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We assess the potentiality of several geometries of metallic nanodimers (one of the simplest thermoplasmonic systems) as candidates for active particles (nanoswimmers) propelled and controlled by light (phototaxis). The studied nanodimers…
The activity and stability of a platinum nanoparticle (NP) is not only affected by its size but additionally depends on its shape. To this end, simulations can identify structure-property relationships to make a priori decisions on the most…
A new mechanism for reactivity of multiply twinned gold nanoparticles resulting from their inherently strained structure provides a further explanation of the surprising catalytic activity of small gold nanoparticles. Atomic defect…
Boosting the sensitivity of solid-state gas sensors by incorporating nanostructured materials as the active sensing element can be complicated by interfacial effects. Interfaces at nanoparticles, grains, or contacts may result in non-linear…
Noble metal nanoparticles show specific optical properties due to the excitation of localized surface plasmons that make them attractive candidates for highly sensitive bionanosensors. The underlying physical principle is either an…
While nanoalloys are of paramount scientific and practical interests, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including crystal phase, chemical ordering, and…
High electric fields can significantly alter catalytic environments and the resultant chemical processes. Such fields arise naturally in biological systems but can also be artificially induced through localized excitations at nanoscale.…
Graphite is one of the most chemically inert materials. Its elementary constituent, monolayer graphene, is generally expected to inherit most of the parent material's properties including chemical inertness. Here we show that, unlike…
Bimetallic nanoalloys have gained extensive attention due to their tunable properties and wide range of catalytic applications. However, achieving good compositional control in nanoalloy catalysts remains a formidable challenge. In this…
We determined a full 3D atomic structure of a dumbbell-shaped Pt nanoparticle formed by a coalescence of two nanoclusters using deep learning assisted atomic electron tomography. Formation of double twin boundary was clearly observed at the…
The kinetic processes in nanoparticle-based catalysis are dominated by large fluctuations and spatiotemporal heterogeneities, in particular for diffusion-influenced reactions which are far from equilibrium. Here, we report results from…
The study of metallic nanoparticles has a long tradition in linear and nonlinear optics [1], with current emphasis on the ultrafast dynamics, size, shape and collective effects in their optical response [2-6]. Nanoparticles also represent…
Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the cost of calculating the adsorption…
Self-assembly processes provide the means to achieve scalable and versatile metamaterials by "bottom-up" fabrication. Despite their enormous potential, especially as a platform for energy materials, self-assembled metamaterials are often…
We present work flows and a software module for machine learning model building in surface science and heterogeneous catalysis. This includes fingerprinting atomic structures from 3D structure and/or connectivity information, it includes…
Bimetallic catalysts provide new routes toward sustainable ammonia synthesis, but the structural dynamics controlling their performance under real-world conditions remain poorly understood. Here, we combine in situ gas-cell and multimodal…
Metallic nanoparticles embedded in stimuli-responsive polymers can be regarded as nanoreactors since their catalytic activity can be changed within wide limits: the physicochemical properties of the polymer network can be tuned and switched…
This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic…
At the nanometric scale, the synthesis of a random alloy (i.e. without phase segregation, whatever the composition) by chemical synthesis remains a not easy task, even for simple binary type systems. In this context, a unique approach based…
Accurately measuring the size, morphology, and structure of nanoparticles is very important, because they are strongly dependent on their properties for many applications. In this paper, we present a deep-learning based method for…