcule database to find hit compound (s).ResultWe defined the binding pocket in the interface of ARF1 and CSW complex including all essential amino acid residues. For filtering the compounds, stringent criteria such as the number of rotatable bonds and the number of heavy atoms were applied. The top 100 hits were identified using this structure‐based virtual screening which show various interactions with the defined binding site of target. Next, the prepared library (the 100 identified Hits) was used as input for virtual screening using Schrodinger software. Flexible docking was performed using Glide program running under LINUX operating system. Based on vina scoring function, 10% of most active compounds that showed the best docking scores were identified and kept for further analysis.ConclusionRational design, computer‐aided, and in silico screening help developing pharmacological modulators to regulate C9ORF72‐ARF signaling and investigate their potential roles in ALS/FTD.Acknowledgement: This study is supported in part by NIH GM146257 on ALS² Initiative and by The Wooten Foundation.

Application of selected chemometric methods to describe and predict the properties of grafted ceramic membranes

Modification of a ceramic membrane surface for the purpose of hydrophobization can be achieved by the grafting technique involving the application of silane compounds and their derivatives. For the selection of the appropriate modifying compound, chosen chemometric methods can be implemented. In this study, the chemometric methods, i.e., hierarchical cluster analysis (HCA) and principal component analysis (PCA), were used for the evaluation of the properties of selected silane compounds applied as modifiers of ceramic membranes.The results of the calculations indicate that these methods are effective in predicting the properties of modifiers. Moreover, the PCA results provide additional information about the physicochemical properties of modifiers which influence grafted membrane properties. The data are essential when looking for (or synthesizing) new modifiers/linkers necessary for the preparation of ceramic membranes with the desirable properties It was found that physicochemical parameters such as molecular shape, atomic mass, total absolute charge, number of heavy atoms, polarizability, van der Waals volume, electronegativity as well as the number of H-bond acceptors are the most important. These parameters describe the observed properties of the grafting molecules and, simultaneously, the properties of the obtained modified ceramic membranes. It was also demonstrated that HCA and PCA can be used to interpret the previously obtained experimental results. For instance, HCA can limit the number of consecutive experiments aimed at estimating the stability of membranes in various organic solvents. Moreover, the applicability of multiple linear regression (MLR) analysis to predict the values of contact angle (CA) and water fluxes of modified membranes was also examined.

The microwave spectra and molecular structures of (Z)-1-chloro-3,3,3-trifluoropropene and its gas-phase heterodimer with the argon atom

The microwave spectra of (Z)-1-chloro-3,3,3-trifluoropropene and its gas-phase heterodimer with the argon atom in the 5.6 to 18.1 GHz frequency range are first observed and assigned using broadband, chirped pulse, Fourier transform microwave (FTMW) spectroscopy. Subsequent analysis of higher-resolution spectra obtained between 5 and 21 GHz with a narrowband, Balle-Flygare FTMW instrument provides spectroscopic constants, including nuclear quadrupole coupling constants, for five isotopologues of the propene molecule and two isotopologues of the complex with argon. Structural parameters obtained from these spectra show the existence of an intramolecular hydrogen bond between one of the fluorine atoms of the trifluoromethyl group and the hydrogen atom on the adjacent carbon atom. No evidence is seen for internal rotation of the trifluoromethyl group. The location of the argon atom in the heterodimer is consistent with the expectation that it will be positioned so to interact with the greatest number of heavy atoms, and in particular, the polarizable chlorine atom.

