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Graphene Oxides (GO) typically contains different oxygen containing groups such as hydroxyl, carboxyl and epoxy, and reduced GO (r-GO) represents a family of material with diverse chemical properties. In an effort to understand how properties of r-GO change as GO is reduced, a stepwise reduction of the same GO to r-GO containing different levels of oxygen was carried out, and their corresponding chemical and colloidal properties are reported. Starting with GO containing 49 percent oxygen, r-GOs containing 31, 19 and 9 percent oxygen were synthesized. The aqueous behavior in terms of solubility gradually decreased from 7.4 µg/ml for GO to nearly zero for r-GO with 9% oxygen, while dispersibility under sonication decreased from 8 to 2.5 µg/ml for the same samples. Hydrophobicity index as measured as the octanol water partition coefficient decreased from −3.89 to 5.2% as oxygen content dropped from 49 to 9%. Colloidal behavior was also dramatically affected by reduction, and critical coagulation concentration (CCC) dropped from 28 to 15 in presence of 0.5 mmole/l NaCl and from 6 to 2 in presence of 0.5 mmole/l MgCl 2 as the oxygen in the original GO was reduced to 9%.

Interest in developing physically active pediculicides has identified new active substances. The objective was to evaluate a new treatment for clinical efficacy. We describe the selection of 1,2-octanediol as a potential pediculicide. Clinical studies were community based. The main outcome measure was no live lice, after two treatments, with follow up visits over 14 days. Study 1 was a proof of concept with 18/20 (90%) participants cured. Study 2 was a multicentre, parallel, randomised, observer-blind study (520 participants) that compared 0.5% malathion liquid with 1,2-octanediol lotion (20% alcohol) applied 2-2.5 hours or 8 hours/overnight. 1,2-octanediol lotion was significantly (p<0.0005) more effective with success for 124/175 (70.9%) RR = 1.50 (97.5% CI, 1.22 to 1.85) for 2-2.5 hours, and 153/174 (87.9%) RR = 1.86 (97.5% CI, 1.54 to 2.26) for 8 hours/overnight compared with 81/171 (47.4%) for malathion. Study 3, a two centre, parallel, randomised, observer-blind study (121 participants), compared 1,2-octanediol lotion, 2-2.5 hours with 1,2-octanediol alcohol free mousse applied for 2-2.5 hours or 8 hours/overnight. The mousse applied for 8 hours/overnight cured 31/40 (77.5%), compared with 24/40 (60.0%) for lotion (RR = 1.29, 95% CI, 0.95 to 1.75; NNT = 5.7) but mousse applied for 2-2.5 hours 17/41 (41.5%) was less effective than lotion (RR = 0.69, 95% CI, 0.44 to 1.08). Adverse events were more common using 1,2-octanediol lotion at both 2-2.5 hours (12.0%, p = 0.001) and 8 hours/overnight (14.9%, p<0.0005), compared with 0.5% malathion (2.3%). Similar reactions were more frequent (p<0.045) using lotion compared with mousse. 1,2-octanediol was found to eliminate head louse infestation. It is believed to disrupt the insect's cuticular lipid, resulting in dehydration. The alcohol free mousse is more acceptable exhibiting significantly fewer adverse reactions. Controlled-Trials.com ISRCTN66611560, ISRCTN91870666, ISRCTN28722846.

Partition or distribution coefficients (and increasingly referred to as partition ratios) are widely used in environmental science to relate the concentration of a chemical solute in one phase to that in a second phase between which equilibrium applies or is approached. The solutes include organic and inorganic substances; the focus of this paper being on the former. The phases of interest include air, water, soils, sediments, aerosols, and biotic phases, such as lipids, blood, and various tissues. Availability of reliable partition coefficients for contaminants is essential for regulatory and scientific purposes, the general aim being to understand and predict the distribution of the substances in multimedia environmental and biological systems. The history of partition coefficients is reviewed, followed by a brief outline of their theoretical basis and a discussion of methods for determining partition coefficients both empirically and using a variety of predictive methods. It is suggested that ultimately a combination of empirical measurements, quantitative structure–property relationships, and computationally intensive quantum chemical molecular modeling techniques is required to provide accurate data for the large and increasing number of chemicals of commerce that may enter the environment.

Graph neural networks (GNNs) have emerged as powerful tools to accurately predict materials and molecular properties in computational and automated discovery pipelines. In this article, we exploit the invertible nature of these neural networks to directly generate molecular structures with desired electronic properties. Starting from a random graph or an existing molecule, we perform a gradient ascent while holding the GNN weights fixed in order to optimize its input, the molecular graph, towards the target property. Valence rules are enforced strictly through a judicious graph construction. The method relies entirely on the property predictor; no additional training is required on molecular structures. We demonstrate the application of this method by generating molecules with specific energy gaps verified with density functional theory (DFT) and with specific octanol-water partition coefficients (logP). Our approach hits target properties with rates comparable to or better than state-of-the-art generative models while consistently generating more diverse molecules. Moreover, while validating our framework we created a dataset of 1617 new molecules and their corresponding DFT-calculated properties that could serve as an out-of-distribution test set for QM9-trained models. Graph neural networks (GNNs) have become ubiquitous as molecular property predictors. Here, authors propose a method to use them in reverse to directly generate diverse functional molecules with desired properties.

