A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Conductive mixed-order generalized dispersion model for noble metals in the optical regime. | LitMetric

AI Article Synopsis

  • The Generalized Dispersion Model (GDM) is a framework that can represent various dispersion models using Padé polynomials, which helps in modeling materials' behaviors.
  • Researchers discovered that materials with Drude dispersive terms can be modeled more efficiently by using a mixed-order model that combines a 1st order Padé polynomial and an additional conductivity term.
  • This mixed-order model not only maintains accuracy but also reduces the number of unknowns needed, leading to significant computational efficiency improvements—up to 12.5% in theory and practical reductions of 9% in memory usage and 11% in processing time.

Article Abstract

Various dispersion models can be expressed as special cases of the Generalized Dispersion Model (GDM), which is composed of a series of Padé polynomials. While important for its broad applicability, we found that some materials with Drude dispersive terms can be accurately modeled by mixing a 1 order Padé polynomial with an extra conductivity term. This conductivity term can be separated from the auxiliary differential equation (ADE). Therefore, the proposed mixed-order model can achieve the same accuracy with fewer unknowns, thus realizing higher computational efficiency and lower memory consumption. For examples, we derive the model parameters and corresponding numerical errors for noble metals including Au, Ag, and Al in the optical regime. Finally, the proposed model's efficiency improvements are validated through implementation within a Discontinuous Galerkin Time Domain (DGTD) framework. The proposed model can achieve up to 12.5% efficiency improvement in theory compared to the conventional GDM with the same accuracy. A numerical example validates that, in practice, 9% memory reduction and 11% acceleration can be realized.

Download full-text PDF

Source
http://dx.doi.org/10.1364/OE.435297DOI Listing

Publication Analysis

Top Keywords

generalized dispersion
8
dispersion model
8
noble metals
8
optical regime
8
conductivity term
8
model achieve
8
model
5
conductive mixed-order
4
mixed-order generalized
4
model noble
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!