Anticholinesterase Agents For Alzheimer's Disease Treatment: An Updated Overview.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that compromises the cognitive system and causes dementia. In general, AD affects people over 65 years old, which implies a social impact if we consider future projections due to the increase in life expectancy. The drugs currently marketed only slow the progression of the disease. In this sense, the search for new drugs is a relevant topic in medicinal chemistry. The therapeutic strategy adopted herein is the cholinergic hypothesis, for which acetylcholinesterase enzyme (AChE) inhibitors constitute the main treatment for the disease. This review compiles research in synthetic and natural compounds with AChE inhibitory function. Data were collected based on investigations of AChE inhibitors in the last 5 years of the 2010 decade. Synthetic and natural compounds were investigated, for which Ligand Based Drug Design (LBDD) and Structure Based Drug Design (SBDD) strategies were performed to better understand the structure-activity relationship of promising therapeutic agents. Prediction of physicochemical and pharmacokinetic properties used to calculate the bioavailability radar, lipophilicity, drug-likeness, and pharmacokinetics parameters (SwissADME) indicated that most active compounds are associated with the following characteristics: molecular weight above 377 g/mol; molar refractivity over 114; fraction Csp3 below 0.39 and TPSA above 43 Å2. The most active compounds had a lipophilicity parameter in the range between 2.5 and 4.52, a predominating lipophilic character. Atoms and bonds/interactions relevant for drug development were also investigated and the data pointed out the following tendencies: number of heavy atoms between 16 and 41; number of aromatic heavy atoms between 6 and 22; number of rotatable bonds between 1 and 14; number of H-bond acceptors between 1 and 11; number of H-bond donors below 7. Molecular docking studies indicated that all compounds had higher Goldscores than the drugs used as a positive control, indicating a stronger interaction with the enzyme. The selected compounds represent a potential for new anticholinesterase drugs and may be good starting-point for the development of new candidates. Also, design rules can be extracted from our analysis.

Brief insight into the in silico properties, structure–activity relationships and biotransformation of fruquintinib, an anticancer drug of a new generation containing a privileged benzofuran scaffold

Current trends in drug design notably consider so-called privileged scaffolds as the core structural fragments with decisive impact on affinity to properly chosen biological targets, potency, selectivity and toxicological characteristics of drugs and prospective drug candidates. Fruquintinib (1) is a novel synthetic selective inhibitor of vascular endothelial growth factor receptor (VEGFR) isoforms, i.e., VEGFR-1, VEGFR-2 and VEGFR-3. The therapeutic agent (1) consists of a flat bicyclic heteroaromatic ring, in which two nitrogens are suitablyincorporated, a core bicyclic heteroaromatic ring – privileged (substituted) benzofuran scaffold, and a pair of hydrogen bond (H-bond) donor and acceptor group, i.e., amide functional moiety. Fruquintinib (1) was first approved in China for the treatment of metastatic colorectal cancer, a severe malignant disease with a high mortality rate. The review article offered a brief insight into the topic of privileged structures, their drug- -like ranges of several parameters, pharmacodynamic characteristics of fruquintinib (1) and various in silico descriptors characterizing drug’s structural and physicochemical properties (molecular weight, number of heavy atoms, number of aromatic heavy atoms, fraction of sp3 C-atoms, number of H-bond acceptors, number of H-bond donors, total polar surface area, molar refractivity, molecular volume as well as parameters of lipophilicity and solubility). Some of these descriptors were related to pharmacokinetics and distribution of fruquintinib (1), and, in addition, might help predict its ability to cross passively the blood–brain barrier (BBB). Moreover, a possible connection between the induction potential on cytochrome P450 isoenzymes (CYP1A2 and CYP3A4) and passive transport of a given drug into the central nervous system via BBB was investigated. Current clinical experience and future directions regarding of fruquintinib (1) were also briefly outlined.

A comparative analysis of different van der Waals treatments for molecular adsorption on the basal plane of 2H-MoS2

