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Concerns over the ecological impacts of urban road runoff have increased, partly due to recent research into the harmful impacts of tire particles and their chemical leachates. This study aimed to help the community of researchers, regulators and policy advisers in scoping out the priority areas for further study. To improve our understanding of these issues an interdisciplinary, international network consisting of experts (United Kingdom, Norway, United States, Australia, South Korea, Finland, Austria, China and Canada) was formed. We synthesised the current state of the knowledge and highlighted priority research areas for tire particles (in their different forms) and their leachates. Ten priority research questions with high importance were identified under four themes (environmental presence and detection; chemicals of concern; biotic impacts; mitigation and regulation). The priority research questions include the importance of increasing the understanding of the fate and transport of these contaminants; better alignment of toxicity studies; obtaining the holistic understanding of the impacts; and risks they pose across different ecosystem services. These issues have to be addressed globally for a sustainable solution. We highlight how the establishment of the intergovernmental science-policy panel on chemicals, waste, and pollution prevention could further address these issues on a global level through coordinated knowledge transfer of car tire research and regulation. We hope that the outputs from this research paper will reduce scientific uncertainty in assessing and managing environmental risks from TP and their leachates and aid any potential future policy and regulatory development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Accurate prediction of micro-p K a values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-p K a prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 p K a units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-p K a prediction models. QupKake represents a significant advancement in micro-p K a prediction, offering a powerful tool for various applications in chemistry and beyond.

Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots-the formalization of cclib's intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)

We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semiempirical methods are suitable priors for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data with small deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from the limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling and propose an alternative to the widely used "experimental torsion and knowledge distance geometry" (ETKDG) method using quantum torsion-derived distance geometry (QTDG) methods.

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. We show that using a self-termination method with a minimum Spearman's rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

While chemicals are vital to modern society through materials, agriculture, textiles, new technology, medicines, and consumer goods, their use is not without risks. Unfortunately, our resources seem inadequate to address the breadth of chemical challenges to the environment and human health. Therefore, it is important we use our intelligence and knowledge wisely to prepare for what lies ahead. The present study used a Delphi-style approach to horizon-scan future chemical threats that need to be considered in the setting of chemicals and environmental policy, which involved a multidisciplinary, multisectoral, and multinational panel of 25 scientists and practitioners (mainly from the United Kingdom, Europe, and other industrialized nations) in a three-stage process. Fifteen issues were shortlisted (from a nominated list of 48), considered by the panel to hold global relevance. The issues span from the need for new chemical manufacturing (including transitioning to non-fossil-fuel feedstocks); challenges from novel materials, food imports, landfills, and tire wear; and opportunities from artificial intelligence, greater data transparency, and the weight-of-evidence approach. The 15 issues can be divided into three classes: new perspectives on historic but insufficiently appreciated chemicals/issues, new or relatively new products and their associated industries, and thinking through approaches we can use to meet these challenges. Chemicals are one threat among many that influence the environment and human health, and interlinkages with wider issues such as climate change and how we mitigate these were clear in this exercise. The horizon scan highlights the value of thinking broadly and consulting widely, considering systems approaches to ensure that interventions appreciate synergies and avoid harmful trade-offs in other areas. We recommend further collaboration between researchers, industry, regulators, and policymakers to perform horizon scanning to inform policymaking, to develop our ability to meet these challenges, and especially to extend the approach to consider also concerns from countries with developing economies. Environ Toxicol Chem 2023;42:1212-1228. © 2023 Crown copyright and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland., (© 2023 Crown copyright and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland.)

Stable ground-state triplet π-conjugated copolymers have many interesting electronic and optoelectronic properties. However, the large number of potential monomer combinations makes it impractical to synthesize or even just use density functional theory (DFT) to calculate their triplet ground-state stability. Here, we present a genetic algorithm implementation that uses the semi-empirical GFN2-xTB to find ground-state triplet polymer candidates. We find more than 1400 polymer candidates with a triplet ground-state stability of up to 4 eV versus the singlet. Additionally, we explore the properties of the monomers of those candidates in order to understand the design rules which promote the formation of a stable ground-state triplet in π-conjugated polymers.

Given the importance of accurate polarizability calculations to many chemical applications, coupled with the need for efficiency when calculating the properties of sets of molecules or large oligomers, we present a benchmark study examining possible calculation methods for polarizable materials. We first investigate the accuracy of the additive model used in GFN2, a highly-efficient semi-empirical tight-binding method, and the D4 dispersion model, comparing its predicted additive polarizabilities to ωB97XD results for a subset of PubChemQC and a compiled benchmark set of molecules spanning polarizabilities from approximately 3 Å 3 to 600 Å 3 , with some compounds in the range of approximately 1200-1400 Å 3 . Although we find additive GFN2 polarizabilities, and thus D4, to have large errors with polarizability calculations on large conjugated oligomers, it would appear an empirical quadratic correction can largely remedy this. We also compare the accuracy of DFT polarizability calculations run using basis sets of varying size and level of augmentation, determining that a non-augmented basis set may be used for large, highly polarizable species in conjunction with a linear correction factor to achieve accuracy extremely close to that of aug-cc-pVTZ.

