A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability

In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner. Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and sustainable innovation and its regulation. This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or timeand environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs’ data generation, curation, representation and use within machine learning or other advanced data-driven models to ultimately enhance toxicity assessment. Finally, the set up of dedicated databases as well as the development of grouping and read-across strategies based on the mode of action of ENMs using omics methods are identified as emerging methodologies for safety assessment and reduction of animal testing

Specific features of defect structure and dynamics in the cylinder phase of block copolymers

We present a systematic study of defects in thin films of cylinder-forming block copolymers upon long-term thermal or solvent annealing. In particular, we consider in detail the peculiarities of both classical and specific topological defects, and conclude that there is a strong "defect structure- chain mobility" relationship in block copolymers. In the systems studied, representative defect configurations provide connectivity of the minority phase in the form of dislocations with a closed cylinder end or classical disclinations with incorporated alternative, nonbulk structures with planar symmetry. In solvent-annealed films with enhanced chain mobility, the neck defects (bridges between parallel cylinders) were observed. This type of nonsingular defect has not been identified in block copolymer systems before. We argue that topological arguments and 2D defect representation, sufficient for lamellar systems, are not sufficient to determine the stability and mobility of defects in the cylindrical phase. In-situ scanning force microscopy measurements are compared with the simulations based on the dynamic self-consistent mean field theory. The close match between experimental measurements and simulation results suggests that the lateral defect motion is diffusion-driven. In addition, 3D simulations demonstrated that the bottom (wetting) layer is only weakly involved into the structure ordering at the free surface. Finally, the morphological evolution is considered with the focus on the motion and interaction of the representative defect configuration. © 2008 American Chemical Society

A preconditioned Krylov subspace method for the solution of least squares problems

We present an iterative method of preconditioned Krylov type for the solution of large least squares problems. We prove that the method is robust and investigate its rate of convergence. For an important application, originating from seismic inverse scattering, we derive a suitable preconditioner using asymptotic theory. Numerical experiments are used to compare the method with other iterative methods. It appears that the preconditioned Krylov method can be much more efficient than CG applied to the normal equations. Keywords: least squares problem, underdetermined problem, Krylov subspace method, preconditioning, inverse scattering. AMS Subject Classification: 65F10, 65F20, 81U40, 86A22 1 Introduction In this paper we consider the solution of underdetermined least squares problems by iterative methods. Section 2 contains the description of the problem. In Section 3 we give a short survey of existing iterative methods for least squares problems, such as SIRT, ART, and CG. After this..

Unfolding the prospects of computational (bio)materials modelling

In this perspective communication, we briefly sketch the current state of computational (bio)materials research and discuss possible solutions for the four challenges that have been increasingly identified within this community: i) the desire to develop a unified framework for testing the consistency of implementation and of physical accuracy for newly developed methodologies, ii) the selection of a standard format that can deal with the diversity of simulation data and at the same time simplifies data storage, data exchange and data reproduction, iii) how to deal with the generation, storage and analysis of massive data, and iv) the benefits of efficient ’core’ engines. Expressed viewpoints are the result of discussions between computational stakeholders during a Lorentz Center workshop with the prosaic title Workshop on Multi-scale Modelling and are aimed at: i) improving validation, reporting and reproducibility of computational results, ii) improving data migration between simulation packages and with analysis tools, iii) popularising the use of coarse-grained and multi-scale computational tools among non-experts, opening up these modern computational developments to an extended user community.Ministerio de Ciencia, Innovacion y UniversidadesGeneralitat de CatalunyaSwiss National Science FoundationNational Science Center of PolandItalian National Projec