Reverse osmosis (RO) and nanofiltration (NF) are ubiquitous technologies in modern water treatment, finding applications across various sectors. However, the availability of high-quality water suitable for RO/NF feed is diminishing due to droughts caused by global warming, increasing demand, and water pollution. As concerns grow over the depletion of precious freshwater resources, a global movement is gaining momentum to utilize previously overlooked or challenging water sources, collectively known as “marginal water”. Fouling is a serious concern when treating marginal water. In RO/NF, biofouling, organic and colloidal fouling, and scaling are particularly problematic. Of these, organic fouling, along with biofouling, has been considered difficult to manage. The major organic foulants studied are natural organic matter (NOM) for surface water and groundwater and effluent organic matter (EfOM) for municipal wastewater reuse. Polymeric substances such as sodium alginate, humic acid, and proteins have been used as model substances of EfOM. Fouling by low molecular weight organic compounds (LMWOCs) such as surfactants, phenolics, and plasticizers is known, but there have been few comprehensive reports. This review aims to shed light on fouling behavior by LMWOCs and its mechanism. LMWOC foulants reported so far are summarized, and the role of LMWOCs is also outlined for other polymeric membranes, e.g., UF, gas separation membranes, etc. Regarding the mechanism of fouling, it is explained that the fouling is caused by the strong interaction between LMWOC and the membrane, which causes the water permeation to be hindered by LMWOCs adsorbed on the membrane surface (surface fouling) and sorbed inside the membrane pores (internal fouling). Adsorption amounts and flow loss caused by the LMWOC fouling were well correlated with the octanol-water partition coefficient (log P). In part 2, countermeasures to solve this problem and applications using the LMWOCs will be outlined.

The present study was conducted to develop a predictive type of PC-SAFT EOS by incorporating the COSMO computations. With the proposed model, the physical adjustable inputs to PC-SAFT EOS were determined from the suggested correlations with dependency to COSMO computation results. Afterwards, we tested the reliability of the proposed predictive PC-SAFT EOS by modeling the solubility data of certain pharmaceutical compounds in pure and mixed solvents and their octanol/water partition coefficients. The obtained RMSE based on logarithmic scale for the predictive PC-SAFT EOS was 1.435 for all of the solubility calculations. The reported values (1.435) had a lower value than RMSE for COSMO-SAC model (4.385), which is the same as that for RMSE for COSMO-RS model (1.412). The standard RMSE for octanol/water partition coefficient of the investigated pharmaceutical compounds was estimated to be 1.515.

In this study, a predictive model named COSMO-SAC was investigated in solid/liquid equilibria for pharmaceutical compounds. The examined properties were the solubility of drug in the pure and mixed solvents, octanol/water partition coefficient, and cocrystal formation. The results of the original COSMO-SAC model (COSMO-SAC (2002)) was compared with a semi-predictive model named Flory–Huggins model and a revised version of the COSMO-SAC (COSMO-SAC (2010)). The results indicated the acceptable accuracy of the COSMO-SAC (2002) in the considered scope. The results emphasized on the suitability of the COSMO-SAC model for simple molecules containing C, H, and O by covalent and hydrogen bonding interactions. Applicability of the COSMO-SAC for more complicated molecules made of various functional groups such as COO and COOH doubly requires more modification in the COSMO-SAC.

Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water–octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water–octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.

Dietary uptake is the main pathway of exposure to polycyclic aromatic hydrocarbons (PAHs). However, there is no data regarding the pollution and health risks posed by PAHs in Lilium davidii var. unicolor . We measured the concentrations of 16 PAHs in lily bulbs from Lanzhou; analyzed the bioaccumulation, sources, and pollution pathways of PAHs; assessed the influence of baking on PAH pollution in the bulb; and assessed the cancer risks associated with PAH exposure via lily consumption. The total PAH concentrations in raw bulbs were 30.39–206.55 μg kg -1 . The bioconcentration factors of total PAHs ranged widely from 0.92 to 5.71, with a median value of 2.25. Pearson correlation analysis revealed that the octanol-water partition coefficients and water solubility values played important roles in the bioaccumulation of naphthalene, fluorene, phenanthrene, pyrene, and fluoranthene in the raw bulb by influencing PAH availability in soil. Correlation analysis and principal component analysis with multivariate linear regression indicated that biomass and wood burning, coal combustion, diesel combustion, and petroleum leakage were the major sources of PAHs in the raw bulbs. The paired t-test showed that the PAH concentrations in the baked bulbs were higher than those in the raw bulbs. PAH compositions in lily bulb changed during the baking process. Baked bulbs exhibited a higher cancer risk than raw bulbs. Local adults had low carcinogenic risks from consuming lily bulbs. This study fills the knowledge gap about PAH pollution and the related health risks of PAHs in the Lanzhou lily.

Chemical graph theory provides a mathematical framework for representing molecular structures as graphs, where atoms correspond to vertices and chemical bonds to edges. This approach enables the use of molecular descriptors to extract reliable structural information and model physicochemical properties. In this study, we investigate the use of recently introduced degree-based molecular descriptors including Euler Sombor, elliptic Sombor, reverse Sombor, reverse elliptic Sombor, reverse Euler Sombor, Lanzhou, and ad-hoc Lanzhou indices to model key properties of polychlorinated biphenyls (PCBs). Experimentally reported properties such as melting point, relative retention time, octanol-water partition coefficient, enthalpy of formation, and Henry’s law constant were analyzed. Quantitative structure–property relationship models were developed using linear, polynomial, and ridge regression techniques. The predictive performance of these models was evaluated through comparison of actual and predicted values, cross-validation, and bootstrapping. Results indicate that the selected descriptors, particularly the elliptic Sombor and reverse Euler Sombor indices, exhibit strong correlations with PCB properties, demonstrating their utility in predicting physicochemical behavior. These models hold potential for applications in chemical ecology, environmental risk assessment, and computational molecular design. Graphical abstract