diff --git a/latex/ac-bo-hackathon.bib b/latex/ac-bo-hackathon.bib
new file mode 100644
index 0000000..f64b530
--- /dev/null
+++ b/latex/ac-bo-hackathon.bib
@@ -0,0 +1,47 @@
+@article{wallum_instrument_2023,
+  title = {An {{Instrument Assembly}} and {{Data Science Lab}} for {{Early Undergraduate Education}}},
+  author = {Wallum, Alison and Liu, Zetai and Lee, Joy and Chatterjee, Subhojyoti and Tauzin, Lawrence and Barr, Christopher D. and Browne, Amberle and Landes, Christy F. and Nicely, Amy L. and Gruebele, Martin},
+  year = {2023},
+  month = may,
+  journal = {Journal of Chemical Education},
+  volume = {100},
+  number = {5},
+  pages = {1866--1876},
+  publisher = {American Chemical Society},
+  issn = {0021-9584},
+  doi = {10.1021/acs.jchemed.2c01072},
+  urldate = {2023-11-03},
+  abstract = {As data science and instrumentation become key practices in common careers ranging from medicine to agriscience, chemistry as a core introductory course must introduce such topics to students early and at an accessible level. Advanced data acquisition and data science generally require expensive precision instrumentation and massive computation, often out-of-reach even for upper-level undergraduate laboratory courses. At the same time, a new generation of affordable do-it-yourself instruments presents an opportunity for incorporation of curricula focused on instrument design and computation into freshman-level courses. We present a new lab for integration into existing courses that starts with hands-on spectrometer building, moves to data collection, and finally introduces an advanced data science technique, singular value decomposition, at an appropriate level with minimal computing requirements. The hardware and software used are modular and inexpensive. The lab was tested in three community college general chemistry sections over two semesters. Previously, students taking these courses did not typically see advanced quantitative chemistry curricula before deciding whether to pursue a bachelor's degree. This lab allowed students to practice data collection and organization skills, use prewritten Jupyter notebooks that perform advanced data analysis, and gain presentation skills. A multiwave assessment completed by students highlights both successes and difficulties associated with incorporating multiple advanced topics involving instrument design, data collection, and analysis techniques in a single lab.},
+  file = {C:\Users\sterg\Zotero\storage\CJX8XCIR\Wallum et al_2023_An Instrument Assembly and Data Science Lab for Early Undergraduate Education.pdf}
+}
+
+@article{mahjour_interactive_2023,
+  title = {Interactive {{Python Notebook Modules}} for {{Chemoinformatics}} in {{Medicinal Chemistry}}},
+  author = {Mahjour, Babak and McGrath, Andrew and Outlaw, Andrew and Zhao, Ruheng and Zhang, Charles and Cernak, Tim},
+  year = {2023},
+  month = nov,
+  journal = {Journal of Chemical Education},
+  publisher = {American Chemical Society},
+  issn = {0021-9584},
+  doi = {10.1021/acs.jchemed.3c00357},
+  urldate = {2023-11-28},
+  abstract = {Data science is becoming a mainstay in research. Despite this, very few STEM graduates matriculate with basic formal training in programming. The current lesson plan was developed to introduce undergraduates studying chemistry or biology to chemoinformatics and data science in medicinal chemistry. The objective of this lesson plan is to introduce students to common techniques used in analyzing medicinal chemistry data sets, such as visualizing chemical space, filtering to molecules that observe the Lipinski rules of drug-likeness, and principal component analysis. The content provided in this lesson plan is intended to serve as a tutorial-based reference for aspiring researchers. The lesson plan is split into two three-hour class sessions, each with an introductory slide deck, Python notebook consisting of several modules, and lab report template. During this activity, students learned to parse medicinal chemistry data sets with Python, perform simple machine learning analyses, and develop interactive graphs. During each session, students complete the Python notebook protocol and fill out a lab report template after a short lecture. By the end of the lesson plan, students were able to generate and manipulate various plots of chemical space and they reported having increased confidence in their understanding of chemistry, Python, and data science.},
+  file = {C:\Users\sterg\Zotero\storage\EDQPCNCH\Mahjour et al_2023_Interactive Python Notebook Modules for Chemoinformatics in Medicinal Chemistry.pdf}
+}
+
+@misc{saar_low-cost_2022,
+  title = {A {{Low-Cost Robot Science Kit}} for {{Education}} with {{Symbolic Regression}} for {{Hypothesis Discovery}} and {{Validation}}},
+  author = {Saar, Logan and Liang, Haotong and Wang, Alex and McDannald, Austin and Rodriguez, Efrain and Takeuchi, Ichiro and Kusne, A. Gilad},
+  year = {2022},
+  month = jun,
+  number = {arXiv:2204.04187},
+  eprint = {2204.04187},
+  primaryclass = {cond-mat},
+  publisher = {arXiv},
+  doi = {10.48550/arXiv.2204.04187},
+  urldate = {2022-07-05},
+  abstract = {The next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop. Such systems have shown real-world success for scientific exploration and discovery, including the first discovery of a best-in-class material. To build and use these systems, the next generation workforce requires expertise in diverse areas including ML, control systems, measurement science, materials synthesis, decision theory, among others. However, education is lagging. Educators need a low-cost, easy-to-use platform to teach the required skills. Industry can also use such a platform for developing and evaluating autonomous physical science methodologies. We present the next generation in science education, a kit for building a low-cost autonomous scientist. The kit was used during two courses at the University of Maryland to teach undergraduate and graduate students autonomous physical science. We discuss its use in the course and its greater capability to teach the dual tasks of autonomous model exploration, optimization, and determination, with an example of autonomous experimental "discovery" of the Henderson-Hasselbalch equation.},
+  archiveprefix = {arXiv},
+  keywords = {Computer Science - Machine Learning,Computer Science - Robotics,Condensed Matter - Materials Science,education},
+  file = {C\:\\Users\\sterg\\Zotero\\storage\\SDBKQZS4\\Saar et al_2022_A Low-Cost Robot Science Kit for Education with Symbolic Regression for.pdf;C\:\\Users\\sterg\\Zotero\\storage\\6X86S3V4\\2204.html}
+}
diff --git a/latex/figures/world_map.png b/latex/figures/world_map.png
index 431cd84..84a4ff6 100644
Binary files a/latex/figures/world_map.png and b/latex/figures/world_map.png differ
diff --git a/main.tex b/main.tex
index ef06828..03dab81 100644
--- a/main.tex
+++ b/main.tex
@@ -1,7 +1,7 @@
 \PassOptionsToPackage{hyphens}{url}
-\documentclass[superscriptaddress, nofootinbib,  amsmath, amssymb, preprint]{revtex4-2}
+\documentclass[superscriptaddress, nofootinbib,  amsmath, amssymb, twocolumn]{revtex4-2}
 