In the design of organic solar cells, there has been a need for materials with high power conversion efficiencies. Scharber's model is commonly used to predict efficiency; however, it exhibits poor performance with new non-fullerene acceptor (NFA) devices, since it was designed for fullerene-based devices. In this work, an empirical model is proposed that can be a more accurate alternative for NFA organic solar cells. Additionally, many screening studies use computationally expensive methods. A model based on using semiempirical simplified time-dependent density functional theory (sTD-DFT) as an alternative method can accelerate the calculations and yield a similar accuracy. The models presented in this paper, termed organic photovoltaic efficiency predictor (OPEP) models, have shown significantly lower errors than previous models, with OPEP/B3LYP yielding errors of 1.53% and OPEP/sTD-DFT of 1.55%. The proposed computational models can be used for the fast and accurate screening of new high-efficiency NFAs/donor pairs.

Materials optimization for organic solar cells (OSCs) is a highly active field, with many approaches using empirical experimental synthesis, computational brute force to screen a subset of chemical space, or generative machine learning methods that often require significant training sets. While these methods may find high-performing materials, they can be inefficient and time-consuming. Genetic algorithms (GAs) are an alternative approach, allowing for the "virtual synthesis" of molecules and a prediction of their "fitness" for some property, with new candidates suggested based on good characteristics of previously generated molecules. In this work, a GA is used to discover high-performing unfused non-fullerene acceptors (NFAs) based on an empirical prediction of power conversion efficiency (PCE) and provides design rules for future work. The electron-withdrawing/donating strength, as well as the sequence and symmetry, of those units are examined. The utilization of a GA over a brute-force approach resulted in speedups up to 1.8 × 10 12 . New types of units, not frequently seen in OSCs, are suggested, and in total 5426 NFAs are discovered with the GA. Of these, 1087 NFAs are predicted to have a PCE greater than 18%, which is roughly the current record efficiency. While the symmetry of the sequence showed no correlation with PCE, analysis of the sequence arrangement revealed that higher performance can be achieved with a donor core and acceptor end groups. Future NFA designs should consider this strategy as an alternative to the current A-D-A'-D-A architecture.

High-performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between the polarizability and dipole moment, indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polarizabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight-binding method for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. We also explored the underlying structure-function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.

Organic π-conjugated polymers with a triplet ground state have been the focus of recent research for their interesting and unique electronic properties, arising from the presence of the two unpaired electrons. These compounds are usually built from alternating electron-donating and electron-accepting monomer pairs which lower the HOMO-LUMO gap and yield a triplet state instead of the typical singlet ground state. In this paper, we use density functional theory calculations to explore the design rules that govern the creation of a ground-state triplet conjugated polymer and find that a small HOMO-LUMO gap in the singlet state is the best predictor for the existence of a triplet ground state, compared to previous use of a pro-quinoidal bonding character. This work can accelerate the discovery of new stable triplet materials by reducing the computational resources needed for electronic-state calculations and the number of potential candidates for synthesis.

Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of time required by commonly used methods such as density functional theory. Most current approaches to deep learning in quantum chemistry begin with geometric information from experimentally derived molecular structures or pre-calculated atom coordinates. These approaches have many useful applications, but they can be costly in time and computational resources. In this study, we demonstrate that accurate quantum chemical computations can be performed without geometric information by operating in the coordinate-free domain using deep learning on graph encodings. Coordinate-free methods rely only on molecular graphs, the connectivity of atoms and bonds, without atom coordinates or bond distances. We also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The structures of these graph-encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: what types of quantum chemical properties can be represented by local variable models? We find that Wave, a local variable model, accurately calculates the quantum chemical properties, while graph convolutional architectures require global variables. Furthermore, local variable Wave models outperform global variable graph convolution models on complex molecules with large, correlated systems.

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic devices, such as solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use toward screening new compounds with low internal reorganization energies. Our models have an overall root mean square error (RMSE) of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers can grow exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically motivated statistical model gives a mean absolute error of ∼4.8 J/mol·K or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often assumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations and advance our understanding of small molecule conformational entropy.

Flexible, biocompatible piezoelectric materials are of considerable research interest for a variety of applications, but many suffer from low response or high cost to manufacture. Herein, novel piezoelectric force and touch sensors based on self-assembled monolayers of oligopeptides are presented, which produce large piezoelectric voltage response and are easily manufactured without the need for electrical poling. While the devices generate modest piezoelectric charge constants (d 33 ) of up to 9.8 pC N -1 , they exhibit immense piezoelectric voltage constants (g 33 ) up to 2 V m N -1 . Furthermore, a flexible device prototype is demonstrated that produces open-circuit voltages of nearly 6 V under gentle bending motion. Improvements in peptide selection and device construction promise to further improve the already outstanding voltage response and open the door to numerous practical applications., (© 2021 Wiley-VCH GmbH.)

While many machine learning (ML) methods, particularly deep neural networks, have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. The methods were trained on the existing ANI-1 training set, calculated using the ωB97X/6-31G(d) single points at nonequilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and a new libmolgrid-based convolutional neural net, the Colorful CNN, show good performance.

