Updated to JOSS paper acceptance and figures

Author: maruti-iitm

docs/source/conf.py

@@ -9,7 +9,7 @@
 project = 'Pore2Chip'
 copyright = '2025, Aramy Truong, Maruti Mudunuru, Md Lal Mamud'
 author = 'Aramy Truong, Maruti Mudunuru, Md Lal Mamud'
-release = '0.1.1'
+release = '0.2.0'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/index.rst

@@ -5,7 +5,7 @@
 
 Pore2Chip Documentation
 =======================
-A python package that takes XCT images of porous materials and generates representative digital twin micromodels.
+A python package that takes XCT images of porous materials and generates representative reduced complexity micromodels.
 
 .. figure:: _static/ModEx_Loop_SoilChip.jpg
    :width: 636px
@@ -35,7 +35,7 @@ Acknowledgements
 ----------------
 This research was performed on a project award (Award DOIs: 10.46936/ltds.proj.2024.61069/60012423; 10.46936/intm.proj.2023.60674/60008777; 10.46936/intm.proj.2023.60904/60008965) 
 from the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under contract no. DE-AC05-76RL01830. 
-The authors acknowledge the contributions of Michael Perkins at PNNL’s Creative Services, who developed the conceptual graphics in this paper.
+The authors acknowledge the contributions of Michael Perkins and Ben Watson at PNNL’s Creative Services, who developed the conceptual graphics in this paper.
 
 PNNL-SA-197910
 

docs/source/quickstart.rst

@@ -33,9 +33,8 @@ custom threshold values, and gray value masking can also be set in the function
 
 Step 3: Pore Data Extraction
 ----------------------------
-Now that the segmented data is loaded into memory, we will use the ``metrics`` module to extract the necessary information needed to 
-cunstruct the micromodel design. This extraction is based on pore network extraction via watershed segmentation and the SNOW 
-algorithm provided by `Porespy`.
+Now that the segmented data is loaded into memory, we will use the ``metrics`` module to extract the necessary information needed to construct the micromodel design. 
+This extraction is based on pore network extraction via watershed segmentation and the SNOW algorithm provided by `Porespy`.
 
 First, we will extract the pore and pore throat diameters:
 
@@ -51,8 +50,7 @@ Next, we will extract the pore coordination numbers:
    :start-after: # Step 3.2 Start
    :end-before: # Step 3.2 End
 
-The variables we extracted are arrays that contain all of the pore diameters, throat diameters, and coordination numbers for all the 
-extracted pores in the pore network. We can visualize the distribution of the data using `matplotlib`:
+The variables we extracted are arrays that contain all of the pore diameters, throat diameters, and coordination numbers for all the extracted pores in the pore network. We can visualize the distribution of the data using `matplotlib`:
 
 .. literalinclude:: ./examples/quickstart_example.py
    :language: python

paper/figures/1_AI4SoilChip.jpg

@@ -53,32 +53,30 @@ bibliography: paper.bib
 # Summary
 
 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. 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.
+(XCT) images. 
+This package helps analyze soil structure and function, allowing for the investigation of hydro-biogeochemical processes 
+that impact mineral extraction and reactivity, 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
 
-The resilience of agricultural and natural landscapes is intrinsically connected to soil structure. Land management (e.g., tillage, grazing, and fire) and 
-associated impacts (e.g., compaction and pore-clogging) along with climate disturbances (e.g., freeze-thaw, flooding, and sea level rise) can transform soil 
-microstructure [@Stoof2016; @Liu2018; @Feng2020; @deOliveira2022; @Rooney2022]. These changes in the soil microstructure 
-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. 
+The resilience of agricultural and natural landscapes is intrinsically connected to soil structure. 
+Land management (e.g., tillage, grazing, and fire) and associated impacts (e.g., compaction, pore-clogging) can transform soil microstructure [@Stoof2016; @Liu2018; @Feng2020; @deOliveira2022; @Rooney2022]. 
+These changes in the soil microstructure determine the flow of water, solutes, and gasses as well as mineral 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. 
 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 
+connectivity. 
+Incorporating pore dynamics into soil models such as chemical species degradation enables dynamic predictions for soil 
 responses under changing pore networks [@davidson2012d; @moyano2018diffusion].
 
-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 information analysis of XCT images 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}).
+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 information analysis of XCT images 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