The binding energy of hydrogen sulfide, ammonia, ethane, ethylene, butadiene, benzene, toluene, pyridine, pyrrole, and thiophene on the basal plane of the semi-conducting 2H-molybdenum sulfide (MoS2) was calculated with the following flavors of Density Functional Theory (DFT): GGA-PW91, PBE-D3, vdW-DF, optPBE, optB86b, optB88, vdW-TS, and BEEF-vdW. The GGA-PW91 binding energies are negligible (<0.07 eV in magnitude) in all cases. The predictions with vdW-DF and PBE-D3 are the closest (error <0.05 eV) to the isosteric heats of adsorption calculated from reported temperature programmed desorption data for thiophene and butadiene. For all dispersion flavors examined here, the magnitude of the dispersion contribution to the binding energy increases linearly with the number of heavy atoms in the adsorbate, with each atom contributing 0.05 eV (BEEF-vdW) – 0.09 eV (optB88-vdW). This implies that the calculated adsorption constants of molecules larger than acridine (i.e., comprising > 14 non-heavy atoms) can vary by more than four orders of magnitude at industrial conditions depending on the chosen method of dispersion correction. Further, dispersion effects fall off rapidly (>0.03 eV/ non-hydrogen atom/Å) as the adsorbate-surface distance increases.

Experimental and kinetic modeling study of α-methylnaphthalene laminar flame speeds

α-Methylnaphthalene (AMN) is the primary reference bicyclic aromatic compound of diesel, and it is commonly used as a component of diesel, kerosene and jet-fuel surrogates formulated to describe real fuel combustion kinetics. However, few experimental data on neat AMN combustion are available in the literature. This work provides the first measurements of laminar flame speed profiles of AMN/air mixtures at 1 bar varying the initial temperature from 425 to 484 K, and equivalence ratio (φ) between 0.8 and 1.35 paving the way for the kinetic study of AMN combustion chemistry at high temperatures (>1800 K). The experimental data obtained in a spherical reactor are compared with kinetic model simulations. Specifically, the AMN kinetics is implemented from its analogous monocyclic aromatic compound, i.e., toluene, through the analogy and rate rule approach. This method allows to develop kinetic mechanisms of large species from the kinetics of smaller ones characterized by analogous chemical features, namely the aromaticity and the methyl functionality in the case of toluene and AMN. In doing so, it is possible to overcome the need of high-level electronic structure calculations for the evaluation of rate constants, as their computational cost increases exponentially with the number of heavy atoms of the selected species. To assess the validity of this approach, ab initio calculations are performed to derive the rate constants of the H-atom abstraction reactions by H, OH and CH3 radicals from both toluene and AMN. The kinetic model obtained satisfactorily agrees with the measured laminar flame speed profiles. Sensitivity and flux analyses are performed to investigate similarities and differences between the main reaction channels of toluene and AMN combustion, with the former leading to ∼6 cm/s faster flame speed at almost identical conditions (P=1 bar, T∼425 K), as evidenced by both kinetic model simulations and experimental findings.

The Inhibitory Effects of Some Artificial Food Colorings on α-amylase and α-glucosidase: In vitro and In silico Studies

Background: Inhibition of α-amylase and α-glucosidase is considered as an important therapeutic target to manage type 2 diabetes mellitus (T2DM), reducing postprandial hyperglycemia (PPHG). Objective: The present work explored the antidiabetic activities of five artificial food colorings by α- amylase and α-glucosidase enzyme inhibition in vitro and in Silico. Methods: In this study, inhibition of α-amylase and α-glucosidase was evaluated. Further, the interaction between enzymes (α-amylase and α-glucosidase) and ligands (food colorings) was followed by QSAR and molecular docking studies. Results: The in vitro results obtained show that the blue patent (SIN131) exhibited more potent inhibition with IC50 values of 0.03± 0.01 mM and 0.014±0.001 mM against α-amylase and α-glucosidase inhibition respectively compared to acarbose. The QSAR study found a strong correlation between IC50 values with four molecular descriptors. This linear regression confirms that a strong polarity (Apol) and low hydrophobia (ALogP) favor the inhibitory effect of these colorings toward both enzymes. Also, a negative role of the number of heavy atoms has been demonstrated in the phenomenon of inhibition of this enzyme. Finally, the descriptor εlumo (electronic affinity) plays a crucial role on the inhibitory power of these dyes toward both enzymes by electron transfer. The virtual screening of the inhibition of α-amylase and α-glucosidase by these colorings, using Molegro Virtual Docker (MVD), allowed us to obtain stable complexes with interaction energies resulting from the place of hydrogen bonds and several hydrophobic interactions. However, the sulfonate groups of these colorings can be the major factors in the inhibition of these enzymes. On the other hand, Rerank Score with the pose are perfectly correlated (R2&gt; 0.76) to the inhibitory activity of these food colorings measured experimentally. Conclusion: The present study suggests that the Blue Patent V (SIN131) effectively acts as α-amylase and α-glucosidase inhibitor leading to a reduction in starch hydrolysis and eventually to lower glucose levels.

Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies.

An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content and structure of the databases used for training impact the prediction quality are scarce. In this work, we analyze and quantify the relationships learned by a machine learning model (Neural Network) trained on five different reference databases (QM9, PC9, ANI-1E, ANI-1, and ANI-1x) to predict tautomerization energies from molecules in Tautobase. For this, characteristics such as the number of heavy atoms in a molecule, number of atoms of a given element, bond composition, or initial geometry on the quality of the predictions are considered. The results indicate that training on a chemically diverse database is crucial for obtaining good results and also that conformational sampling can partly compensate for limited coverage of chemical diversity. The overall best-performing reference database (ANI-1x) performs on average by 1 kcal/mol better than PC9, which, however, contains about 2 orders of magnitude fewer reference structures. On the other hand, PC9 is chemically more diverse by a factor of ∼5 as quantified by the number of atom-in-molecule-based fragments (amons) it contains compared with the ANI family of databases. A quantitative measure for deficiencies is the Kullback-Leibler divergence between reference and target distributions. It is explicitly demonstrated that when certain types of bonds need to be covered in the target database (Tautobase) but are undersampled in the reference databases, the resulting predictions are poor. Examples of this include the poor performance of all databases analyzed to predict C(sp2)-C(sp2) double bonds close to heteroatoms and azoles containing N-N and N-O bonds. Analysis of the results with a Tree MAP algorithm provides deeper understanding of specific deficiencies in predicting tautomerization energies by the reference datasets due to inadequate coverage of chemical space. Capitalizing on this information can be used to either improve existing databases or generate new databases of sufficient diversity for a range of machine learning (ML) applications in chemistry.

Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation.

Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes, or homogeneous catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or conformational sampling. We present a Free energy Machine Learning (FML) model applicable throughout chemical compound space and based on a representation that employs Boltzmann averages to account for an approximated sampling of configurational space. Using the FreeSolv database, FML's out-of-sample prediction errors of experimental hydration free energies decay systematically with training set size, and experimental uncertainty (0.6 kcal/mol) is reached after training on 490 molecules (80% of FreeSolv). Corresponding FML model errors are on par with state-of-the art physics based approaches. To generate the input representation for a new query compound, FML requires approximate and short molecular dynamics runs. We showcase its usefulness through analysis of solvation free energies for 116k organic molecules (all force-field compatible molecules in the QM9 database), identifying the most and least solvated systems and rediscovering quasi-linear structure-property relationships in terms of simple descriptors such as hydrogen-bond donors, number of NH or OH groups, number of oxygen atoms in hydrocarbons, and number of heavy atoms. FML's accuracy is maximal when the temperature used for the molecular dynamics simulation to generate averaged input representation samples in training is the same as for the query compounds. The sampling time for the representation converges rapidly with respect to the prediction error.

Structure Identification of Novel Compounds Using Simple IR, 1H, and 13C NMR Spectroscopy and Computational Tools

A simple method is suggested to identify the structure of novel compounds with basic IR, 1H, and 13C nuclear magnetic resonance (NMR) spectroscopy and computational tools. With the molecular formula obtained from high‐resolution mass spectrometry, all possible isomers are calculated. The absence or presence of particular functional groups which were inferred from IR, 1H, and 13C NMR and DEPT spectra are used to sort all the possible isomers using SMARTS code and RDKit. Then, the final structure(s) are identified by comparing the correlation between Quantum mechanically calculated 13C NMR chemical shielding constants and experimental 13C chemical shifts of molecules in consideration. We have applied this protocol to five natural compounds, the number of heavy atoms ranging from 11 to 15, and correctly identified the structures of all test cases. The limitations and further consideration are discussed.

