Graphene-Based Metasurface Refractive Index Biosensor for Hemoglobin Detection: Machine Learning Assisted Optimization

Patel, Shobhit K.; Surve, Jaymit; Parmar, Juveriya; Ayyanar, N.; Katkar, Vijay

doi:10.1109/tnb.2022.3201237

Cited by 30 publications

(16 citation statements)

References 42 publications

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“…Adding graphene nanoplatelets at a weight percent of 0.1-1 to an aluminum AA7068 matrix has been shown to boost the material's tensile strength, as reported by Ul Haq and Anand [13]. But others, such as Hasan et al [14] and Patel et al [15] found that adding graphene to AA7075 nanocomposites reduced the material's tensile strength.…”

Section: Introductionmentioning

confidence: 88%

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Prasanth

Jeevanandam²,

Selvaraju³

et al. 2023

Journal of Nanomaterials

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In this research, the friction and wear of AA7075 nanocomposites reinforced with graphene and graphite were studied. Graphene’s inclusion dramatically enhanced the material’s mechanical characteristics, friction, and wear resistance. AA7075 is strengthened with less graphene, and AA7075, reinforced with more graphite, exhibits similar wear and friction behavior. Wear rate and coefficient of friction predictions for AA7075-graphene nanocomposites were made using five machine learning (ML) regression models. ML simulations reveal that the wear and friction of AA7075-graphene composites are most sensitive to the proportion of graphene presence, the loadings, and the hardness.

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Section: Introductionmentioning

confidence: 88%

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Prasanth

Jeevanandam²,

Selvaraju³

et al. 2023

Journal of Nanomaterials

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“…with the help of solar absorbers and sensors, respectively [28][29][30]. Machine learning algorithms can be applied to several photo-optical devices for resource reduction and simulation time improvement as discussed in [31][32][33]. A temperature and magnetic field detecting sensor based on photonic crystal fiber based structure with high sensitivity [34,35].…”

Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 detecting rapid metasurface-based sensor

Patel

Surve

Parmar

et al. 2023

Diamond and Related Materials

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“…To verify the local average power measurement results, we applied them to the close-in (CI) path loss model, a propagation prediction model. Furthermore, we obtained the coefcient of determination using a linear regression equation [12,13] and performed a comparative analysis.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Analysis of Local Average Power According to Averaging Length Changes of 3, 6, 10, and 17 GHz in an Indoor Corridor Environment

Lee

Cho

2023

International Journal of Antennas and Propagation

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This study measures and analyzes the local average power for line-of-sight (LOS) and non-line-of-sight (NLOS) paths according to the averaging length in an indoor corridor environment. The indoor corridor comprises multiple offices, laboratory spaces, and lecture rooms. We selected 3, 6, 10, and 17 GHz measurement frequency bands. The measurement system consists of a signal generator, a low-noise amplifier, transmission and receiving antenna, and spectrum analyzer. To obtain an accurate prediction model of propagation due to the multipath effect, we determined the measurement method based on the measurement interval and number of measurements according to changes in the averaging length. 2, 4, 6, 8, and 10 lambdas (λ) were selected for the number of measurements by frequency, and 1.5 cm was set as the measurement interval. We used the close-in (CI) path loss model for the analysis according to changes in the averaging length. The coefficient of determination (R-squared) was applied using a linear regression equation to verify the measurement accuracy. Based on parameter n of the CI path loss model, no large differences were observed in the averaging length at each measurement frequency. However, at 2λ, owing to the multipath effect, R-squared was approximately 0.4–0.7 for the LOS path and 0.6–0.8 for the NLOS path. At 10λ, R-squared was approximately 0.7–0.8 for the LOS path and 0.8–0.9 for the NLOS path. This indicated that as the number of measurements increased by increasing the averaging length, the accuracy of the measurement results improved. The study findings will help determine an optimal averaging length, thus ensuring reliable indoor propagation measurement and contributing to the ITU-R standard.

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Graphene-Based Metasurface Refractive Index Biosensor for Hemoglobin Detection: Machine Learning Assisted Optimization

Cited by 30 publications

References 42 publications

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

SARS-CoV-2 detecting rapid metasurface-based sensor

Measurement and Analysis of Local Average Power According to Averaging Length Changes of 3, 6, 10, and 17 GHz in an Indoor Corridor Environment

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