用于黑色素瘤早期检测的高灵敏度石墨烯-金超表面光学生物传感器,使用一维卷积神经网络和二进制编码进行机器学习优化

IF 2.9 3区 物理与天体物理 Q3 NANOSCIENCE & NANOTECHNOLOGY
Jacob Wekalao , Ahmed Mehaney , Nassir Saad Alarifi , Mostafa R. Abukhadra , Hussein A. Elsayed
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引用次数: 0

摘要

本研究提出了一种先进的太赫兹超表面传感器,将石墨烯和金元素结合在w形谐振器配置中,用于非侵入性黑色素瘤检测。通过COMSOL Multiphysics模拟优化的传感器设计,通过检测黑色素瘤早期发展过程中皮肤组织折射率的微小变化来工作。优化后的传感器在太赫兹波段的灵敏度为450 GHzRIU−1,谱线宽度为35 GHz。一维卷积神经网络(1D-CNN)算法增强了传感器的预测能力,在各种操作参数中实现R2值超过0.95。该传感器通过化学势调制实现二进制信息编码,实现了双重功能。与传统的诊断方法相比,所提出的设计显示出显著的优势,提供快速、无创、高精度的检测。此外,我们的数值研究结果表明,所设计的传感器对各种几何参数和入射角具有一定的鲁棒性,这反过来又使其在实际黑色素瘤诊断应用中具有前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-sensitivity graphene-gold metasurface optical biosensor for early melanoma detection optimized with machine learning using a one-dimensional convolutional neural network and binary encoding
This research presents an advanced terahertz metasurface sensor incorporating graphene and gold elements in a W-shaped resonator configuration for non-invasive melanoma detection. The sensor design, optimized through COMSOL Multiphysics simulations, operates by detecting minute variations in the refractive index of skin tissue that occur during early melanoma development. The optimized sensor achieves a sensitivity of 450 GHzRIU−1 with a narrow spectral linewidth of 35 GHz in the terahertz regime. A one-dimensional convolutional neural network (1D-CNN) algorithm enhances the sensor's predictive capabilities, achieving R2 values exceeding 0.95 across various operational parameters. The sensor demonstrates dual functionality through binary information encoding capability via chemical potential modulation. The proposed design shows significant advantages over conventional diagnostic methods, offering rapid, non-invasive detection with high accuracy. Moreover, our numerical findings reveals that the designed sensor provides some robustness performance against various geometric parameters and incident angles, which in turns make it promising for practical melanoma diagnosis applications.
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来源期刊
CiteScore
7.30
自引率
6.10%
发文量
356
审稿时长
65 days
期刊介绍: Physica E: Low-dimensional systems and nanostructures contains papers and invited review articles on the fundamental and applied aspects of physics in low-dimensional electron systems, in semiconductor heterostructures, oxide interfaces, quantum wells and superlattices, quantum wires and dots, novel quantum states of matter such as topological insulators, and Weyl semimetals. Both theoretical and experimental contributions are invited. Topics suitable for publication in this journal include spin related phenomena, optical and transport properties, many-body effects, integer and fractional quantum Hall effects, quantum spin Hall effect, single electron effects and devices, Majorana fermions, and other novel phenomena. Keywords: • topological insulators/superconductors, majorana fermions, Wyel semimetals; • quantum and neuromorphic computing/quantum information physics and devices based on low dimensional systems; • layered superconductivity, low dimensional systems with superconducting proximity effect; • 2D materials such as transition metal dichalcogenides; • oxide heterostructures including ZnO, SrTiO3 etc; • carbon nanostructures (graphene, carbon nanotubes, diamond NV center, etc.) • quantum wells and superlattices; • quantum Hall effect, quantum spin Hall effect, quantum anomalous Hall effect; • optical- and phonons-related phenomena; • magnetic-semiconductor structures; • charge/spin-, magnon-, skyrmion-, Cooper pair- and majorana fermion- transport and tunneling; • ultra-fast nonlinear optical phenomena; • novel devices and applications (such as high performance sensor, solar cell, etc); • novel growth and fabrication techniques for nanostructures
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