Chiral nanostructures from metals and semiconductors attract wide interest as components for polarization-enabled optoelectronic devices. Similarly to other fields of nanotechnology, graphene-based materials can greatly enrich physical and chemical phenomena associated with optical and electronic properties of chiral nanostructures and facilitate their applications in biology as well as other areas. Here, we report that covalent attachment of l/d-cysteine moieties to the edges of graphene quantum dots (GQDs) leads to their helical buckling due to chiral interactions at the "crowded" edges. Circular dichroism (CD) spectra of the GQDs revealed bands at ca. 210-220 and 250-265 nm that changed their signs for different chirality of the cysteine edge ligands. The high-energy chiroptical peaks at 210-220 nm correspond to the hybridized molecular orbitals involving the chiral center of amino acids and atoms of graphene edges. Diverse experimental and modeling data, including density functional theory calculations of CD spectra with probabilistic distribution of GQD isomers, indicate that the band at 250-265 nm originates from the three-dimensional twisting of the graphene sheet and can be attributed to the chiral excitonic transitions. The positive and negative low-energy CD bands correspond to the left and right helicity of GQDs, respectively. Exposure of liver HepG2 cells to L/D-GQDs reveals their general biocompatibility and a noticeable difference in the toxicity of the stereoisomers. Molecular dynamics simulations demonstrated that d-GQDs have a stronger tendency to accumulate within the cellular membrane than L-GQDs. Emergence of nanoscale chirality in GQDs decorated with biomolecules is expected to be a general stereochemical phenomenon for flexible sheets of nanomaterials.

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Nanocomposites based on vinyl alcohol-containing polymers and nanostructured gold have been efficiently prepared by a UV photo-reduction process. The very fast process provided dispersed gold nanoparticles with average diameters ranging from 3 to 20 nm depending on the host polymer matrix and the irradiation time. Uniaxial drawing of the irradiated Au/polymer nanocomposites favours the anisotropic distribution of packed assemblies of gold particles, providing oriented films with polarization-dependent tunable optical properties. These pronounced dichroic properties suggest that the nanocomposite films could find potential applications as colour polarizing filters, radiation responsive polymeric objects and smart flexible films in packaging application

Motivation Understanding the mechanisms and structural mappings between molecules and pathway classes are critical for design of reaction predictors for synthesizing new molecules. This article studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compound participates. We apply a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier. In contrast to previously applied machine learning methods for this problem, our framework automatically extracts relevant shape features directly from input SMILES representations, which are atom-bond specifications of chemical structures composing the molecules. Results Our method is capable of correctly predicting the respective metabolic pathway class of 95.16% of tested compounds, whereas competing methods only achieve an accuracy of 84.92% or less. Furthermore, our framework extends to the task of classification of compounds having mixed membership in multiple pathway classes. Our prediction accuracy for this multi-label task is 97.61%. We analyze the relative importance of various global physicochemical features to the pathway class prediction problem and show that simple linear/logistic regression models can predict the values of these global features from the shape features extracted using our framework. Availability and implementation https://github.com/baranwa2/MetabolicPathwayPrediction. Supplementary information Supplementary data are available at Bioinformatics online.