Dataset Information

Surface Plasmon Enhancement of Eu3+ Emission Intensity in LaPO4/Ag Nanoparticles.

ABSTRACT: A promising way to improve the performance of luminescent materials is to combine them with noble metal nanoparticles. Herein, a set of silver/europium-doped lanthanum orthophosphate (Ag/La0.95Eu0.05PO4) nanostructures with different concentrations of silver nanoparticles were prepared and investigated. The presented overlap between the strongest europium (Eu3+) excitation line and the broad silver nanoparticle surface plasmon resonance makes the combination prospective for coupling. X-ray powder diffraction confirmed the monoclinic monazite structure. The transmission electron microscopy revealed particles with a rod-like shape and ~4 aspect ratio. Photoluminescence spectra show characteristic Eu3+ ion red emission. One of the requirements for an enhanced luminescence effect is the precise control of the distance between the noble metal nanoparticles and the emitter ion. The distance is indirectly varied throughout the change of Ag nanoparticle concentration in the La0.95Eu0.05PO4 host. The emission intensity increases with the increase in Ag nanoparticles up to 0.6 mol %, after which the luminescence decreases due to the nanoparticles' close packing and aggregation leading to the displacement of La0.95Eu0.05PO4 from the vicinity of the metal particles and reabsorption of the emitted light. The emission intensity of La0.95Eu0.05PO4 increases more than three times when the Eu3+ excitation is supported by the localized surface plasmon resonance in the Ag/La0.95Eu0.05PO4 nanostructures.

SUBMITTER: Kuzman S

PROVIDER: S-EPMC7412108 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Surface Plasmon Enhancement of Eu3+ Emission Intensity in LaPO4/Ag Nanoparticles.

Kuzman Sanja S Periša Jovana J Đorđević Vesna V Zeković Ivana I Vukoje Ivana I Antić Željka Ž Dramićanin Miroslav D MD

Materials (Basel, Switzerland) 20200710 14

A promising way to improve the performance of luminescent materials is to combine them with noble metal nanoparticles. Herein, a set of silver/europium-doped lanthanum orthophosphate (Ag/La0.95Eu0.05PO4) nanostructures with different concentrations of silver nanoparticles were prepared and investigated. The presented overlap between the strongest europium (Eu3+) excitation line and the broad silver nanoparticle surface plasmon resonance makes the combi ...[more]

PMID: 32664307

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Project description:Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of physical processes from plasmon excitation in the metal to their collection in a molecule or a semiconductor, where atomistic theoretical investigation may be particularly beneficial. Unfortunately, first-principles theoretical modeling of these processes is extremely costly, preventing a detailed analysis over a large number of potential nanostructures and limiting the analysis to systems with a few 100s of atoms. Recent advances in machine learned interatomic potentials suggest that dynamics can be accelerated with surrogate models which replace the full solution of the Schrödinger Equation. Here, we modify an existing neural network, Hierarchically Interacting Particle Neural Network (HIP-NN), to predict plasmon dynamics in Ag nanoparticles. The model takes as a minimum as three time steps of the reference real-time time-dependent density functional theory (rt-TDDFT) calculated charges as history and predicts trajectories for 5 fs in great agreement with the reference simulation. Further, we show that a multistep training approach in which the loss function includes errors from future time-step predictions can stabilize the model predictions for the entire simulated trajectory (∼25 fs). This extends the model's capability to accurately predict plasmon dynamics in large nanoparticles of up to 561 atoms, not present in the training data set. More importantly, with machine learning models on GPUs, we gain a speed-up factor of ∼103 as compared with the rt-TDDFT calculations when predicting important physical quantities such as dynamic dipole moments in Ag55 and a factor of ∼104 for extended nanoparticles that are 10 times larger. This underscores the promise of future machine learning accelerated electron/nuclear dynamics simulations for understanding fundamental properties of plasmon-driven hot carrier devices.

Dataset Information

Surface Plasmon Enhancement of Eu3+ Emission Intensity in LaPO4/Ag Nanoparticles.

Publications

Surface Plasmon Enhancement of Eu<sup>3+</sup> Emission Intensity in LaPO<sub>4</sub>/Ag Nanoparticles.

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