Visiting Student Summer Undergraduate Research Program

The student will study and complete assignments and practicum in museum studies and art history, through applied practices of curatorship and museum research standard. 

OpenGulf (opengulf.github.io), a transdisciplinary digital humanities research group working on historical materials about the Arabian Gulf region, is looking for a visiting student researcher.

The data for this project include a searchable text version of Lorimer's Gazetteer of the Persian Gulf, Oman and Central Arabia and a large dataset of annotated places found in it. The student researcher will work (1) to refine the data from one portion of the Arabian Gulf region, visualize and write about that data and (2) to extract entities and quantities from this historical corpus (animals, plants, temperature ranges, objects, water terminology, construction materials, rainfall amounts) to build a geocoded dataset for a web-based environment atlas. A sample visualization of such data (for camels) created by a visiting researcher last summer can be found here: opengulf.github.io/camels/.

Creating dataset
Verification of data
Documenting work
Communicating with project team
Designing and implementing the web maps
Publishing the finalized data

OpenGulf (opengulf.github.io), a research group working on digital Gulf history is looking for a visiting student researcher.

The data for this project includes many handwritten historical archival documents from the Gulf region. The student researcher will work on transcribing some documents from scratch and correcting machine generated transcriptions. The student researcher will be doing research concerning handwritten digitized documents in Arabic, particularly from the Arabian Gulf. The student will learn about new advances in artificial intelligence which allow us to train models to recognize handwriting with precision using the Transkribus platform.

Research to identify documentation
Verification of data
Documenting work
Communicating with project team
Transcribing and correcting automated transcription of documents
Writing a few blog posts on the process such as these by a previous assistant.

The candidate is responsible for scientific thinking, programming, and applying methods and techniques in Computer Science to process data and train machine learning models with results compared against established benchmarks. The candidate will also be responsible for rendering results and generating reports on their work, as well as targeting a peer-reviewed research venue for publication of their work.

This project focuses on generating cartographic models of cities using spatial syntax techniques. The student will be involved in geographic information systems (GIS), geospatial data management, and spatial data analysis.

The selected student will work on:

1. Digitizing and generating maps for various cities.

2. Processing and managing geospatial data for urban analysis.

3. Developing and implementing spatial syntax methodologies for city mapping.

• Weeks 1–2: Developing essential skills (ArcGIS, QGIS, Adobe Illustrator, Python).

• Weeks 3–4: Collecting and processing cartographic data.

• Weeks 5–8: Generating city maps and preparing a final technical report.

• Generating city maps

• Document findings and write a technical report summarizing the project outcomes.

• Programming: Python or R Studio

• GIS & Mapping: ArcGIS, QGIS

• Design & Visualization: Adobe Illustrator

This position is connected to CITIES's CityGraph project. The student will work on developing software for knowledge extraction and annotation. Given the time limitation, the scope will be discussed with the student to focus on specific websites or information type:

1) Fact extraction from textual and tabular content, for example, the data extraction can extract facts about public buildings from their websites: ⟨Louvres_Abu_Dhabi, Open_days, Tuesday_to_Sunday⟩, and/or
2) Interactive interfaces for citizens to enter complementary information in the system, for example, by interacting with an AI chatbot, or simply filling out missing properties in the portal.

- Write python scripts for data scrapping from HTML public APIs.
- Code prompts for ChatGPT or similar models for relation extraction.
- Develop human input interfaces for data correction or augmentation.

We will develop cognitive brain-computer interaction using EEG brain imaging and machine learning. The candidate will work on developing VR simulations, collecting and processing EEG data, and developing machine learning models for classification/regression.

Develop VR simulation using Unity game engine. Develop machine learning models to process the EEG data. Participate in a literature review about the topic. Write a technical report about the project.

This project involves developing origami-inspired haptic interfaces for wearable hand augmentation. We will go through the complete design and evaluation for the interface, including design, simulation, 3D printing and prototyping, characterization, and evaluation with human subjects.

Learn about 3D modeling and printing technologies.
Develop code for controlling the haptic interface.
Build an experimental setup for characterizing the haptic interface.
Conduct a human-subject evaluation for the haptic interface.
Documenting code and writing up a report about the task.

Designing Dedicated 6G and Beyond Terrestrial Networks for Ubiquitous Unmanned Aerial Vehicle (UAV) Coverage

We aim to design a cellular terrestrial network for providing ubiquitous coverage and uninterrupted connectivity to aerial users, including unmanned aerial vehicles (UAVs) and high altitude platform (HAPs), that will find increasing use in the coming years for applications such as video surveillance, smart irrigation and agriculture, search-and-rescue mission and last mile cargo delivery to name a few. The network design will be optimized using classical optimization tools to minimize the coverage holes, i.e., areas without reliable cellular coverage, in the sky.