Algorithmic Discovery of Tactical Combinations for Advanced Organic Syntheses

Summary Whereas most organic molecules can be synthesized from progressively simpler substrates, syntheses of complex organic targets often involve counterintuitive sequence of steps that first complexify the structure but, by doing so, open up possibilities for pronounced structural simplification in subsequent, downstream steps. Such complexifying/simplifying reaction sequences, called tactical combinations (TCs), can be quite powerful and elegant but also inherently hard to spot—indeed, only some 500 TCs have so far been cataloged, and even fewer are routinely used in synthetic practice. This paper describes computer-driven discovery of large numbers of viable TCs (over 46,000 combinations of reaction classes and ∼4.85 million combinations of reaction variants), the vast majority of which have no prior literature precedent. Examples—including a concise wet lab synthesis of a small natural product—are provided to illustrate how the use of these newly discovered TCs can streamline the design of syntheses leading to important drugs and/or natural products.

Gamma radiation attenuation properties of tellurite glasses: A comparative study

Abstract This work investigated the radiation attenuation characteristics of three series of tellurite glass systems with the following compositions: 30PbO-10ZnO-xTeO2-(60-x)B2O3 where x = 10, 30, 40, 50 and 60 mol%, xBaO–xB2O3–(100-2x)TeO2 with x = 15–40 mol% and 50ZnO–(50-x)P2O5–xTeO2, where x = 0, 10, .40 mol%. The results revealed that the attenuation parameters in all the samples decrease with increase in the energy, which implied that all the samples have better interaction with gamma photons at low energies and thus higher photon attenuating efficiency. From the three systems, the samples coded as PbZnBTe60, BaBTe70 and ZnPTe40 have the lowest half value layer values and accordingly have superior photon attenuation efficacy. The maximum effective atomic number values were found for energy less than 0.1 MeV particularly near the K-edges absorption of the heavy atomic number elements such as Te, Ba and Pb. At the lowest energy, the Zeff values are found in the range of 62.33–66.25, 49.43–50.81 and 24.99–35.83 for series 1–3 respectively. Also, we found that the density of the glass remarkably affects the photon attenuation ability of the selected glasses. The mean free path results showed that the PbO-ZnO-TeO2-B2O3 glass system has better radiation shielding efficiency than the glass samples in series 2 and 3.

Physical Radiation Enhancement Effects Around Clinically Relevant Clusters of Nanoagents in Biological Systems

Here we show that the determining factor for physical radiation enhancement effects for a clinically realistic cluster of heavy-atom bearing nanoparticles is the total number of heavy atoms packed into the cluster. We do this through a multiscale Monte Carlo approach which permits the consideration of radiation transport through clusters of millions of nanoparticles. The finding is in contrast to that predicted when isolated nanoparticles are considered and is a direct consequence of the Auger electrons playing less of a role for clusters compared to isolate nanoparticles. We further show that this result is agnostic to selection of the subcellular region considered to be sensitive to the effects of radiation, provided the inside the cluster of nanoparticles is not considered to be biologically active.

Assessment of scoring functions and in silico parameters for AChE-ligand interactions as a tool for predicting inhibition potency

In this study, 68 crystal structures of complexes between acetylcholinesterase (AChE, EC 3.1.1.7) and its ligands, deposited in the PDB, were analyzed by scoring the functions: LigScore1, LigScore2, PLP1, PLP2, Jain, PMF and PMF04. The scores derived from scoring functions were correlated with an inhibition constant for each ligand (Ki or IC50) in a broad range 10−3 – 10−12 M. The linear correlation model resulted in the highest coefficient of determination (r2) for the PLP2 function, 0.591. The LigScore1 function resulted in the lowest r2 value of 0.226. The PubChem database was the source of in silico computed ligand properties which were then correlated with an inhibition constant for each ligand. For the purposes of this study, two additional non-PubChem parameters were evaluated: total and relative number of sp2 hybridized atoms in the ligand. A high coefficient of determination (r2 > 0.5) was calculated for the following parameters: the number of heavy atoms, molecular mass, and number of atoms with sp2 hybridization. The PLP2 scoring function is a good candidate for drug discovery related to AChE, although a better scoring function could be developed with a higher number of crystal structures of AChE complexes and more reliable kinetic data.

