Plasmonic hot carriers scratch the surface

Kumar, Sushant; Habib, Adela; Sundararaman, Ravishankar

doi:10.1016/j.trechm.2021.08.006

Cited by 12 publications

(11 citation statements)

References 79 publications

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“…The extraction and transmission efficiencies of photogenerated carriers are affected by device structures, fabrication processes, functional layer compositions and interface contact. [131][132][133] Therefore, by designing device structures, optimizing the preparation processes, regulating the active layer morphology and improving the transport layers, the extraction and transmission of carriers at the interface can be promoted, and nonradiative recombination can be suppressed, thus reducing open circuit voltage loss and improving the PCE and the operation stability of FSCs.…”

Section: Strategies To Enhance the Extraction And Transport Of Photog...mentioning

confidence: 99%

Performance enhancement strategies of fibrous solar cells for wearable hybrid energy systems

et al. 2023

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Section: Strategies To Enhance the Extraction And Transport Of Photog...mentioning

confidence: 99%

Performance enhancement strategies of fibrous solar cells for wearable hybrid energy systems

et al. 2023

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“…Meanwhile, the inevitable nonradiative decay of SP through Landau damping, which can generate a large amount of hot electrons and holes, has recently been intensively studied for optoelectronic and photocatalytic applications. 44,45 Physically, there are mainly two mechanisms contributing to the Landau damping of SP, i.e., direct interband and phonon-assisted intraband electronic transitions. Therefore, we systematically studied the contributions of direct interband and phononassisted intraband electronic transitions to hot carrier generation.…”

Section: Hot Carrier Generation and Distributionmentioning

confidence: 99%

Kagome metal KV₃Sb₅: an excellent material for surface plasmon and plasmon-mediated hot carrier applications in the infrared region

Jiang

et al. 2022

J. Mater. Chem. C

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“…Many factors including geometry, electronic band structure of the system, energy and momentum matching at the interface contribute to these processes. However, studying these processes only by experimental means is limited due to a variety of challenges such as interpretation of experimental data and time/spatial resolution of specific experiments. , …”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles

et al. 2023

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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.

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Plasmonic hot carriers scratch the surface

Cited by 12 publications

References 79 publications

Performance enhancement strategies of fibrous solar cells for wearable hybrid energy systems

Performance enhancement strategies of fibrous solar cells for wearable hybrid energy systems

Kagome metal KV₃Sb₅: an excellent material for surface plasmon and plasmon-mediated hot carrier applications in the infrared region

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles

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Plasmonic hot carriers scratch the surface

Cited by 12 publications

References 79 publications

Performance enhancement strategies of fibrous solar cells for wearable hybrid energy systems

Performance enhancement strategies of fibrous solar cells for wearable hybrid energy systems

Kagome metal KV3Sb5: an excellent material for surface plasmon and plasmon-mediated hot carrier applications in the infrared region

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles

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Kagome metal KV₃Sb₅: an excellent material for surface plasmon and plasmon-mediated hot carrier applications in the infrared region