Weeks 1-2: The student will develop an understanding of the link budget analysis for channels between the dedicated terrestrial base station (TBS) and terrestrial user equipment (UE) as well as between the TBS and the UAV-UE.

Week 3: The student will formulate the problem to optimize the locations of these dedicated terrestrial base stations and their transmit power.

Week 4-6: The student will use an optimization technique to solve the formulated optimization problem, and provide some simulation results that corroborate the accuracy of the solution.

Week 7-8: The student will document the results in the form of a research paper.

1. Review the literature on wireless networks for aerial users. 

2. Formulate the optimization problem for the dedicated terrestrial base stations locations and solve it with available solvers. 

3. Prepare a report with all simulation results.

Literature review
Experiments
Report writing

Conduct research related to Neuroengineering. Our lab uses mechanical, electrical, and chemical techniques to attempt to interface with the brain and peripheral nervous system in a minimally invasive way. These technologies could potentially lead to therapies of neurological, metabolic, and gastrointestinal disorders. Responsibilities will include literature review, preparation, and characterization of nanomaterials, device design and fabrication, presenting data/results in the weekly group meetings.

Literature review, experimental set up, data collection, analysis

3D Printing and Bioprinting is increasingly being considered as an ideal tool for engineered tissues and organs. Students can choose to work on one of the focus areas
(i) New bioink isolation and formulation from plant / animal sources
(ii) Bioink optimization and bioprinting
(iii) Bioprinting of specific tissue structures such as bone, nerve, or skin and
(iv) Computational studies on various tissue engineering scaffold designs and computational bioprinting.

This project will focus on implementing different machine learning algorithms for advanced driver assistance and their mapping on smart mobile phones.

Development, implementation, testing, comparison between different approaches.

This project has multiple sub-projects that can be given to different students based on their interests, i.e., whether they would like to do hardware- or software-related work. These sub-projects aim at developing and optimizing bio-signal processing for low-power computing systems:

(a) Implementing architecture-aware pruning and quantization on biomedical DNNs to develop precision/recall-aware model compression techniques for embedded GPUs (like Nvidia, Jetson Nano, and Xavier).

(b) Implementation of advanced algorithms for time-series analysis for ECG/EEG for short-term and long-term predictions, and their deployment on embedded GPUs.

(c) Implementing and evaluating deep learning / autoencoder models for reconstructing bio-signals; in this work, we investigate the compressed sensing tolerance of bio-signals and investigate novel DL architectures and autoencoders, which can be used to reconstruct the original signal with maximum possible accuracy.

(d) Implementation of state-of-the-art deep learning models for classification of COVID-19 from real-world biomedical image datasets like X-rays.

Development, implementation, testing, comparison between different approaches

This project has multiple sub-projects that can be given to different students based on their interests, i.e., whether they would like to do hardware- or software-related work. These sub-projects aim at optimizing ML algorithms for embedded platforms deployed in autonomous systems like UAVs and autonomous vehicles under energy-constrained scenarios:

(a) Implementing tinyML benchmark applications (like tinyMlPerf) on Embedded GPUs like Jetson Nano, implementing different optimization techniques for performance/energy optimization, profiling, and benchmarking.

(b) Developing a hardware-aware neural architecture search framework (based on existing open-source implementations) under constraints for autonomous mobile robots (like Rovers and UAVs), and their deployment on embedded GPUs.

(c) Developing embedded Lifelong Learning algorithms for autonomous mobile robots (like Rovers and UAVs) considering limited energy budgets.

Development, implementation, testing, comparison between different approaches

This project has multiple sub-projects that can be given to different students, if multiple candidates apply, and based on their interests, i.e., whether they would like to do do hardware- or software-related work. These sub-projects aim at developing different types of security attacks and defenses in autonomous vehicles in the smart cities settings.

Topic (a) Implementing backdoor attacks for autonomous vehicles and evaluate them in the CARLA full-system simulator demonstrating a smart mobility use case, where a complete environmental model is available.

Topic (b) Implementing defenses for backdoors for autonomous vehicles and evaluating them in the CARLA full-system simulator demonstrating a smart mobility use case, where a complete environmental model is available. The work would analyze the pros and cons of existing defense mechanisms, their robustness in full-system setting, and potentially devise a more powerful defense considering the ADAS stack.

Topic (c) Robustness of Traffic Sign Recognition Framework against Adversarial Attacks: Most of the state-of-the-art adversarial attacks do not consider the complete pipeline of ML-based systems. For example, in the traffic sign recognition system, the impact of traffic sign detection is ignored in most of the research works. Therefore, in this project, we plan to evaluate the robustness of the traffic sign recognition system while considering the complete pipeline, including traffic sign detection and all preprocessing stages. To
evaluate the robustness against different environmental conditions, we plan to implement and analyze this whole pipeline in CARLA simulator.