Unfolding the prospects of computational (bio)materials modeling

In this perspective communication, we briefly sketch the current state of computational (bio)material research and discuss possible solutions for the four challenges that have been increasingly identified within this community: (i) the desire to develop a unified framework for testing the consistency of implementation and physical accuracy for newly developed methodologies, (ii) the selection of a standard format that can deal with the diversity of simulation data and at the same time simplifies data storage, data exchange, and data reproduction, (iii) how to deal with the generation, storage, and analysis of massive data, and (iv) the benefits of efficient "core" engines. Expressed viewpoints are the result of discussions between computational stakeholders during a Lorentz center workshop with the prosaic title Workshop on Multi-scale Modeling and are aimed at (i) improving validation, reporting and reproducibility of computational results, (ii) improving data migration between simulation packages and with analysis tools, (iii) popularizing the use of coarse-grained and multi-scale computational tools among non-experts and opening up these modern computational developments to an extended user community

Contrasting Modes of Self-Assembly and Hydrogen-Bonding Heterogeneity in Chlorosomes of Chlorobaculum tepidum

Chlorosome antennae form an interesting class of materials for studying the role of structural motifs and dynamics in nonadiabatic energy transfer. They perform robust and highly quantum-efficient transfer of excitonic energy while allowing for compositional variation and completely lacking the usual regulatory proteins. Here, we first cast the geometrical analysis for ideal tubular scaffolding models into a formal framework, to relate effective helical properties of the assembly structures to established characterization data for various types of chlorosomes. This analysis shows that helicity is uniquely defined for chlorosomes composed of bacteriochlorophyll (BChl) <i>d</i> and that three chiral angles are consistent with the nuclear magnetic resonance (NMR) and electron microscope data for BChl <i>c</i>, including two novel ones that are at variance with current interpretations of optical data based on perfect cylindrical symmetry. We use this information as a starting point for investigating dynamic and static heterogeneity at the molecular level by unconstrained molecular dynamics. We first identify a rotational degree of freedom, along the Mg–OH coordination bond, that alternates along the syn–anti stacks and underlies the (flexible) curvature on a larger scale. Because rotation directly relates to the formation or breaking of interstack hydrogen bonds of the O–H···OC structural motif along the syn–anti stacks, we analyzed the relative fractions of hydrogen-bonded and the nonbonded regions, forming stripe domains in otherwise spectroscopically homogeneous curved slabs. The ratios 7:3 for BChl <i>c</i> and 9:1 for BChl <i>d</i> for the two distinct structural components agree well with the signal intensities determined by NMR. In addition, rotation with curvature-independent formation of stripe domains offers a viable explanation for the localization and dispersion of exciton states over two fractions, as observed in single chlorosome fluorescence decay studies

Predicted Adsorption Affinity for Enteric Microbial Metabolites to Metal and Carbon Nanomaterials.

Ingested nanomaterials are exposed to many metabolites that are produced, modified, or regulated by members of the enteric microbiota. The adsorption of these metabolites potentially affects the identity, fate, and biodistribution of nanomaterials passing the gastrointestinal tract. Here, we explore these interactions using in silico methods, focusing on a concise overview of 170 unique enteric microbial metabolites which we compiled from the literature. First, we construct quantitative structure-activity relationship (QSAR) models to predict their adsorption affinity to 13 metal nanomaterials, 5 carbon nanotubes, and 1 fullerene. The models could be applied to predict log k values for 60 metabolites and were particularly applicable to 'phenolic, benzoyl and phenyl derivatives', 'tryptophan precursors and metabolites', 'short-chain fatty acids', and 'choline metabolites'. The correlations of these predictions to biological surface adsorption index descriptors indicated that hydrophobicity-driven interactions contribute most to the overall adsorption affinity, while hydrogen-bond interactions and polarity/polarizability-driven interactions differentiate the affinity to metal and carbon nanomaterials. Next, we use molecular dynamics (MD) simulations to obtain direct molecular information for a selection of vitamins that could not be assessed quantitatively using QSAR models. This showed how large and flexible metabolites can gain stability on the nanomaterial surface via conformational changes. Additionally, unconstrained MD simulations provided excellent support for the main interaction types identified by QSAR analysis. Combined, these results enable assessing the adsorption affinity for many enteric microbial metabolites quantitatively and support the qualitative assessment of an even larger set of complex and biologically relevant microbial metabolites to carbon and metal nanomaterials