Anomalous transport models for fluid classification: insights from an experimentally driven approach
Abstract In recent years, research and development in nanoscale science and technology have grown significantly, with electrical transport playing a key role. A natural challenge for its description is to shed light on anomalous behaviours observed in a variety of low-dimensional systems. We use a s...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Nano |
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| Online Access: | https://doi.org/10.1186/s11671-025-04297-5 |
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| author | Sara Bernardi Paolo Begnamino Marco Pizzi Lamberto Rondoni |
| author_facet | Sara Bernardi Paolo Begnamino Marco Pizzi Lamberto Rondoni |
| author_sort | Sara Bernardi |
| collection | DOAJ |
| description | Abstract In recent years, research and development in nanoscale science and technology have grown significantly, with electrical transport playing a key role. A natural challenge for its description is to shed light on anomalous behaviours observed in a variety of low-dimensional systems. We use a synergistic combination of experimental and mathematical modelling to explore the transport properties of the electrical discharge observed within a micro-gap based sensor immersed in fluids with different insulating properties. Data from laboratory experiments are collected and used to inform and calibrate four mathematical models that comprise partial differential equations describing different kinds of transport, including anomalous diffusion: the Gaussian Model with Time Dependent Diffusion Coefficient, the Porous Medium Equation, the Kardar-Parisi-Zhang Equation and the Telegrapher Equation. Performance analysis of the models through data fitting reveals that the Gaussian Model with a Time-Dependent Diffusion Coefficient most effectively describes the observed phenomena. This model proves particularly valuable in characterizing the transport properties of electrical discharges when the micro-electrodes are immersed in a wide range of insulating as well as conductive fluids. Indeed, it can suitably reproduce a range of behaviours spanning from clogging to bursts, allowing accurate and quite general fluid classification. Finally, we apply the data-driven mathematical modeling approach to ethanol-water mixtures. The results show the model’s potential for accurate prediction, making it a promising method for analyzing and classifying fluids with unknown insulating properties. |
| format | Article |
| id | doaj-art-ab2f51e9f97149b39ef38c66a528c4d7 |
| institution | Kabale University |
| issn | 2731-9229 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Nano |
| spelling | doaj-art-ab2f51e9f97149b39ef38c66a528c4d72025-08-20T04:02:55ZengSpringerDiscover Nano2731-92292025-08-0120111810.1186/s11671-025-04297-5Anomalous transport models for fluid classification: insights from an experimentally driven approachSara Bernardi0Paolo Begnamino1Marco Pizzi2Lamberto Rondoni3Department of Mathematical Sciences, Politecnico di TorinoResearch Department, ELTEK S.p.A.Research Department, ELTEK S.p.A.Department of Mathematical Sciences, Politecnico di TorinoAbstract In recent years, research and development in nanoscale science and technology have grown significantly, with electrical transport playing a key role. A natural challenge for its description is to shed light on anomalous behaviours observed in a variety of low-dimensional systems. We use a synergistic combination of experimental and mathematical modelling to explore the transport properties of the electrical discharge observed within a micro-gap based sensor immersed in fluids with different insulating properties. Data from laboratory experiments are collected and used to inform and calibrate four mathematical models that comprise partial differential equations describing different kinds of transport, including anomalous diffusion: the Gaussian Model with Time Dependent Diffusion Coefficient, the Porous Medium Equation, the Kardar-Parisi-Zhang Equation and the Telegrapher Equation. Performance analysis of the models through data fitting reveals that the Gaussian Model with a Time-Dependent Diffusion Coefficient most effectively describes the observed phenomena. This model proves particularly valuable in characterizing the transport properties of electrical discharges when the micro-electrodes are immersed in a wide range of insulating as well as conductive fluids. Indeed, it can suitably reproduce a range of behaviours spanning from clogging to bursts, allowing accurate and quite general fluid classification. Finally, we apply the data-driven mathematical modeling approach to ethanol-water mixtures. The results show the model’s potential for accurate prediction, making it a promising method for analyzing and classifying fluids with unknown insulating properties.https://doi.org/10.1186/s11671-025-04297-5NanotechnologyAnomalous diffusionPDE calibrationVoltage discharge |
| spellingShingle | Sara Bernardi Paolo Begnamino Marco Pizzi Lamberto Rondoni Anomalous transport models for fluid classification: insights from an experimentally driven approach Discover Nano Nanotechnology Anomalous diffusion PDE calibration Voltage discharge |
| title | Anomalous transport models for fluid classification: insights from an experimentally driven approach |
| title_full | Anomalous transport models for fluid classification: insights from an experimentally driven approach |
| title_fullStr | Anomalous transport models for fluid classification: insights from an experimentally driven approach |
| title_full_unstemmed | Anomalous transport models for fluid classification: insights from an experimentally driven approach |
| title_short | Anomalous transport models for fluid classification: insights from an experimentally driven approach |
| title_sort | anomalous transport models for fluid classification insights from an experimentally driven approach |
| topic | Nanotechnology Anomalous diffusion PDE calibration Voltage discharge |
| url | https://doi.org/10.1186/s11671-025-04297-5 |
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