Update to readme bullets related to modex loop; to make it consistent across modex bullets

Author: maruti-iitm

README.md

@@ -13,19 +13,19 @@ Below is a conceptual figure, workflow, and vision for this all-in-one Python to
 
 (2) **3D Pore Network Characterization (Pore2Chip):** The 3D pore network is analyzed to determine pore size frequency and distribution, which is critical for understanding flow and transport properties.
 
-3. **Transform 3D Pore Network into 2D Rendering (Pore2Chip):** The complex 3D network is simplified into a 2D rendering for easier analysis and visualization.
+(3) **Transform 3D Pore Network into 2D Rendering (Pore2Chip):** The complex 3D network is simplified into a 2D rendering for easier analysis and visualization.
 
-4. **Build Micromodels for Environmental Experiments (Pore2Chip):** Micromodels replicate environmental conditions, enabling controlled experiments to observe fluid flow and chemical species degradation.
+(4) **Build Micromodels for Environmental Experiments (Pore2Chip):** Micromodels replicate environmental conditions, enabling controlled experiments to observe fluid flow and chemical species degradation.
 
-5. **Microscale Experimental Data on Chemical Hotspots (Chip2Flow):** Detailed experiments using techniques like ToF-SIMS and SEM-EDS provide data on chemical hotspots within the porous media.
+(5) **Microscale Experimental Data on Chemical Hotspots (Chip2Flow):** Detailed experiments using techniques like ToF-SIMS and SEM-EDS provide data on chemical hotspots within the porous media.
 
-6a. **Pore-Scale Multi-Physics Modeling (Chip2Flow):** Simulations model fluid flow, heat transfer, and chemical reactions at the pore scale, which is needed to predict system behavior under different environmental conditions.
+(6a) **Pore-Scale Multi-Physics Modeling (Chip2Flow):** Simulations model fluid flow, heat transfer, and chemical reactions at the pore scale, which is needed to predict system behavior under different environmental conditions.
 
-6b. **Calibration and Validation (Chip2Flow):** Predictive AI/ML-enabled models are calibrated and validated using experimental data for accuracy and reliability.
+(6b) **Calibration and Validation (Chip2Flow):** Predictive AI/ML-enabled models are calibrated and validated using experimental data for accuracy and reliability.
 
-7a. **Understanding Fluid Flow and Species Degradation in Soil Core Experiments (Chip2Flow):** Experiments on soil cores provide vital information on fluid flow and chemical species degradation, connecting back to micromodel generation.
+(7a) **Understanding Fluid Flow and Species Degradation in Soil Core Experiments (Chip2Flow):** Experiments on soil cores provide vital information on fluid flow and chemical species degradation, connecting back to micromodel generation.
 
-7b. **Upscaled Properties (Chip2Flow):** Properties and behaviors observed at smaller scales are upscaled to larger scales (mm to cm) for real-world application.
+(7b) **Upscaled Properties (Chip2Flow):** Properties and behaviors observed at smaller scales are upscaled to larger scales (mm to cm) for real-world application.
 
 **Conclusion:** The iterative ModEx loop continuously improves multi-physics process models through integration with experimental data, leading to more accurate predictions for system evolution and rhizosphere function applications. Additional specifics on extending this to Critical Minerals and Material (CMM) science applications is here [Download the SoilTwin poster pdf](https://github.com/EMSL-Computing/Pore2Chip/blob/main/paper/material/Pore2Chip_Chip2Flow_Specifics.pdf)