The geometry of a molecule plays a significant role in determining its physical and chemical properties. Despite its importance, there are relatively few studies on ring puckering and conformations, often focused on small cycloalkanes, 5- and 6-membered carbohydrate rings, and specific macrocycle families. We lack a general understanding of the puckering preferences of medium-sized rings and macrocycles. To address this, we provide an extensive conformational analysis of a diverse set of rings. We used Cremer-Pople puckering coordinates to study the trends of the ring conformation across a set of 140 000 diverse small molecules, including small rings, macrocycles, and cyclic peptides. By standardizing using key atoms, we show that the ring conformations can be classified into relatively few conformational clusters, based on their canonical forms. The number of such canonical clusters increases slowly with ring size. Ring puckering motions, especially pseudo-rotations, are generally restricted and differ between clusters. More importantly, we propose models to map puckering preferences to torsion space, which allows us to understand the inter-related changes in torsion angles during pseudo-rotation and other puckering motions. Beyond ring puckers, our models also explain the change in substituent orientation upon puckering. We also present a novel knowledge-based sampling method using the puckering preferences and coupled substituent motion to generate ring conformations efficiently. In summary, this work provides an improved understanding of general ring puckering preferences, which will in turn accelerate the identification of low-energy ring conformations for applications from polymeric materials to drug binding.

A key challenge in conformer sampling is finding low-energy conformations with a small number of energy evaluations. We recently demonstrated the Bayesian Optimization Algorithm (BOA) is an effective method for finding the lowest energy conformation of a small molecule. Our approach balances between exploitation and exploration, and is more efficient than exhaustive or random search methods. Here, we extend strategies used on proteins and oligopeptides (e.g. Ramachandran plots of secondary structure) and study correlated torsions in small molecules. We use bivariate von Mises distributions to capture correlations, and use them to constrain the search space. We validate the performance of our new method, Bayesian Optimization with Knowledge-based Expected Improvement (BOKEI), on a dataset consisting of 533 diverse small molecules, using (i) a force field (MMFF94); and (ii) a semi-empirical method (GFN2), as the objective function. We compare the search performance of BOKEI, BOA with Expected Improvement (BOA-EI), and a genetic algorithm (GA), using a fixed number of energy evaluations. In more than 60% of the cases examined, BOKEI finds lower energy conformations than global optimization with BOA-EI or GA. More importantly, we find correlated torsions in up to 15% of small molecules in larger data sets, up to 8 times more often than previously reported. The BOKEI patterns not only describe steric clashes, but also reflect favorable intramolecular interactions such as hydrogen bonds and π-π stacking. Increasing our understanding of the conformational preferences of molecules will help improve our ability to find low energy conformers efficiently, which will have impact in a wide range of computational modeling applications.

We report a new methodology for the electromechanical characterization of organic monolayers based on the implementation of dual AC resonance tracking piezo force microscopy (DART-PFM) combined with a sweep of an applied DC field under a fixed AC field. This experimental design allows calibration of the electrostatic component of the tip response and enables the use of low spring constant levers in the measurement. Moreover, the technique is shown to determine both positive and negative piezo response. The successful decoupling of the electrostatic component from the mechanical response will enable more quantitative electromechanical characterization of molecular and biomaterials and should generate new design principles for soft bio-compatible piezoactive materials. To highlight the applicability, our new methodology was used to successfully characterize the piezoelectric coefficient ( d 33 ) of a variety of piezoactive materials, including self-assembled monolayers made of small molecules (dodecane thiol, mercaptoundecanoic acid) or macromolecules (peptides, peptoids), as well as a variety of inorganic materials, including lead zirconate titanate [PZT], quartz, and periodically poled lithium niobate [PPLN]. Due to high differential capacitance, the soft organic monolayers demonstrated exceedingly large electromechanical response (as high as 250 pm V -1 ) but smaller d 33 piezocoefficients. Finally, we find that the capacitive electrostatic response of the organic monolayers studied are significantly larger than conventional inorganic piezoelectric materials ( e.g. , PZT, PPLN, quartz), suggesting organic electromechanical materials applications can successfully draw from both piezo and electrostatic responses., Competing Interests: The authors declare no competing financial interests., (This journal is © The Royal Society of Chemistry.)

Rapidly predicting an accurate three dimensional geometry of a molecule is a crucial task for cheminformatics and across a wide range of molecular modeling. Consequently, developing a fast, accurate, and open implementation of structure prediction is necessary for reproducible cheminformatics research. We introduce a fragment-based coordinate generation implementation for Open Babel, a widely-used open source toolkit for cheminformatics. The new implementation improves speed and stereochemical accuracy, while retaining or improving accuracy of bond lengths, bond angles, and dihedral torsions. Input molecules are broken into fragments by cutting at rotatable bonds. The coordinates of fragments are set according to a fragment library, prepared from open crystallographic databases. Since the coordinates of multiple atoms are decided at once, coordinate prediction is accelerated over the previous rules-based implementation in Open Babel, as well as the widely-used distance geometry methods in RDKit. This new implementation will be beneficial for a wide range of applications, including computational property prediction in polymers, molecular materials and drug design.

Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method called the Bayesian optimization algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation, and torsion fingerprint deviation are used to quantify the performance of the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates [Formula: see text] (median) conformers in its search, while BOA only requires [Formula: see text] energy evaluations to find top candidates. Despite using evaluating fewer conformers, 20-40% of the time BOA finds lower-energy conformations than a systematic Confab search for molecules with four or more rotatable bonds.

