Further reduce paper.md wordcount

Author: aramyxt

paper/paper.md

@@ -52,12 +52,11 @@ bibliography: paper.bib
 
 # Summary
 
-The `Pore2Chip` Python package is designed to create two-dimensional micromodels using extracted data from three-dimensional X-ray computed tomography 
+The `Pore2Chip` Python package is designed to create 2D micromodels using extracted data from 3D X-ray computed tomography 
 (XCT) images. This package helps analyze soil structure and function, allowing for the investigation of environmentally significant biogeochemical processes 
-that impact soil organic matter (SOM) decomposition and loss, oxygen concentrations, and nutrient availability in disturbed or managed soils.  The extracted 
-data includes characterizations of the pore network using major water retention and flow metrics relevant to porous materials. Key metrics encompass pore size 
-distributions, pore throat size distributions, and connectivity (pore coordination numbers). The software's final output is a 2D scalable lab-on-chip SVG 
-design representing a core or aggregate. It can be fabricated with methods such as laser etching, 3D printing, and photolithography.
+that impact soil organic matter (SOM) decomposition and loss, oxygen concentrations, and nutrient availability in disturbed or managed soils. Key metrics encompass pore size 
+distributions, pore throat size distributions, and connectivity (pore coordination numbers). The final output is a 2D scalable SVG 
+design representing a core or aggregate. Designs can be fabricated with methods such as laser etching, 3D printing, and photolithography.
 
 # Statement of need
 
@@ -67,47 +66,41 @@ microstructure [@Stoof2016; @Liu2018; @Feng2020; @deOliveira2022; @Rooney2022].
 determine the flow of water, solutes, and gasses as well as SOM retention, transport, and distribution 
 [@hamamoto2010excluded; @bailey2017differences; @Waring2020]. Simplified, homogeneous pore networks provide innovative 
 demonstrations of how water, solutes, and microbes interact [@Bhattacharjee2022] but need more accurate representations of soil properties. 
-Heterogeneous synthetic habitats are more realistic but time-consuming to design and do not include pore network characteristics, such as pore 
-connectivity or pore throat measurements. Incorporating pore dynamics into soil models such as SOM degradation enables dynamic predictions for soil 
+Creating realistic heterogeneous habitats is time-consuming and does not include pore network characteristics, such as pore 
+connectivity. Incorporating pore dynamics into soil models such as SOM degradation enables dynamic predictions for soil 
 responses under changing pore networks [@davidson2012d; @moyano2018diffusion].
 
-Currently, there is no software available to seamlessly provide various micromodel designs that researchers can test and validate with minimal computational 
-cost (Dentz et al., 2023; Oostrom et al., 2014). `Pore2Chip` allows us to overcome this barrier by providing the intended users, such as earth scientists and 
-lab-on-chip instrument specialists, with easy-to-use research software for lab-on-chip designs. Specifically, the Pore2Chip-based data worth analysis of 
-high-resolution XCT images allows us to fill this experimental design gap by allowing the users to build a representative quasi-2D pore network along with 
-first-order, fast, and reasonably accurate flow models that can be linked with experiments. These `Pore2Chip` flow models are built using recent advances in 
+The need for software that can generate various micromodel designs that researchers can test and validate with minimal computational 
+cost [@Dentz2023; @Oostrom2014] is increasing. `Pore2Chip` allows this functionality by providing the intended users, such as earth scientists and 
+lab-on-chip instrument specialists, with easy-to-use research software for lab-on-chip designs. Specifically, the Pore2Chip-based data worth analysis allows researchers to 
+fill this experimental design gap by enabling the ability to build a representative quasi-2D pore network along with 
+first-order, fast, and reasonably accurate flow models that can be linked with experiments. These flow models are built using recent advances in 
 physics-informed neural networks [@New2024], laying the foundation to accelerate numerical simulations and improve the fidelity of predictions in 
 microscale environments. Moreover, `Pore2Chip` allows one to assess the impact of various system parameters, such as pore structures, fluid properties, and 
 flow conditions, needed to develop optimal micromodel experiments. Such a capability can guide model-experiment-data (ModEx) integration at the microscale, 
 allowing for upscaling microscale processes and predictions of dynamic soil properties and functions (see \autoref{fig:fig1}).
 
 ## Main features and differences with other tools
 
-Sphere packing algorithms are often used to simulate soil structures in a simplified 2D design. However, this approach only deals with individual grain size 
-distributions and grain clustering without allowing control over important porous properties such as pore connectivity and size distributions. `Pore2Chip` 
-addresses this limitation by representing pore networks as connected shapes. This enables users to easily create and control pore networks representing 
-various real-world conditions. `Pore2Chip` offers experimental design capabilities that cannot be achieved by existing software such as epyc [@Dobson2022]. 
+`Pore2Chip` addresses complex pore structures by representing pore networks as connected shapes, unlike older sphere packing algorithms. 
+This enables users to easily create and control pore networks representing various real-world conditions. `Pore2Chip` offers experimental design capabilities that 
+cannot be achieved by existing software such as epyc [@Dobson2022]. 
 `Pore2Chip` provides support and reproducibility for developing lab-on-chip experimental designs uniformly across different soil datasets with fast, reasonably 
-accurate, first-order flow modeling capabilities. Microscale experimental designs using printed Pore2Chip-based micromodels may target both abiotic and biotic 
+accurate, first-order flow modeling capabilities. Microscale experiments using `Pore2Chip` micromodels may target both abiotic and biotic 
 processes and be integrated into modeling efforts such as water flow modeling, reactive transport modeling, and microbial activity simulations. 
 
 
 ## Implementation details and support libraries
 
 Using `Porespy` [@Gostick2016], `OpenPNM` [@Gostick2016] and various graphics rendering libraries (e.g., drawsvg, ezdxf, svglib, cairosvg, reportlab), 
-`Pore2Chip` renders SVG or DXF micromodel designs of the desired XCT images or similar 3D imaging sources. Output designs are scalable and adjustable based 
-on the target porosity of the micromodel. Once the network is generated, it can be exported as micromodel data in SVG or DXF formats and VTK formats for 
+`Pore2Chip` renders SVG or DXF micromodel designs of the generated network. Output designs are scalable and adjustable based 
+on the target porosity of the micromodel. It can also be exported as micromodel data in VTK formats for 
 visualization in Paraview or microfluidic simulations with open-source software such as `PFLOTRAN` (https://www.pflotran.org), `OpenFOAM` (https://www.openfoam.com), 
 and other physics-informed neural network modules. If the user wants to extract data from XCT images, `Pore2Chip` has image filtering and network extraction 
 modules utilizing Otsu thresholding and `PoreSpy`. The generation function can also work with data extracted by other means if it is an array of values that 
 Python can read. 
 
 \autoref{fig:fig2} provides a high-level overview of the repository structure and example use cases (\autoref{fig:fig1}) within the `Pore2Chip` repository. 
-The package is hosted on the Python Package Index and can be installed using `pip` on any operating system with Python installed.
-Docker files exist for users who want to build an image that starts a JupyterLab server. 
-These JupyterLab-based notebooks provide various examples for users to better understand the `Pore2Chip` micromodel design and first-order flow capabilities. 
-The `tests` folder in the repository contains Python files used to test the `Pore2Chip` functionalities. 
-All source code is available via Git, so the user can build and upgrade the package if desired.
 
 # Figures