Scholarlink

<p>The paper introduces a computational inverse design framework based on an AI-based platform to identify alloyed plasmonic nanodisks best suited to broadband solar absorption, the generation of hot carriers, and thermal stability. Three alloy systems, namely Au₀.₄Ag₀.₄Cu₀.₂, Au₀.₅Cu₀.₅, and Ag₀.₃Pd₀.₄Al₀.₃, were tested using finite-difference time-domain (FDTD) simulations in wavelengths between 300 and 1200&#xa0;nm. Au₀.₄Ag₀.₄Cu₀.₂ exhibited the highest absorption efficiency (<i>Qₐ</i><sub><i>ß</i></sub><i>ₛ</i> = 3.4), Integrated Solar Absorption Efficiency Factor (<i>ISAEF</i> = 0.82), and hot carrier generation rate (5.8 × 10<sup>3</sup>⁰ s⁻<sup>1</sup>·cm⁻<sup>3</sup>) with a carrier multiplication efficiency (<i>η</i><sub><i>CM</i></sub>) of 1.22. It had also exhibited good thermal behavior with a rise of 18.2&#xa0;K and <i>ηₜₕₑ</i><sub><i>r</i></sub><i>ₘ</i> = 0.81. Bayesian optimization, combined with a deep neural network (<i>R</i><sup>2</sup> = 0.98) and alloy geometry, enabled the discovery of high-performing, yet counterintuitive, alloy geometries 10 times faster than with conventional search procedures. The nanodisks in alloy formation are lithographically feasible and sensible means of their creation are put across, coupled with the methods of protection to guard against oxidation and phase segregation. In spite of the modeling limitations, this project shows the feasibility of scalable, AI-seamless materials search in plasmonic new-generation devices. These nanostructures have the potential to find uses in solar energy collection, hot-carrier photodetectors, and miniaturized sensors, ushering in performance that was previously out of reach or pricey and inconvenient to manufacture, to industrial, and community-level photonic technologies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-driven discovery of alloyed plasmonic nanodisks for broadband solar absorption and charge carrier multiplication

  • 期刊论文

  • 发表日期：

    2025年7月31日

查看全文
  • Ravindra Prathap Singh,
  • N. Nagabhooshanam,
  • Yogendra Thakur,
  • Deepak Nathiya,
  • Anto Praveena,
  • U. L. Nagendra Kumar,
  • A. Rajaram

摘要

The paper introduces a computational inverse design framework based on an AI-based platform to identify alloyed plasmonic nanodisks best suited to broadband solar absorption, the generation of hot carriers, and thermal stability. Three alloy systems, namely Au₀.₄Ag₀.₄Cu₀.₂, Au₀.₅Cu₀.₅, and Ag₀.₃Pd₀.₄Al₀.₃, were tested using finite-difference time-domain (FDTD) simulations in wavelengths between 300 and 1200 nm. Au₀.₄Ag₀.₄Cu₀.₂ exhibited the highest absorption efficiency (Qₐßₛ = 3.4), Integrated Solar Absorption Efficiency Factor (ISAEF = 0.82), and hot carrier generation rate (5.8 × 103⁰ s⁻1·cm⁻3) with a carrier multiplication efficiency (ηCM) of 1.22. It had also exhibited good thermal behavior with a rise of 18.2 K and ηₜₕₑrₘ = 0.81. Bayesian optimization, combined with a deep neural network (R2 = 0.98) and alloy geometry, enabled the discovery of high-performing, yet counterintuitive, alloy geometries 10 times faster than with conventional search procedures. The nanodisks in alloy formation are lithographically feasible and sensible means of their creation are put across, coupled with the methods of protection to guard against oxidation and phase segregation. In spite of the modeling limitations, this project shows the feasibility of scalable, AI-seamless materials search in plasmonic new-generation devices. These nanostructures have the potential to find uses in solar energy collection, hot-carrier photodetectors, and miniaturized sensors, ushering in performance that was previously out of reach or pricey and inconvenient to manufacture, to industrial, and community-level photonic technologies.