Gas-phase electric properties of molecules can be computed routinely using wave function methods or density functional theory (DFT). However, these methods remain computationally expensive for high-throughput screening of the vast chemical space of virtual compounds. Therefore, empirical force fields are a more practical choice in many cases, particularly since force field methods allow one to routinely predict the physicochemical properties in the condensed phases. This work presents Drude polarizable models, to increase the physical realism in empirical force fields, where the core particle is treated as a point charge and the Drude particle is treated either as a 1 s-Gaussian or a ns-Slater ( n = 1, 2, 3) charge density. Systematic parametrization to large high-quality quantum chemistry data obtained from the open access Alexandria Library ( https://doi.org/10.5281/zenodo.1004711 ) ensures the transferability of these parameters. The dipole moments and isotropic polarizabilities of the isolated molecules predicted by the proposed Drude models are in agreement with experiment with accuracy similar to DFT calculations at the B3LYP/aug-cc-pVTZ level of theory. The results show that the inclusion of explicit polarization into the models reduces the root-mean-square deviation with respect to DFT calculations of the predicted dipole moments of 152 dimers and clusters by more than 50%. Finally, we show that the accuracy of the electrostatic interaction energy of the water dimers can be improved systematically by the introduction of polarizable smeared charges as a model for charge penetration.

Neutrophil surveillance is central to nanoparticle clearance. Silver nanoparticles (AgNP) have numerous uses, however conflicting evidence exists as to their impact on neutrophils and whether they trigger damaging inflammation. Neutrophil's importance in innate defence and regulating immune networks mean it's essential we understand AgNP's impact on neutrophil function. Human neutrophil viability following AgNP or Ag Bulk treatment was analysed by flow cytometry and AnV/PI staining. Whilst AgNP exposure did not increase the total number of apoptotic neutrophils, the number of late apoptotic neutrophils was increased, suggesting AgNP increase transit through apoptosis. Mature (CD16 bright /CD62L bright ), immature (CD16 dim /CD62L bright ) and apoptotic (CD16 dim /CD62L dim ) neutrophil populations were evident within isolated neutrophil preparations. AgNP exposure significantly reduced CD62L staining of CD16 bright /CD62L bright neutrophils, and increased CD16 staining of CD16 dim /CD62L bright populations, suggesting AgNPs trigger neutrophil activation and maturation, respectively. AgNP exposure dramatically increased IL-8, yet not classical pro-inflammatory cytokine release, suggesting AgNP triggers neutrophil activation, without pro-inflammation or damaging, necrotic cell death. For the first time, we show AgNPs differentially affect distinct sub-populations of circulating human neutrophils; activating mature neutrophils with the emergence of CD16 bright /CD62L dim neutrophils. This may stimulate particle clearance without harmful inflammation, challenging previous assumptions that silver nanomaterials induce neutrophil toxicity and damaging inflammatory responses.

Many biomaterials are piezoelectric (i.e., mechanically deform under an applied electric field); however, the molecular origin of this phenomenon remains unclear. In the case of protein-based scaffolds, one possibility involves flexible response of local folding motifs to the applied field. Here, we test this hypothesis by examining the piezoresponse in a series of helical peptide-based oligomers. Control over folding propensity is exerted through systematic variation in both side-chain sequence and backbone composition. Piezoresponse is quantified by piezo-force microscopy on polar self-assembled monolayers. The results indicate backbone rigidity is an important determinant in peptide electromechanical responsiveness.

To investigate the sequence effect on donor-acceptor conjugated oligomers and polymers, the trimeric isomers PBP and BPP, comprising dialkoxy phenylene vinylene (P), benzothiadiazole vinylene (B), and alkyl endgroups with terminal olefins, are synthesized. Sequence effects are evident in the optical/electrochemical properties and thermal properties. Absorption maxima for PBP and BPP differ by 41 nm and the electrochemical band gaps by 0.1 V. The molar emission intensity is five times greater in PBP than BPP. Both trimers are crystalline and the melting points differ by 17 °C. The PBP and BPP trimers are used as macromonomers in an acyclic diene metathesis polymerization to give PolyPBP and PolyBPP. The optical and electrochemical properties are similar to those of their trimer precursors-sequence effects are still evident. These results suggest that sequence is a tunable variable for electronic materials and that the polymerization of oligomeric sequences is a useful approach to introducing sequence into polymers., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

