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Porosity

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Machine learning to predict effective reaction rates in 3D porous media from pore structural features.

Scientific reports
Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that ...

Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence.

Nature nanotechnology
X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for b...

A unified parameter model based on machine learning for describing microbial transport in porous media.

The Science of the total environment
The transport and retention of microorganisms are typically described using attachment/detachment and straining/liberation models. However, the parameters in the models varied significantly, posing a significant challenge to describe microbial transp...

Deep Learning-Based Photoacoustic Imaging of Vascular Network Through Thick Porous Media.

IEEE transactions on medical imaging
Photoacoustic imaging is a promising approach used to realize in vivo transcranial cerebral vascular imaging. However, the strong attenuation and distortion of the photoacoustic wave caused by the thick porous skull greatly affect the imaging quality...

Shale gas geological "sweet spot" parameter prediction method and its application based on convolutional neural network.

Scientific reports
Parameters such as gas content (GAS), porosity (PHI) and total organic carbon (TOC) are key parameters that reveal the shale gas geological "sweet spot" of reservoirs. However, the lack of a three-dimensional high-precision prediction method is not c...

Soft Robots' Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks.

Sensors (Basel, Switzerland)
Soft robots can create complicated structures and functions for rehabilitation. The posture perception of soft actuators is critical for performing closed-loop control for a precise location. It is essential to have a sensor with both soft and flexib...

Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.

Environmental science and pollution research international
Gas breakthrough pressure is a significant parameter for the gas exploration and safety evaluation of engineering barrier systems in the carbon dioxide storage, remediation of contaminated sites, and deep geological repository for disposal of high-le...

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods.

Analytical methods : advancing methods and applications
A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites. Then, ZSHPC/MWCNTs were used to modify a screen-printed electrode, and a portable electrochemical detection system combined with mac...

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

Bone
BACKGROUND: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone...

High-Fidelity Permeability and Porosity Prediction Using Deep Learning With the Self-Attention Mechanism.

IEEE transactions on neural networks and learning systems
Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments o...