Fuel classification and adulteration detection using a highly sensitive plasmonic sensor

Rahad, Rummanur; Rakib, A.K.M.; Mahadi, Mahin Khan; Faruque, Md. Omar

doi:10.1016/j.sbsr.2023.100560

Cited by 20 publications

(6 citation statements)

References 52 publications

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“…In this scenario, the correlation is appropriate to measure the variation of the

curve in conjunction with the analyte RI. Therefore, there are two good metrics: RI sensitivity (

) and amplitude sensitivity (

), which are defined in the equations below [ 35 ],

where

represents the change in the resonant wavelength value, while

stands for the RI variation.

represents the change in the confinement loss and

denotes the initial confinement loss.…”

Section: Design and Analysis Of The Structurementioning

confidence: 99%

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A D-Shaped SPR-Based PCF Sensor with an Extremely High-Amplitude Sensitivity for Measuring the Refractive Index

Chen

et al. 2023

Micromachines

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In this work, a new D-shaped photonic crystal fibre sensor structure based on surface plasmon resonance (SPR) is purposed for measurement of analyte refractive index (RI). In this design, a silica cylinder is polished into a D-shaped silica material structure with a flattened surface where two Au nanowires are coated. Some air holes are omitted to form the core mode region. With the implementation of gold nanowires and a core region for the exciting SPR effect with variate physical values, analyte RI can be measured with a wavelength coverage from 850 to 1350nm. The numerical simulation shows the maximum wavelength sensitivity of the purposed design achieves 19,600nm/RIU with an RI coverage from 1.37 to 1.42. Moreover, the sensor has a tremendous amplitude sensitivity and the maximum absolute value is about 2300RIU−1. Benefiting from these outstanding performance, the purposed structure can be given priority when it is applied in biomedical detecting and environmental assessment science.

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“…In this scenario, the correlation is appropriate to measure the variation of the

curve in conjunction with the analyte RI. Therefore, there are two good metrics: RI sensitivity (

) and amplitude sensitivity (

), which are defined in the equations below [ 35 ],

where

represents the change in the resonant wavelength value, while

stands for the RI variation.

represents the change in the confinement loss and

denotes the initial confinement loss.…”

Section: Design and Analysis Of The Structurementioning

confidence: 99%

“…In this scenario, the correlation is appropriate to measure the variation of the CL curve in conjunction with the analyte RI. Therefore, there are two good metrics: RI sensitivity (S n ) and amplitude sensitivity (S a ), which are defined in the equations below [35],…”

Section: Elliptical-core Capillarymentioning

confidence: 99%

A D-Shaped SPR-Based PCF Sensor with an Extremely High-Amplitude Sensitivity for Measuring the Refractive Index

Chen

et al. 2023

Micromachines

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show abstract

“…Under this scenario, it is appropriate to characterize the relationship between changes in the CL curves and variations in RI. Therefore, the RI sensitivity, denoted as S n , can be a good metric, and it is obtained through the following equation [37],…”

Section: Auxiliarymentioning

confidence: 99%

A Polished-D-Shape SPR-Based Photonic Crystal Fiber Sensor with High Sensitivity for Measuring Refractive Index

Li,

Jiang,

et al. 2023

Crystals

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In the correspondence, a novel polished-D-shape photonic crystal fiber sensor structure on the basis of surface plasmon resonance is proposed for measuring analyte refractive index. With the help of the finite element method, sensing performances of the structure have been analyzed through numerical simulations along with a step-by-step optimization . In this design, different capillaries are gathered and processed to form a D-shape silica structure and nano-scale gold material is coated on the flattened surface. With utilization of a thin gold film and solid silica background, the resonance effect is excited and the loss curve has red shift along with an increase in refractive index, which is applied for sensing. From the simulation and calculation results, the final sensor structure achieves the optimal performance where values of maximum and average sensitivity reach 32,000 and 12,167 nm/RIU along with a sensing coverage of refractive index from 1.26 to 1.32. Also, the proposed design obtains a range of resonant wavelength from 1810 to 2540 nm. We believe the proposed sensor can be a potential candidate for organic and biological detection and related applications.

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“…DL learns rules for inputs and outputs from large amounts of data, enabling the construction of non-linear models for various applications. Deep learning optimization methods can be applied in the fields of metasurface device design, including sensor [22][23][24][25], demultiplexer [26,27], coupler [28,29], inferometer [30], etc, to improve their design efficiency.The use of neural networks to implement data-driven models provides a new approach for the design of electromagnetic structures [31][32][33][34][35][36], such as EIT [37], broadband absorption [38] and perfect absorption [39]. Deep learning uses neural networks to learn patterns in data, and after training and optimizing on a dataset of metasurfaces, neural networks can effectively predict the best metasurface design.…”

Section: Introductionmentioning

confidence: 99%

Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

Zhu,

Du,

Dong

et al. 2024

Phys. Scr.

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In order to speed up the process of optimizing design of metasurface absorbers, an improved design model for metasurface absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-short-term memory network, attention mechanism, and fully-connection layer network) is proposed. The metasurface structural parameters can be input into the forward prediction network to predict the corresponding absorption spectra. Meantime, the metasurface structural parameters can be obtained by inputting the absorption spectra into the inverse prediction network. Specially, in the inverse prediction network, the bidirectional long-short-term memory (BiLSTM) network can effectively capture the context relationship between absorption spectral sequence data, and the attention mechanism can enhance the BiLSTM output sequence features, which highlight the critical feature information. After the training, the mean square error (MSE) value on the validation set of the reverse prediction network converges to 0.0046, R2 reaches 0.975, and our network can accurately predict the metasurface structure parameters within 1.5s with a maximum error of 0.03mm. Moreover, this model can achieve the optimal design of multi-band metasurface absorbers, including the single-band, dual-band, and three-band absorptions. The proposed method can also be extended to other types of metasurface optimization design.

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Fuel classification and adulteration detection using a highly sensitive plasmonic sensor

Cited by 20 publications

References 52 publications

A D-Shaped SPR-Based PCF Sensor with an Extremely High-Amplitude Sensitivity for Measuring the Refractive Index

A D-Shaped SPR-Based PCF Sensor with an Extremely High-Amplitude Sensitivity for Measuring the Refractive Index

A Polished-D-Shape SPR-Based Photonic Crystal Fiber Sensor with High Sensitivity for Measuring Refractive Index

Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

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