-\usepackage[margin=1in]{geometry} 
+\usepackage[margin=1.5cm]{geometry} 
 \usepackage[english]{babel}
 \usepackage[utf8]{inputenc}
 \usepackage[]{graphicx}
@@ -169,9 +169,9 @@
 
 \usepackage{setspace}
 
-\clubpenalty=10000
-\widowpenalty=10000
-\displaywidowpenalty=10000
+% \clubpenalty=10000
+% \widowpenalty=10000
+% \displaywidowpenalty=10000
 \usepackage{titlesec}
 
 \titlespacing{\subsection}
@@ -206,29 +206,61 @@
 
 \section{Introduction}
 
-Bayesian optimization (BO) has emerged as a powerful tool in optimizing complex and expensive-to-evaluate functions, often outperforming traditional search methods in a variety of scientific domains such as optimizing composition and processing parameters to maximize alloy yield strength or identifying synthesis pathways that maximize efficacy of HIV drugs. Hackathons help people to connect, gain skills, and flesh out new ideas. In the words of Michelle Duke, the "Hackathon Queen":
+Bayesian optimization (BO) has emerged as a powerful tool in optimizing complex and expensive-to-evaluate functions, often outperforming traditional search methods in a variety of scientific domains such as optimizing composition and processing parameters to maximize alloy yield strength or identifying synthesis pathways that maximize efficacy of HIV drugs (\cref{fig:intro-bo}). Hackathons help people to connect, gain skills, and flesh out new ideas. In the words of Michelle Duke, the "Hackathon Queen":
 
 \begin{quote}
 A hackathon is a short competition where people work together in teams to solve problems and challenges by coming up with solutions and ideas.
 \end{quote}
 
