Publication Cover
Molecular Physics
An International Journal at the Interface Between Chemistry and Physics
Volume 112, 2014 - Issue 24
110
Views
21
CrossRef citations to date
0
Altmetric
Research Article

Prediction of the linear and nonlinear optical properties of tetrahydronaphthalone derivatives via long-range corrected hybrid functionals

, , , , &
Pages 3165-3172 | Received 21 Mar 2014, Accepted 09 Jun 2014, Published online: 30 Jun 2014
 

Abstract

The linear and nonlinear optical (NLO) properties of methoxybenzylidene (1) and thiophen-2-ylmethylidene (2) tetrahydronaphthalone derivatives are studied using long-range corrected density functional theory (LC-DFT). The calculated hyperpolarisabilities indicate that both compounds have measurable NLO properties (approximately one to two times the hyperpolarisability of p-nitroaniline). Charge-transfer indices and time-dependent DFT calculations suggest that the NLO properties are a result of a charge-transfer excitation, which is typical in conjugated donor–acceptor structures. The ultraviolet–visible spectra of 1 and 2 are also predicted using gap-fitting schemes, and these data are used to assess how accurately the hyperpolarisabilities of 1 and 2 could be estimated by the solvatochromic method.

Acknowledgements

The authors acknowledge the technical and financial support of King Abdulaziz University.

Additional information

Funding

This work was funded by King Abdulaziz University [grant number 21-3-1432/HiCi].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 886.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.