Merge pull request #36 from lucaskorol21/version_paper

Final paper version before submission

PAPER/paper.md

@@ -9,29 +9,31 @@ authors:
   - name: Lucas Korol
     orchid: 0009-0007-1329-6065
     affiliation: "1"
-  - name: Raymond J. Spiteri
-    affiliation: "2"
+  - name: Robert J. Green
+    orchid: 0000-0003-1849-1576
+    affiliation: "1, 3"
   - name: Jesus P. Curbelo
     orchid: 0000-0003-2417-071X
     affiliation: "2"
-  - name: Robert J. Green
-    affiliation: "1, 3"
+  - name: Raymond J. Spiteri
+    orchid: 0000-0002-3513-6237
+    affiliation: "2"
 affiliations:
   - name: Department of Physics & Engineering Physics, University of Saskatchewan, Saskatoon, Canada S7N 5E2
     index: 1
   - name: Department of Computer Science, University of Saskatchewan, Saskatoon, Canada S7N 5E2
     index: 2
   - name: Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, Canada V6T 1Z1 
     index: 3
-date: 23 March 2024
+date: 2 April 2024
 bibliography: paper.bib
 ---
 
 # Summary and Statement of Need
 
 Resonant x-ray reflectometry (RXR) is a cutting-edge synchrotron technique used to characterize the depth-dependent structure of quantum materials [@keimer_moore_NPh_2017; @green-etal_SRN_2020]. However, the main challenge impeding the success of RXR data analysis lies in its extreme complexity, driven by complicated model construction and the fitting of numerous independent variables. This complexity results in prolonged analysis periods that demand significant engagement from researchers. In response to these challenges, the Global Optimization of Resonant X-ray Reflectometry (GO-RXR) software emerged from rigorous development efforts as a main contribution from the work by [@korol_MSc_2023]. GO-RXR streamlines data analysis, enhances visualization, and reduces the expertise required, offering researchers a more efficient means to analyze RXR data. 
 
-One of the challenges addressed by [@korol_MSc_2023] included the analysis of LaMnO<sub>3</sub>/SrTiO<sub>3</sub> thin-film heterostructures. The data for these samples were collected at the resonant elastic and inelastic x-ray scattering beamline (REIXS) at the Candian Light Source (CLS) back in 2017. Although the initial data collection took only three days, attempts to analyze it with the tools available in 2021 yielded little success. In 2023, use of the GO-RXR tool led to a successful analysis, highlighting its efficacy in overcoming longstanding analysis barriers.
+One of the challenges addressed by [@korol_MSc_2023] included the analysis of LaMnO<sub>3</sub>/SrTiO<sub>3</sub> thin-film heterostructures. The data for these samples were collected at the resonant elastic and inelastic x-ray scattering beamline (REIXS) at the Candian Light Source (CLS) in 2017. Although the initial data collection took only three days, attempts to analyze it with the tools available in 2021 yielded little success. In 2023, use of the GO-RXR tool led to a successful analysis, highlighting the efficacy of the software in overcoming longstanding analysis barriers.
 
 The analysis of RXR presents multifaceted challenges, extending beyond computational aspects to encompass expertise in materials and the physics of light-matter interactions. This expertise is pivotal because it provides intuition about parameter adjustments and guides the direction of data analysis to achieve desired outcomes. In addressing this challenge, GO-RXR integrates global optimization algorithms, thereby lowering the expertise threshold necessary for effective data analysis. Through the extensive development of GO-RXR, diverse global optimization algorithms and unique objective functions were thoroughly explored. The software's capability to capture features in experimental data without exhaustive parameter understanding significantly reduces the expertise required. GO-RXR serves as a valuable scientific tool for material scientists, offering advanced capabilities to streamline data analysis and reduce the expertise barrier, ultimately facilitating breakthrough discoveries in the field of materials science.
 
@@ -45,27 +47,27 @@ GO-RXR is available for installation through its GitHub repository, providing us
 
 ## Basic concepts
 
-The primary goal of GO-RXR is to simplify and optimize the data analysis process for resonant x-ray reflectometry. Initially conceived as a command-line tool, it evolved into a GUI-based software with enhanced visualization capabilities. Developed using Python with PyQt5 for the interface, this software integrates the Pythonreflectivity [@pythonreflectivity] open-source package to carry out reflectivity calculations efficiently. GO-RXR has been tested extensively on Ubuntu 22.04 with Python 3.10, ensuring reliable performance on this operating system
+The primary goal of GO-RXR is to simplify and optimize the data analysis process for resonant x-ray reflectometry. Initially conceived as a command-line tool, it evolved into a GUI-based software with enhanced visualization capabilities. Developed using Python with PyQt5 for the interface, this software integrates the Pythonreflectivity [@pythonreflectivity] open-source package to carry out reflectivity calculations efficiently. GO-RXR has been tested extensively on Ubuntu 22.04 with Python 3.10, ensuring reliable performance on this operating system.
 
