Abstract: The bottom-up production of chiral gold nanomaterials holds great potential for the advancement of biosensing and nano-optics, among other applications. Reproducible preparations of colloidal nanomaterials with chiral morphology have been reported, using cosurfactants or chiral inducers such as thiolated amino acids. However, the underlying growth mechanisms for these nanomaterials remain insufficiently understood. We introduce herein a purposely devised chiral inducer, a cysteine modified with a hydrophobic chain, as a versatile chiral inducer. The amphiphilic and chiral features of this molecule provide control over the chiral morphology and the chiroptical signature of the obtained nanoparticles by simply varying the concentration of chiral inducer. These results are supported by circular dichroism and electromagnetic modeling as well as electron tomography to analyze structural evolution at the facet scale. Our observations suggest complex roles for the factors involved in chiral synthesis: the chemical nature of the chiral inducers and the influence of cosurfactants.

Abstract: Chemically synthesized metal nanoparticles with morphological chiral features are known to exhibit strong circular dichroism. However, we still lack understanding of the correlation between morphological and chiroptical features of plasmonic nanoparticles. To shed light on that question, single nanoparticle experiments are required. We performed photothermal circular dichroism measurements of single chiral and achiral gold nanoparticles and correlated the chiroptical response to the 3D morphology of the same nanoparticles retrieved by electron tomography. In contrast to an ensemble measurement, we show that individual particles within the ensemble display a broad distribution of strength and handedness of circular dichroism signals. Whereas obvious structural chiral features, such as helical wrinkles, translate into chiroptical ones, nanoparticles with less obvious chiral morphological features can also display strong circular dichroism signals. Interestingly, we find that even seemingly achiral nanoparticles can display large g-factors. The origin of this circular dichroism signal is discussed in terms of plasmonics and other potentially relevant factors.

Abstract: A robust and reproducible methodology to prepare stable inorganic nanoparticles with chiral morphology might hold the key to the practical utilization of these materials. We describe herein an optimized chiral growth method to prepare 4-fold twisted gold nanorods, where the amino acid cysteine is used as a dissymmetry inducer. Four tilted ridges were found to develop on the surface of single-crystal nanorods upon repeated reduction of HAuCl4, in the presence of cysteine as the chiral inducer and ascorbic acid as a reducing agent. From detailed electron microscopy analysis of the crystallographic structures, we propose that dissymmetry results from the development of chiral facets in the form of protrusions (tilted ridges) on the initial nanorods, eventually leading to a twisted shape. The role of cysteine is attributed to assisting enantioselective facet evolution, which is supported by density functional theory simulations of the surface energies, modified upon adsorption of the chiral molecule. The development of R-type and S-type chiral structures (small facets, terraces, or kinks) would thus be non-equal, removing the mirror symmetry of the Au NR and in turn resulting in a markedly chiral morphology with high plasmonic optical activity.

Abstract: The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex, multi-component systems, such as semiconductor devices, as well as catalytic and plasmonic nanoparticles. Unfortunately, the applicability of EDX tomography is severely limited because of extremely long acquisition times and high electron irradiation doses required to obtain 3D EDX reconstructions with an adequate signal-to-noise ratio. One possibility to address this limitation is intelligent denoising of experimental data using prior expectations about the objects of interest. Herein, this approach is followed using the deep learning methodology, which currently demonstrates state-of-the-art performance for an increasing number of data processing problems. Design choices for the denoising approach and training data are discussed with a focus on nanoparticle-like objects and extremely noisy signals typical for EDX experiments. Quantitative analysis of the proposed method demonstrates its significantly enhanced performance in comparison to classical denoising approaches. This allows for improving the tradeoff between the reconstruction quality, acquisition time and radiation dose for EDX tomography. The proposed method is therefore especially beneficial for the 3D EDX investigation of electron beam-sensitive materials and studies of nanoparticle transformations.