This paper aimed to clarify whether maternal inhalation of engineered nanoparticles (NP) may constitute a hazard to pregnancy and fetal development, primarily based on experimental animal studies of NP and air pollution particles. Overall, it is plausible that NP may translocate from the respiratory tract to the placenta and fetus, but also that adverse effects may occur secondarily to maternal inflammatory responses. The limited database describes several organ systems in the offspring to be potentially sensitive to maternal inhalation of particles, but large uncertainties exist about the implications for embryo-fetal development and health later in life. Clearly, the potential for hazard remains to be characterized. Considering the increased production and application of nanomaterials and related consumer products a testing strategy for NP should be established. Due to large gaps in data, significant amounts of groundwork are warranted for a testing strategy to be established on a sound scientific basis., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Phthalate exposure induces germ cell effects in the fetal rat testis. Although experimental models have shown that the human fetal testis is insensitive to the steroidogenic effects of phthalates, the effects on germ cells have been less explored., Objectives: We sought to identify the effects of phthalate exposure on human fetal germ cells in a dynamic model and to establish whether the rat is an appropriate model for investigating such effects., Methods: We used immunohistochemistry, immunofluorescence, and quantitative real-time polymerase chain reaction to examine Sertoli and germ cell markers on rat testes and human fetal testis xenografts after exposure to vehicle or di(n-butyl) phthalate (DBP). Our study included analysis of germ cell differentiation markers, proliferation markers, and cell adhesion proteins., Results: In both rat and human fetal testes, DBP exposure induced similar germ cell effects, namely, germ cell loss (predominantly undifferentiated), induction of multinucleated gonocytes (MNGs), and aggregation of differentiated germ cells, although the latter occurred rarely in the human testes. The mechanism for germ cell aggregation and MNG induction appears to be loss of Sertoli cell-germ cell membrane adhesion, probably due to Sertoli cell microfilament redistribution., Conclusions: Our findings provide the first comparison of DBP effects on germ cell number, differentiation, and aggregation in human testis xenografts and in vivo in rats. We observed comparable effects on germ cells in both species, but the effects in the human were muted compared with those in the rat. Nevertheless, phthalate effects on germ cells have potential implications for the next generation, which merits further study. Our results indicate that the rat is a human-relevant model in which to explore the mechanisms for germ cell effects.

Organic piezoelectric materials are promising targets in applications such as energy harvesting or mechanical sensors and actuators. In a recent paper (Werling, K. A.; et al. J. Phys. Chem. Lett. 2013, 4, 1365-1370), we have shown that hydrogen bonding gives rise to a significant piezoelectric response. In this article, we aim to find organic hydrogen bonded systems with increased piezo-response by investigating different hydrogen bonding motifs and by tailoring the hydrogen bond strength via functionalization. The largest piezo-coefficient of 23 pm/V is found for the nitrobenzene-aniline dimer. We develop a simple, yet surprisingly accurate rationale to predict piezo-coefficients based on the zero-field compliance matrix and dipole derivatives. This rationale increases the speed of first-principles piezo-coefficient calculations by an order of magnitude. At the same time, it suggests how to understand and further increase the piezo-response. Our rationale also explains the remarkably large piezo-response of 150 pm/V and more for another class of systems, the "molecular springs" (Marvin, C.; et al. J. Phys. Chem. C 2013, 117, 16783-16790.).

There has been increasing interest in rational, computationally driven design methods for materials, including organic photovoltaics (OPVs). Our approach focuses on a screening "pipeline", using a genetic algorithm for first stage screening and multiple filtering stages for further refinement. An important step forward is to expand our diversity of candidate compounds, including both synthetic and property-based measures of diversity. For example, top monomer pairs from our screening are all donor-donor (D-D) combinations, in contrast with the typical donor-acceptor (D-A) motif used in organic photovoltaics. We also find a strong "sequence effect", in which the average HOMO-LUMO gap of tetramers changes by ∼0.2 eV as a function of monomer sequence (e.g., ABBA versus BAAB); this has rarely been explored in conjugated polymers. Beyond such optoelectronic optimization, we discuss other properties needed for high-efficiency organic solar cells, and applications of screening methods to other areas, including non-fullerene n-type materials, tandem cells, and improving charge and exciton transport.

The piezoelectric properties of 2-methyl-4-nitroaniline crystals were explored qualitatively and quantitatively using an electrostatically embedded many-body (EE-MB) expansion scheme for the correlation energies of a system of monomers within the crystal. The results demonstrate that hydrogen bonding is an inherently piezoelectric interaction, deforming in response to the electrostatic environment. We obtain piezo-coefficients in excellent agreement with the experimental values. This approach reduces computational cost and reproduces the total resolution of the identity (RI)-Møller-Plesset second-order perturbation theory (RI-MP2) energy for the system to within 1.3 × 10(-5)%. Furthermore, the results suggest novel ways to self-assemble piezoelectric solids and suggest that accurate treatment of hydrogen bonds requires precise electrostatic evaluation. Considering the ubiquity of hydrogen bonds across chemistry, materials, and biology, a new electromechanical view of these interactions is required.

Effects on the liver C3A cell line treated with a panel of engineered nanomaterials (NMs) consisting of two zinc oxide particles (ZnO; coated 100 nm and uncoated 130 nm), two multi-walled carbon nanotubes (MWCNTs), one silver (Ag < 20 nm), one 7 nm anatase, two rutile TiO2 nanoparticles (10 and 94 nm) and two derivatives with positive and negative covalent functionalisation of the 10 nm rutile were evaluated. The silver particles elicited the greatest level of cytotoxicity (24 h LC50 - 2 µg/cm(2)). The silver was followed by the uncoated ZnO (24 h LC50 - 7.5 µg/cm(2)) and coated ZnO (24 h LC50 - 15 µg/cm(2)) particles with respect to cytotoxicity. The ZnO NMs were found to be about 50-60% soluble which could account for their toxicity. By contrast, the Ag was <1% soluble. The LC50 was not attained in the presence of any of the other engineered NMs (up to 80 µg/cm(2)). All NMs significantly increased IL-8 production. Meanwhile, no significant change in TNF-α, IL-6 or CRP was detected. Urea and albumin production were measured as indicators of hepatic function. These markers were only altered by the coated and uncoated ZnO, which significantly decreased albumin production.

