Geochemical Influences on Carbon Nanotubes Transport in Subsurface Environments: Integrating Millifluidics, Spectral Induced Polarization, and Machine Learning

Geochemical Influences on Carbon Nanotubes Transport in Subsurface Environments: Integrating Millifluidics, Spectral Induced Polarization, and Machine Learning

Abstract Understanding nanoparticles, like carbon nanotube (CNT), transport and retention in porous media is critical for environmental risk assessment and remediation. Millifluidic visualization, spectral induced polarization (SIP), and deep neural network (DNN) were integrated to resolve spatio‐te...

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Bibliographic Details
Main Authors: Yixin Yang, Pengfei Liu, Kexin Chen, Sheng Zhou, Longlong Meng, Chi Zhang, Junnan Cao, Bate Bate
Format: Article
Language:English
Published: Wiley 2025-07-01
Series:Geophysical Research Letters
Subjects:
spectral induced polarization
millifluidics
machine learning
particle transport
monitoring
Online Access:https://doi.org/10.1029/2025GL116768
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Summary:Abstract Understanding nanoparticles, like carbon nanotube (CNT), transport and retention in porous media is critical for environmental risk assessment and remediation. Millifluidic visualization, spectral induced polarization (SIP), and deep neural network (DNN) were integrated to resolve spatio‐temporal CNT deposition dynamics under complex geochemical conditions. Millifluidic visualization revealed concentration‐dependent retention mechanisms: irreversible straining dominated at high CNT concentrations, while site‐blocking enabled delayed redistribution during flushing. SIP detected real‐time retention by chargeability (R2 = 0.82–0.96), while a DNN decoded SIP signals to predict spatially resolved CNT deposition (R2 = 0.926), outperforming phenomenological Cole‐Cole modeling. Surface coatings (e.g., montmorillonite, MMT) and divalent cations modulate retention by electrostatic repulsion and pore‐throat constriction, with CNT concentration governing transport efficiency. The hierarchy of controlling factors: CNT concentration > surface properties > cation valence > ionic strength (IS), provides a predictive framework for nanoparticle fate in subsurface environments. This integrated system bridges lab‐scale insights to field‐scale monitoring, advancing non‐invasive SIP applications for nanomaterial management.
ISSN:0094-8276
1944-8007

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