We report on the high resolution imaging of multipolar plasmonic resonances in aluminum nanoantennas using electron energy loss spectroscopy (EELS). Plasmonic resonances ranging from near-infrared to ultraviolet (UV) are measured. The spatial distributions of the multipolar resonant modes are mapped and their energy dispersion is retrieved. The losses in the aluminum antennas are studied through the full width at half-maximum of the resonances, unveiling the weight of both interband and radiative damping mechanisms of the different multipolar resonances. In the blue-UV spectral range, high order resonant modes present a quality factor up to 8, two times higher than low order resonant modes at the same energy. This study demonstrates that near-infrared to ultraviolet tunable multipolar plasmonic resonances in aluminum nanoantennas with relatively high quality factors can be engineered. Aluminum nanoantennas are thus an appealing alternative to gold or silver ones in the visible and can be efficiently used for UV plasmonics.

We demonstrate the strong influence of strain on the morphology and In content of InGaN insertions in GaN nanowires, in agreement with theoretical predictions which establish that InGaN island nucleation on GaN nanowires may be energetically favorable, depending on In content and nanowire diameter. EDX analyses reveal In inhomogeneities between the successive dots but also along the growth direction within each dot, which is attributed to compositional pulling. Nanometer-resolved cathodoluminescence on single nanowires allowed us to probe the luminescence of single dots, revealing enhanced luminescence from the high In content top part with respect to the lower In content dot base.

In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

We report on nanometer-scale cathodoluminescence (nanoCL) experiments in a scanning transmission electron microscope on individual core−shell CdSe/ CdS quantum dots (QDs). By performing combined photoluminescence (PL) and nanoCL experiments of the same individual QDs, we first show that both spectroscopies can be used equally well to probe the spectral properties of QDs. We then demonstrate that the spatial resolution of the nanoCL is only limited by the size of the QDs themselves by performing nanoCL experiments on QDs lying side by side. Finally, we show how nanoCL can be advantageous with respect to PL as it can rapidly and efficiently characterize the optical properties of a large set of individual QDs. These results contrast with pioneering CL works on II−VI QDs and pave the way to the characterization of any II−VI quantum-confined structure at the relevant scale.

Microscopie électronique à transmission en balayage Spectroscopie de perte d'énergie d'électrons Cathodoluminescence Plasmons de surface Excitons Nano-optiqueOver the past ten years, Scanning Transmission Electron Microscopes (STEM) fitted with Electron Energy Loss Spectroscopy (EELS) and/or Cathodoluminescence (CL) spectroscopy have demonstrated to be essential tools for probing the optical properties of nano-objects at sub-wavelength scales. Thanks to the possibility of measuring them at a nanometer scale in parallel to the determination of the structure and morphology of the object of interest, new challenging experimental and theoretical horizons have been unveiled. As regards optical properties of metallic nanoparticles, surface plasmons have been mapped at a scale unimaginable only a few years ago, while the relationship between the energy levels and the size of semiconducting nanostructures a few atomic layers thick could directly be measured. This paper reviews some of these highly stimulating recent developments.

Bound-states-in-the-continuum (BIC) is an emerging concept in nanophotonics with potential impact in applications, such as hyperspectral imaging, mirror-less lasing, and nonlinear harmonic generation. As true BIC modes are non-radiative, they cannot be excited by using propagating light to investigate their optical characteristics. In this paper, for the 1st time, we map out the strong near-field localization of the true BIC resonance on arrays of silicon nanoantennas, via electron energy loss spectroscopy with a sub-1-nm electron beam. By systematically breaking the designed antenna symmetry, emissive quasi-BIC resonances become visible. This gives a unique experimental tool to determine the coherent interaction length, which we show to require at least six neighboring antenna elements. More importantly, we demonstrate that quasi-BIC resonances are able to enhance localized light emission via the Purcell effect by at least 60 times, as compared to unpatterned silicon. This work is expected to enable practical applications of designed, ultra-compact BIC antennas such as for the controlled, localized excitation of quantum emitters.

<p>In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with a desired absorbance spectrum. Combining a Gaussian Process based Bayesian Optimization (BO) with a Deep Neural Network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable<i> </i>by humans, the proposed framework efficiently optimizes the nanomaterial synthesis, and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.</p>

It has been long known that low molecular weight resists can achieve a very high resolution, theoretically close to the probe diameter of the electron beam lithography (EBL) system. Despite technological improvements in EBL systems, the advances in resists have lagged behind. Here we demonstrate that a low-molecular-mass single-source precursor resist (based on cadmium­(II) ethylxanthate complexed with pyridine) is capable of a achieving resolution (4 nm) that closely matches the measured probe diameter (∼3.8 nm). Energetic electrons enable the top-down radiolysis of the resist, while they provide the energy to construct the functional material from the bottom-upunit cell by unit cell. Since this occurs only within the volume of resist exposed to primary electrons, the minimum size of the patterned features is close to the beam diameter. We speculate that angstrom-scale patterning of functional materials is possible with single-source precursor resists using an aberration-corrected electron beam writer with a spot size of ∼1 Å.