Here, we report an unusual oxidation-induced photoluminescence (PL) turn-on response of a poly(3-alkoxythiophene), poly(3-{2-[2-(2-ethoxyethoxy)ethoxy]ethoxy}thiophene) (PEEEET). PEEEET shows a significantly red-shifted absorption spectrum compared to polyalkylthiophenes and is almost nonfluorescent (quantum yield ≪ 1%) in its pristine state. The introduction of sulfonyl defects along the polymer backbone by the oxidation of PEEEET with meta-chloroperbenzoic acid (m-CPBA) increased the emission quantum yield with the intensity increasing with the degree of oxidation. Molecular modeling data indicated that the oxidation-induced PL increase cannot be explained by the nature of monomer units and radiative rate changes. We attributed the enhanced fluorescence to the reduced nonradiative rate caused by the increased band gap, according to the energy gap law, which is consistent with the observed blue shifts in absorption and PL spectra accompanied by the PL increase.

The effect of morphology on charge transport in organic photovoltaics is assessed using Monte Carlo. In isotopic two-phase morphologies, increasing the domain size from 6.3 to 18.3 nm improves the fill factor by 11.6%, a result of decreased tortuosity and relaxation of Coulombic barriers. Additionally, when small aggregates of electron acceptors are interdispersed into the electron donor phase, charged defects form in the system, reducing fill factors by 23.3% on average, compared with systems without aggregates. In contrast, systems with idealized connectivity show a 3.31% decrease in fill factor when domain size was increased from 4 to 64 nm. We attribute this to a decreased rate of exciton separation at donor-acceptor interfaces. Finally, we notice that the presence of Coulomb interactions increases device performance as devices become smaller. The results suggest that for commonly found isotropic morphologies the Coulomb interactions between charge carriers dominates exciton separation effects.

Electron delocalization of new mixed-valent (MV) systems with the aid of lateral metal chelation is reported. 2,2'-Bipyridine (bpy) derivatives with one or two appended di-p-anisylamino groups on the 5,5'-positions and a coordinated [Ru(bpy)(2)] (bpy = 2,2'-bipyridine), [Re(CO)(3)Cl], or [Ir(ppy)(2)] (ppy = 2-phenylpyridine) component were prepared. The single-crystal molecular structure of the bis-amine ligand without metal chelation is presented. The electronic properties of these complexes were studied and compared by electrochemical and spectroscopic techniques and DFT/TDDFT calculations. Compounds with two di-p-anisylamino groups were oxidized by a chemical or electrochemical method and monitored by near-infrared (NIR) absorption spectral changes. Marcus-Hush analysis of the resulting intervalence charge-transfer transitions indicated that electron coupling of these mixed-valent systems is enhanced by metal chelation and that the iridium complex has the largest coupling. TDDFT calculations were employed to interpret the NIR transitions of these MV systems., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types., Results: The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here., Conclusions: Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net.

Background: Following exposure via inhalation, intratracheal instillation or ingestion some nanomaterials (NM) have been shown to translocate to the liver. Since oxidative stress has been implicated as a possible mechanism for NM toxicity this study aimed to investigate the effects of various materials (five titanium dioxide (TiO2), two zinc oxide (ZnO), two multi-walled carbon nanotubes (MWCNT) and one silver (Ag) NM) on oxidative responses of C3A cell line as a model for potential detrimental properties of nanomaterials on the liver., Results: We noted a dose dependant decrease in the cellular glutathione content following exposure of the C3A cells to Ag, the ZnO and the MWCNTs. Intracellular ROS levels were also measured and shown to increase significantly following exposure of the C3A to the low toxicity NMs (MWCNT and TiO(2)). The antioxidant Trolox in part prevented the detrimental effect of NMs on cell viability, and decreased the NM induced IL8 production after exposure to all but the Ag particulate. Following 4 hr exposure of the C3A cells to sub-lethal levels of the NMs, the largest amount of DNA damage was induced by two of the TiO(2) samples (7 nm and the positively charged 10 nm particles)., Conclusions: All ten NMs exhibited effects on the hepatocyte cell line that were at least in part ROS/oxidative stress mediated. These effects included mild genotoxicity and IL8 production for all NM except the Ag possibly due to its highly cytotoxic nature.

Background: The Blue Obelisk movement was established in 2005 as a response to the lack of Open Data, Open Standards and Open Source (ODOSOS) in chemistry. It aims to make it easier to carry out chemistry research by promoting interoperability between chemistry software, encouraging cooperation between Open Source developers, and developing community resources and Open Standards., Results: This contribution looks back on the work carried out by the Blue Obelisk in the past 5 years and surveys progress and remaining challenges in the areas of Open Data, Open Standards, and Open Source in chemistry., Conclusions: We show that the Blue Obelisk has been very successful in bringing together researchers and developers with common interests in ODOSOS, leading to development of many useful resources freely available to the chemistry community.

Background: A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats., Results: We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion., Conclusions: Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.

