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Porosity

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Deep learning method of stochastic reconstruction of three-dimensional digital cores from a two-dimensional image.

Physical review. E
Digital cores can characterize the true internal structure of rocks at the pore scale. This method has become one of the most effective ways to quantitatively analyze the pore structure and other properties of digital cores in rock physics and petrol...

Evaluation of reproducible cryogel preparation based on automated image analysis using deep learning.

Journal of biomedical materials research. Part A
Cryogels represent a class of porous sponge-like materials possessing unique properties including high-fidelity reproduction of tissue structure and maximized permeability. Their architecture is mainly based on an interconnected network of macropores...

Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs.

Scientific reports
This study employs a stacked ensemble machine learning approach to predict carbonate rocks' porosity and absolute permeability with various pore-throat distributions and heterogeneity. Our dataset consists of 2D slices from 3D micro-CT images of four...

Deep learning for diffusion in porous media.

Scientific reports
We adopt convolutional neural networks (CNN) to predict the basic properties of the porous media. Two different media types are considered: one mimics the sand packings, and the other mimics the systems derived from the extracellular space of biologi...

An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors.

Sensors (Basel, Switzerland)
This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the so...

Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet.

Neural networks : the official journal of the International Neural Network Society
We predict steady-state Stokes flow of fluids within porous media at pore scales using sparse point observations and a novel class of physics-informed neural networks, called "physics-informed PointNet" (PIPN). Taking the advantages of PIPN into acco...

Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring.

Nano letters
Respiratory pattern is one of the most crucial indicators for accessing human health, but there has been limited success in implementing fast-responsive, affordable, and miniaturized platforms with the capability for smart recognition. Herein, a full...

Deep learning to overcome Zernike phase-contrast nanoCT artifacts for automated micro-nano porosity segmentation in bone.

Journal of synchrotron radiation
Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacuna...

Nonionic surfactant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic concentration-dependent performance, mechanism, and machine learning prediction.

Environmental research
The surfactant-enhanced bioremediation (SEBR) of organic-contaminated soil is a promising soil remediation technology, in which surfactants not only mobilize pollutants, but also alter the mobility of bacteria. However, the bacterial response and und...

Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches.

Chemosphere
Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computation...