Emerging Chemodiversity and Stereoisomerism in Acyclic Mono- and Dichloroalkanes.

The objective of this pen-and-paper study is to monitor the growth of structural complexity as a function of the number of heavy atoms. This is done here using simple chloroalkanes, the indices of structural complexity being the number of regioisomers and mainly stereoisomeric features. Following a previous work dealing with acyclic alkanes, the present study focuses on all acyclic monochloroalkanes from four heavy atoms (four regioisomers) to 10 heavy atoms (2 to 211 regioisomers, respectively). Similarly, all acyclic dichloroalkanes were drawn, ranging from five heavy atoms to nine heavy atoms (4 to 130 regioisomers, respectively). The first part of the work describes the nature and number of stereoisomeric features seen to emerge in molecular structures featuring one or more stereogenic centers. These molecular features include enantiomerism, diastereoisomerism, pseudoasymmetry, and meso-isomerism. The first appearance of chirality among the monochloro and dichloro homologs occurred in the families with five heavy atoms. Diastereoisomerism, meso-isomerism, and pseudoasymmetry appear in higher families of homologs. The second part of the article is an attempt to rationalize the appearance of regioisomers, stereogenic centers, and stereoisomers as their number grows along homologous series, from one family of regioisomers to the higher ones. Far from being random as anticipated, such increases appear regular and ordered. Indeed, the data available demonstrate exponential increases and suggest the involvement of geometric order in the growth of chemical complexity.

Computational Investigation of the Influence of Halogen Atoms on the Photophysical Properties of Tetraphenylporphyrin and Its Zinc(II) Complexes.

How the tetraphenylporphyrin (TPP) and its zinc(II) complexes (ZnTPP) photophysical properties (absorption energies, singlet-triplet energy gap and spin-orbit coupling contributions) can change due to the presence of an increasing number of heavy atoms in their molecular structures has been investigated by means of density functional theory and its time-dependent formulation. Results show that the increase of the atomic mass of the substituted halogen strongly enhances the spin-orbit coupling values, allowing a more efficient singlet-triplet intersystem crossing. Different deactivation channels have been considered and rationalized on the basis of El-Sayed and Kasha rules. Most of the studied compounds possess the appropriate properties to generate cytotoxic singlet molecular oxygen (1Δg) and, consequently, they can be proposed as photosensitizers in photodynamic therapy.

Aqueous OH Radical Reaction Rate Constants for Organophosphorus Flame Retardants and Plasticizers: Experimental and Modeling Studies.

Aqueous ·OH reaction rate constants ( kOH) for organophosphate esters (OPEs) are essential for assessing their environmental fate and removal potential in advanced oxidation processes (AOPs). Herein experimental and in silico approaches were adopted to obtain kOH values for a variety of OPEs. The determined kOH for 18 OPEs varies from 4.0 × 108 M-1 s-1 to 1.6 × 1010 M-1 s-1. Based on the experimental kOH values, a quantitative structure-activity relationship model that involves molecular structural information on the number of heavy atoms, content index, and the most negative charge of C atoms was developed for predicting kOH of other OPEs. Furthermore, appropriate density functional theory (DFT) and solvation models were selected, which together with transition state theory were employed to predict kOH of three representative OPEs. The deviation between the DFT calculated and the experimental kOH values ( kcal/ kexp) is within 2. Half-lives of the OPEs were estimated to be 0.5-22791.3 days in natural waters and 0.044-19.7 s in AOPs, indicating the OPEs are potentially persistent in natural waters and can be quickly eliminated by AOPs. The determined kOH values and the in silico methods offer a scientific base for assessing OPEs fate in aquatic environments.