Environmental factors are implicated in increased incidence of human testicular germ-cell cancer (TGCC). TGCC has foetal origins and may be one component of a testicular dysgenesis syndrome (TDS). Certain phthalates induce TDS in rats, including effects on foetal germ cells (GC). As humans are widely exposed to phthalates, study of the effects of phthalates on foetal rat GC could provide an insight into the vulnerability of foetal GC to disruption by environmental factors, and thus to origins of TGCC. This study has therefore characterized foetal GC development in rats after in utero exposure to di(n-butyl) phthalate (DBP) with emphasis on GC numbers/proliferation, differentiation and time course for inducing effects. Pregnant rats were treated orally from embryonic day 13.5 (e13.5) with 500 mg/kg/day DBP for varying periods. GC number, proliferation, apoptosis, differentiation (loss of OCT4, DMRT1 expression, DMRT1 re-expression, GC migration) and aggregation were evaluated at various foetal and postnatal ages. DBP exposure reduced foetal GC number by ∼60% by e15.5 and prolonged GC proliferation, OCT4 and DMRT1 immunoexpression; these effects were induced in the period immediately after testis differentiation (e13.5-e15.5). In contrast, DBP-induced GC aggregation stemmed from late gestation effects (beyond e19.5). Foetal DBP exposure delayed postnatal resumption of GC proliferation, leading to bigger deficits in numbers, and delayed re-expression of DMRT1 and radial GC migration. Therefore, DBP differentially affects foetal GC in rats according to stage of gestation, effects that may be relevant to the human because of their nature (OCT4, DMRT1 effects) or because similar effects are demonstrable in vitro on human foetal testes (GC number). Identification of the mechanisms underlying these effects could give a new insight into environment-sensitive mechanisms in early foetal GC development that could potentially be relevant to TGCC origins., (© 2011 The Authors. International Journal of Andrology © 2011 European Academy of Andrology.)

Background: Many computational chemistry analyses require the generation of conformers, either on-the-fly, or in advance. We present Confab, an open source command-line application for the systematic generation of low-energy conformers according to a diversity criterion., Results: Confab generates conformations using the 'torsion driving approach' which involves iterating systematically through a set of allowed torsion angles for each rotatable bond. Energy is assessed using the MMFF94 forcefield. Diversity is measured using the heavy-atom root-mean-square deviation (RMSD) relative to conformers already stored. We investigated the recovery of crystal structures for a dataset of 1000 ligands from the Protein Data Bank with fewer than 1 million conformations. Confab can recover 97% of the molecules to within 1.5 Å at a diversity level of 1.5 Å and an energy cutoff of 50 kcal/mol., Conclusions: Confab is available from http://confab.googlecode.com.

This critical review of the available human health safety data, relating to carbon nanotubes (CNTs), was conducted in order to assess the risks associated with CNT exposure. Determining the toxicity related to CNT exploitation is of great relevance and importance due to the increased potential for human exposure to CNTs within occupational, environmental and consumer settings. When this information is combined with knowledge on the likely exposure levels of humans to CNTs, it will enable risk assessments to be conducted to assess the risks posed to human health. CNTs are a diverse group of materials and vary with regards to their wall number (single and multi-walled CNTs are evident), length, composition, and surface chemistry. The attributes of CNTs that were identified as being most likely to drive the observed toxicity have been considered, and include CNT length, metal content, tendency to aggregate/agglomerate and surface chemistry. Of particular importance, is the contribution of the fibre paradigm to CNT toxicity, whereby the length of CNTs appears to be critical to their toxic potential. Mechanistic processes that are critical to CNT toxicity will also be discussed, with the findings insinuating that CNTs can exert an oxidative response that stimulates inflammatory, genotoxic and cytotoxic consequences. Consequently, it may transpire that a common mechanism is responsible for driving CNT toxicity, despite the fact that CNTs are a diverse population of materials. The similarity of the structure of CNTs to that of asbestos has prompted concern surrounding the exposure of humans, and so the applicability of the fibre paradigm to CNTs will be evaluated. It is also necessary to determine the systemic availability of CNTs following exposure, to determine where potential targets of toxicity are, and to thereby direct in vitro investigations within the most appropriate target cells. CNTs are therefore a group of materials whose useful exploitable properties prompts their increased production and utilization within diverse applications, so that ensuring their safety is of vital importance.

We have shown previously that deficient androgen action within a masculinization programming window (MPW; e15.5-e18.5 in rats) is important in the origin of male reproductive disorders and in programming male reproductive organ size, but that androgen action postnatally may be important to achieve this size. To further investigate importance of the MPW, we used two rat models, in which foetal androgen production or action was impaired during the MPW by exposing in utero to either di(n-butyl) phthalate (DBP) or to flutamide. Reduced anogenital distance (AGD) was used as a monitor of androgen production/action during the MPW. Offspring were evaluated in early puberty (Pnd25) to establish if reproductive organ size was altered. The testes, penis, ventral prostate (VP) and seminal vesicles (SV) were weighed and penis length measured. Both DBP and flutamide exposure in the MPW significantly reduced penis, VP and SV size along with AGD at Pnd25; AGD and organ size were highly correlated. In DBP-, but not flutamide-, exposed animals, testis weight was also reduced and correlated with AGD. Intratesticular testosterone was also measured in control and DBP-exposed males during (e17.5) or after (e21.5) the MPW and related to AGD at e21.5. To evaluate the importance of postnatal androgen action in reproductive organ growth, the effect of combinations of prenatal and postnatal maternal treatments on AGD and penis size at Pnd25 was evaluated. In prenatally DBP-exposed animals, further postnatal exposure to either DBP or flutamide significantly reduced AGD and penis size in comparison with prenatal DBP exposure alone. In comparison, rats exposed postnatally to testosterone propionate after prenatal vehicle-exposure showed considerable increase in these parameters vs. controls. In conclusion, we show that the size of all male reproductive organs is programmed by androgen exposure in the MPW, but that growth towards this size is dependent on androgen action postnatally.

