A Genetic Algorithm for Universal Optimization of Ultrasensitive Surface Plasmon Resonance Sensors with 2D Materials

Matej Sebek; Nguyen Thi Kim Thanh; Xiaodi Su; Jinghua Teng

doi:10.1021/acsomega.3c01387

A Genetic Algorithm for Universal Optimization of Ultrasensitive Surface Plasmon Resonance Sensors with 2D Materials

ACS Omega. 2023 May 26;8(23):20792-20800. doi: 10.1021/acsomega.3c01387. eCollection 2023 Jun 13.

Authors

Matej Sebek^{1

2

3}, Nguyen Thi Kim Thanh^{1

2}, Xiaodi Su³, Jinghua Teng³

Affiliations

¹ Biophysics Group, Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
² UCL Healthcare Biomagnetics and Nanomaterials Laboratories, 21 Albemarle Street, London W1S 4BS, United Kingdom.
³ Institute of Materials Research and Engineering, Agency for Science, Technology and Research, Innovis, Singapore 138634 Singapore.

Abstract

We present a general optimization technique for surface plasmon resonance, (SPR) yielding a range of ultrasensitive SPR sensors from a materials database with an enhancement of ∼100%. Applying the algorithm, we propose and demonstrate a novel dual-mode SPR structure coupling SPP and a waveguide mode within GeO₂ featuring an anticrossing behavior and an unprecedented sensitivity of 1364 deg/RIU. An SPR sensor operating at wavelengths of 633 nm having a bimetal Al/Ag structure sandwiched between hBN can achieve a sensitivity of 578 deg/RIU. For a wavelength of 785 nm, we optimized a sensor as a Ag layer sandwiched between hBN/MoS₂/hBN heterostructures achieving a sensitivity of 676 deg/RIU. Our work provides a guideline and general technique for the design and optimization of high sensitivity SPR sensors for various sensing applications in the future.