+\begin{figure}
+    \centering
+    \includegraphics[width=1\linewidth]{latex/figures/intro-bo.png}
+    \caption{Optimization traces for various algorithms. Bayesian optimization (BO) typically outperforms traditional design of experiment (DoE) methods. It uses a smart model, often based on Gaussian Processes, to predict where to look next in an experiment to find the best results with minimal trials. This method is especially handy when dealing with experiments that don’t have straightforward outcomes or involve a lot of variables, which is often the case in these fields.}
+    \label{fig:intro-bo}
+\end{figure}
+
+
 The goal of the AC BO Hackathon was to leverage the expertise of a diverse, global community to advance the development and application of BO techniques for solving critical challenges in the physical sciences. The hackathon also aimed to foster collaboration and knowledge sharing among participants from different backgrounds, including academia, national laboratories, government agencies, and private industry. The event attracted 120 active participants from 44 teams, representing 41 academic institutions, 12 national labs, and 9 companies. Likewise, the participants were located in 38 cities, 14 countries, and 4 continents (\cref{fig:map}).
 
 \begin{figure}[h!]
     \centering
     % \captionsetup{justification=centering}
-    \includegraphics[width=1\textwidth]{latex/figures/world_map.png}
-    \caption{\textbf{Demographic distributions of the participating teams and their affiliations}. 
- \label{fig:map}}
+    \includegraphics[width=1\linewidth]{latex/figures/world_map.png}
+    \caption{Demographic distributions of the participating teams and their affiliations. \label{fig:map}}
 \end{figure}
 
+\begin{table*}[]
+\caption{List of projects and project types, with links to corresponding website project pages, repositories, videos, and social media posts.}
+\label{tab:projects}
+\setlength{\extrarowheight}{0.4em}
+\begin{tabularx}{\textwidth}{>{\centering\arraybackslash}p{1cm} X >{\centering\arraybackslash}X}
+\toprule
+\# & Project Name & Links \\ \midrule
+\href{https://example.com}{\#1} & Project A & 
+\href{https://github.com/example}{\faGithub} \,
+\href{https://youtube.com}{\faVideo} \,
+\href{https://twitter.com}{\faTwitter} \tabularnewline
+\href{https://example.com}{\#2} & Project B & 
+\href{https://github.com/example}{\faGithub} \,
+\href{https://youtube.com}{\faVideo} \,
+\href{https://linkedin.com}{\faLinkedin} \tabularnewline
+\href{https://example.com}{\#3} & Project C & 
+\href{https://github.com/example}{\faGithub} \,
+\href{https://youtube.com}{\faVideo} \,
+\href{https://twitter.com}{\faTwitter} \tabularnewline
+\bottomrule
+\end{tabularx}
+\end{table*}
+
+
 Participants were provided with various resources to prepare for the hackathon – this included GitHub classroom assignments with automated feedback, application- and theory-focused videos and tutorials, Python refresher materials, and a list of tools to consider using during the hackathon \cref{fig:preparation}.
 
+
 \begin{figure}
     \centering
-    \includegraphics[width=0.5\linewidth]{latex}
+    \includegraphics[width=1\linewidth]{latex/figures/preparation.png}
     \caption{Caption}
-    \label{fig:enter-label}
+    \label{fig:preparation}
 \end{figure}
 
 One of the unique aspects of this event is that it was hosted in Gather Town, a sort of union between traditional video conferencing software and retro arcade-style avatars and virtual spaces (\cref{fig:gathertown}).
@@ -280,6 +312,6 @@ \section*{Acknowledgements}
 %\printglossaries
 
 % \bibliographystyle{achemso}
-% \bibliography{refs}
+\bibliography{latex/ac-bo-hackathon}
 
 \end{document}
\ No newline at end of file