-\autoref{fig:flowchart} displays the main steps GO-RXR uses to convert the input into its output. The inputs of the software are the experimental data and are defined by the reflected intensity, incident grazing angle, photon energy, and polarization. The first step in the data analysis is the data selection step. In this step, the user selects the experimental datasets to include into the data analysis. The next step is the parameter selection. In this step, the user selects the model parameters to vary. The final step is the data fitting. In this step, the user selects the global optimization algorithm and its parameters and fits the data between the selected experimental and simulated datasets. The output of GO-RXR is the depth-dependent density profile, as defined by its model parameters.
+\autoref{fig:flowchart} displays the main steps GO-RXR uses to go from input to output. The inputs of the software are the experimental data and are defined by the reflected intensity, incident grazing angle, photon energy, and polarization. The first step in the data analysis is the data selection step. In this step, the user selects the experimental datasets to include into the data analysis. The next step is the parameter selection. In this step, the user selects the model parameters to vary. The final step is the data fitting. In this step, the user selects the global optimization algorithm and its parameters and fits the data from the selected experimental and simulated datasets. The output of GO-RXR is the depth-dependent density profile, as defined by the model parameters.
 
-![Flowchart of GO-RXR. The flowchart illustrates the path the software uses to take the experimental data and convert it into a depth-dependent density profile defined by its parameters.\label{fig:flowchart}](../FIGURES/go-rxr-flowchart.png)
+![Flowchart of GO-RXR. The flowchart illustrates the path the software uses to take the experimental data and convert them into a depth-dependent density profile.\label{fig:flowchart}](../FIGURES/go-rxr-flowchart.png)
 
 
 ## Example use-cases
 
-The GO-RXR software has been instrumental in analyzing RXR data for diverse applications. For instance, it has facilitated the in-depth study of the electrochemical water splitting catalyst La<sub>0.7</sub> Sr<sub>0.3</sub> MnO3/SrTiO<sub>3</sub> (LSMO/STO), revealing insights into the material's structural, electronic, and magnetic depth profiles. Moreover, GO-RXR's utilization in investigating the relationship between film thickness and the presence of ferromagnetism in the LaMnO<sub>3</sub>/SrTiO<sub>3</sub> heterostructure has shed new light on the mechanisms underlying magnetic phase transitions in ultra-thin films
+The GO-RXR software has been instrumental in analyzing RXR data for diverse applications. For instance, it has facilitated the in-depth study of the electrochemical water splitting catalyst La<sub>0.7</sub> Sr<sub>0.3</sub> MnO3/SrTiO<sub>3</sub> (LSMO/STO), revealing insights into the material's structural, electronic, and magnetic depth profiles. Moreover, GO-RXR's utilization in investigating the relationship between film thickness and the presence of ferromagnetism in the LaMnO<sub>3</sub>/SrTiO<sub>3</sub> heterostructure has shed new light on the mechanisms underlying magnetic phase transitions in ultra-thin films.
 
-The adaptability of GO-RXR extends to analyzing a wide array of quantum materials, including semiconductors, superconductors, and magnetic materials, allowing researchers to delve into their depth-dependent structures and properties. More generally, GO-RXR could could be apply to materials science research, particularly in comprehending the structural and electronic attributes crucial for designing cutting-edge electronic devices and functional materials, especially thin films. Furthermore, its ability to streamline data analysis processes and enhance visualization can benefit researchers across different disciplines, including physics, chemistry, and materials engineering, by providing them with a robust tool for studying complex material systems with greater efficiency and accuracy.
+The adaptability of GO-RXR extends to analyzing a wide array of quantum materials, including semiconductors, superconductors, and magnetic materials, allowing researchers to explore their depth-dependent structures and properties. More generally, GO-RXR could be applied to materials science research, particularly in comprehending the structural and electronic attributes for designing cutting-edge electronic devices and functional materials, especially thin films. Furthermore, its ability to streamline data analysis processes and enhance visualization can benefit researchers across different disciplines, including physics, chemistry, and materials engineering, by providing them with a robust tool for studying complex material systems with greater efficiency and accuracy.
 
 
 # Publications and ongoing research
 
-The GO-RXR software package was utilized for analyzing the RXR data in the paper titled "The effect of intrinsic magnetic order on electrochemical water splitting" published in the journal Applied Physics Reviews by [@vanderMinne_etal_2023]. Additionally, GO-RXR played a valuable role in a complementary experimental study investigating morphological and chemical disorder in epitaxial La<sub>0.67</sub>Sr<sub>0.33</sub>MnO<sub>3</sub>. This study is presently under review in the journal ACS Applied Materials & Interfaces, with its preprint format available online [@verhage_etal_2023]. These instances underscore the versatility and impact of GO-RXR in advancing scientific exploration, with ongoing research endeavors continuing to harness its capabilities for further discoveries.
+The GO-RXR software package was utilized for analyzing the RXR data in the paper titled "The effect of intrinsic magnetic order on electrochemical water splitting" published in the journal Applied Physics Reviews by [@vanderMinne_etal_2023]. Additionally, GO-RXR played a valuable role in a complementary experimental study investigating morphological and chemical disorder in epitaxial La<sub>0.67</sub>Sr<sub>0.33</sub>MnO<sub>3</sub>. This study has been accepted for publication in the journal ACS Applied Materials & Interfaces, with its preprint format available online [@verhage_etal_2023]. These instances underscore the versatility and impact of GO-RXR in advancing scientific exploration, with ongoing research endeavors continuing to harness its capabilities for further discoveries.
 
 # Acknowledgments
 
-Raymond J. Spiteri and Robert J. Green acknowledge the support
+Robert J. Green and Raymond J. Spiteri acknowledge the support
 from the Natural Sciences and Engineering Research Council of
 Canada (NSERC) Discovery Grant program. Lucas Korol
 acknowledges the support from the NSERC CREATE to INSPIRE