Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments
Abstract This investigation presents the development and characterization of an advanced piezoelectric perovskite-based biosensing platform optimized for formalin detection in aqueous media through the implementation of Locally Weighted Linear Regression (LWLR) machine learning algorithms. The senso...
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| Format: | Article |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88766-y |
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| author | Jacob Wekalao Shobhit K. Patel Om Prakash Kumar Fahad Ahmed Al-zahrani |
| author_facet | Jacob Wekalao Shobhit K. Patel Om Prakash Kumar Fahad Ahmed Al-zahrani |
| author_sort | Jacob Wekalao |
| collection | DOAJ |
| description | Abstract This investigation presents the development and characterization of an advanced piezoelectric perovskite-based biosensing platform optimized for formalin detection in aqueous media through the implementation of Locally Weighted Linear Regression (LWLR) machine learning algorithms. The sensor architecture operates within the terahertz spectral region and incorporates an advanced nanomaterial composite system comprising black phosphorus, gold nanostructures, graphene, and barium titanate to maximize detection sensitivity and operational performance metrics. The engineered platform integrates a circular graphene metasurfaces configuration with a gold-based H-resonator assembly and concentrically arranged circular ring resonators. Computational simulations demonstrate vigorous sensing capabilities across three discrete frequency bands, achieving remarkable sensitivity parameters of 444 GHzRIU⁻¹, accompanied by a quality factor of 5.970 and detection accuracy of 7.576. The integration of LWLR-based optimization protocols substantially enhances prediction accuracy while reducing computational time by ≥ 85% as well as cutting down the required resources. The proposed sensor architecture presents significant potential for environmental monitoring and clinical applications, offering a highly sensitive and efficient methodology for quantitative formalin detection in aqueous environments. |
| format | Article |
| id | doaj-art-72b0cdc8705e46249a2a80ced994bfb1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-72b0cdc8705e46249a2a80ced994bfb12025-02-09T12:34:50ZengNature PortfolioScientific Reports2045-23222025-02-0115113210.1038/s41598-025-88766-yMachine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environmentsJacob Wekalao0Shobhit K. Patel1Om Prakash Kumar2Fahad Ahmed Al-zahrani3Department of Optics and Optical Engineering, University of Science and Technology of ChinaDepartment of Computer Engineering, Marwadi UniversityDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationComputer Engineering Department, Umm Al-Qura UniversityAbstract This investigation presents the development and characterization of an advanced piezoelectric perovskite-based biosensing platform optimized for formalin detection in aqueous media through the implementation of Locally Weighted Linear Regression (LWLR) machine learning algorithms. The sensor architecture operates within the terahertz spectral region and incorporates an advanced nanomaterial composite system comprising black phosphorus, gold nanostructures, graphene, and barium titanate to maximize detection sensitivity and operational performance metrics. The engineered platform integrates a circular graphene metasurfaces configuration with a gold-based H-resonator assembly and concentrically arranged circular ring resonators. Computational simulations demonstrate vigorous sensing capabilities across three discrete frequency bands, achieving remarkable sensitivity parameters of 444 GHzRIU⁻¹, accompanied by a quality factor of 5.970 and detection accuracy of 7.576. The integration of LWLR-based optimization protocols substantially enhances prediction accuracy while reducing computational time by ≥ 85% as well as cutting down the required resources. The proposed sensor architecture presents significant potential for environmental monitoring and clinical applications, offering a highly sensitive and efficient methodology for quantitative formalin detection in aqueous environments.https://doi.org/10.1038/s41598-025-88766-y2-bit encodingNanomaterialsAqueousMachine learningFood SafetyGraphene |
| spellingShingle | Jacob Wekalao Shobhit K. Patel Om Prakash Kumar Fahad Ahmed Al-zahrani Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments Scientific Reports 2-bit encoding Nanomaterials Aqueous Machine learning Food Safety Graphene |
| title | Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments |
| title_full | Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments |
| title_fullStr | Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments |
| title_full_unstemmed | Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments |
| title_short | Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments |
| title_sort | machine learning optimized design of thz piezoelectric perovskite based biosensor for the detection of formalin in aqueous environments |
| topic | 2-bit encoding Nanomaterials Aqueous Machine learning Food Safety Graphene |
| url | https://doi.org/10.1038/s41598-025-88766-y |
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