This review provides a comprehensive critical review of the available literature purporting to assess the toxicity of carbon fullerenes. This is required as prior to the widespread utilization and production of fullerenes, it is necessary to consider the implications of exposure for human health. Traditionally, fullerenes are formed from 60 carbon atoms, arranged in a spherical cage-like structure. However, manipulation of surface chemistry and molecular makeup has created a diverse population of fullerenes, which exhibit drastically different behaviors. The cellular processes that underlie observed fullerene toxicity will be discussed and include oxidative, genotoxic, and cytotoxic responses. The antioxidant/cytoprotective properties of fullerenes (and the attributes responsible for driving these phenomena) have been considered and encourage their utilization within the treatment of oxidant-mediated disease. A number of studies have focused on improving the water solubility of fullerenes in order to enable their exploitation within biological systems. Manipulating fullerene water solubility has included the use of surface modifications, solvents, extended stirring, and mechanical processes. However, the ability of these processes to also impact on fullerene toxicity requires assessment, especially when considering the use of solvents, which particularly appear to enhance fullerene toxicity. A number of the discussed investigations were not conducted to reveal if fullerene behavior was due to their nanoparticle dimensions but instead addressed the biocompatibility and toxicity of fullerenes. The hazards to human health, associated with fullerene exposure, are uncertain at this time, and further investigations are required to decipher such effects before an effective risk assessment can be conducted.

This review focuses on outlining the toxicity of titanium dioxide (TiO(2)) particulates in vitro and in vivo, in order to understand their ability to detrimentally impact on human health. Evaluating the hazards associated with TiO(2 )particles is vital as it enables risk assessments to be conducted, by combining this information with knowledge on the likely exposure levels of humans. This review has concentrated on the toxicity of TiO(2), due to the fact that the greatest number of studies by far have evaluated the toxicity of TiO(2), in comparison to other metal oxide particulates. This derives from historical reasons (whereby the size dependency of particulate toxicity was first realised for TiO(2)) and due to its widespread application within consumer products (such as sunscreens). The pulmonary and dermal hazards of TiO(2 )have been a particular focus of the available studies, due to the past use of TiO(2 )as a (negative) control when assessing the pulmonary toxicity of particulates, and due to its incorporation within consumer products such as sunscreens. Mechanistic processes that are critical to TiO(2 )particulate toxicity will also be discussed and it is apparent that, in the main, the oxidant driven inflammatory, genotoxic and cytotoxic consequences associated with TiO(2 )exposure, are inherently linked, and are evident both in vivo and in vitro. The attributes of TiO(2 )that have been identified as being most likely to drive the observed toxicity include particle size (and therefore surface area), crystallinity (and photocatalytic activity), surface chemistry, and particle aggregation/agglomeration tendency. The experimental set up also influences toxicological outcomes, so that the species (or model) used, route of exposure, experiment duration, particle concentration and light conditions are all able to influence the findings of investigations. In addition, the applicability of the observed findings for particular TiO(2 )forms, to TiO(2 )particulates in general, requires consideration. At this time it is inappropriate to consider the findings for one TiO(2 )form as being representative for TiO(2 )particulates as a whole, due to the vast number of available TiO(2 )particulate forms and large variety of potential tissue and cell targets that may be affected by exposure. Thus emphasising that the physicochemical characteristics are fundamental to their toxicity.

Common male reproductive abnormalities including cryptorchidism, hypospadias, and low sperm counts may comprise a testicular dysgenesis syndrome (TDS), resulting from fetal testis dysfunction during a critical developmental period involving reduced androgen production/action. The recent increase in TDS prevalence suggests environmental/lifestyle factors may be etiologically important. The developing fetus is exposed to multimodal challenges, and we hypothesized that exposure to a combination of factors rather than single agents may be important in the pathogenesis of TDS. We experimentally induced fetal testis dysfunction in rats via treatment of pregnant females daily from embryonic day (e) 13.5 to e21.5 with vehicle, 100 or 500 mg/kg . d dibutyl phthalate (DBP), 0.1 mg/kg . d dexamethasone (Dex), or a combination of DBP + Dex. In adulthood, penile length/normality, testis weight/descent, prostate weight, and plasma testosterone levels were measured plus anogenital distance (AGD) as a measure of androgen action within the masculinization programming window. Intratesticular testosterone and steroidogenic enzyme gene expression were measured in fetal testes at e17.5. High-dose DBP reduced fetal intratesticular testosterone and steroidogenic gene expression; induced mild hypospadias (31%) and cryptorchidism (53%); and reduced penile length, AGD, and testis and prostate weight in adulthood. Dex alone had no effect except to reduce birth weight but amplified the adverse effects of 500 mg/kg . d DBP and exacerbated the effects of 100 mg/kg . d DBP. All adverse effects were highly correlated to AGD, emphasizing the etiological importance of the masculinization programming window. These findings suggest that exposure to common environmental chemicals in combination with, for example, maternal stress, may increase the risk of common male reproductive abnormalities, with implications for human populations.