Development, implementation, testing, comparison between different approaches

This project aims at inferring the traffic information such as traffic flow and speed of new urban environments (e.g., urban expansion or new cities). Specifically, the goal is to devise a machine learning model that is capable of zero-shot/few-shot prediction and inductive prediction of the traffic of urban road networks.

The student will work on:
1) Processing various urban data such as traffic speed, traffic flow, population, and POI (point-of-interests) from various sources;
2) Training machine learning models including large language models to exploit the few-shot and zero-shot prediction capability; and
3) Conducting experiments with real-world datasets.

- Analyze the importance of different data sources in inferring the traffic of new urban environments.
- Develop machine learning models that utilize LLMs for zero-shot/few-shot prediction of the traffic information.
- Write a technical report about the project.

Marine iguanas living on Galapagos Islands are known for their very efficient salt glands, where they “sneeze” out salt. Because they feed underwater, they take in a large amount of saltwater. In order to prevent dehydration, they must expel salt without expelling water, so they have specialized glands that remove salt from their blood. Bioinspired by this ability, the project aims to build an "artificial salt gland" in a microfluidic chip that can expel salt ions from sea water and demonstrate the capability of marine iguanas' salt sneezing on chip.

Design and build microfluidic chips and testing with various salt water samples in the lab.
Characterization of the extracted salt ions in terms of flow rate and power consumption.

Catalysis contributes to more than 35% of global GDP, and the importance of green, sustainable approaches in this field was underscored by the 2021 Nobel Prize in Chemistry for the development of organocatalysis. Radical reactions remain largely unexplored

in traditional organocatalysis. Photoredox catalysis has recently gained prominence, leveraging the enhanced redox properties of excited-state molecular catalysts using visible light. Developing organic catalysts capable of mimicking transition metal and photoredox catalysts without light activation could complement existing methods and advance greener approaches to radical generation. However, purely organic ground-state redox catalysis is extremely rare, and
no general organocatalysis platforms based on ground-state single-electron shuttling currently exist. To address this challenge, our lab has focused on developing new catalytic processes using redox-active organic molecules as catalytic platforms. We have discovered that organic nitrogen-centered persistent radicals are excellent, tunable ground-state redox catalysts. The student will work on further applications of our catalysts in developing sustainable synthetic methods.

Contribute to ongoing projects and perform synthetic/mechanistic studies.

Basic knowledge of organic chemistry (Organic chemistry I and/or II)

The Burt Marine Biology Lab in collaboration with the Center for Interacting Urban Networks is seeking a student research assistant to contribute to a study on the social impact of coastal urbanization in the Emirates. The full-time intern will be requested to validate, translate, and code interview transcripts recorded from the local fishing community, real estate developers, and environmental regulators across all seven Emirates.

A strong command of Arabic coupled with a high-level comprehension of the Emirati dialect is required. To efficiently complete these tasks and responsibilities, interns must be meticulous and exhibit strong time management and communication skills. Students from all cohorts, majors, skills, and perspectives are encouraged to apply, particularly those familiar with semi-structured interviews, and/or interested in the regional environment. Flexible working hours and remote conditions will be available as well as mentorship and networking opportunities in this field.

A strong command in Arabic coupled with a high-level comprehension of the Emirati dialect is required. To efficiently complete these tasks and responsibilities, interns must be meticulous and exhibit strong time management and communication skills. Students from all cohorts, majors, skills, and perspectives are encouraged to apply, particularly those familiar with semi-structured interviews, and/or interested in the regional environment. Flexible working hours and remote conditions will be available as well as mentorship and networking opportunities in this field.

A strong command in Arabic coupled with a high-level comprehension of the Emirati dialect is required.

Applicants must have experience in working with cell culture and should master aseptic techniques to prevent contamination, along with skills in maintaining cell cultures, including media preparation and passaging.

Familiarity with cell counting methods, viability assays, and troubleshooting issues such as contamination is essential.

While experience with microscopy techniques, including light, fluorescence, or confocal microscopy, and image processing using software like ImageJ or FIJI is an advantage, it is not strictly necessary.

Attention to detail, accurate record-keeping, and adherence to laboratory safety protocols remain crucial for ensuring reliable results and a safe working environment.

Senior or junior Biology major students are preferred for this project.

Motivated and engaged

Cell culture experience is a plus

Microfluidic experience is a plus

Microscopy and data analysis skills are highly appreciated

RNA is one of the essential molecules of life. The structure and interactions of RNA molecules have implications in medicine and understanding of biological processes. The project aims to model structure and dynamics of RNA molecules using molecular dynamics simulations.

Execute and analyze MD simulation data.
Write reports.
Attend group meetings.

This internship provides training in the basic analytical techniques for characterization of oil and gas, specifically related to components that cause fouling in the oil extraction and processing plants.

The student will work closely with the members of the Smart Materials Lab (SML) and the Center for Smart Engineering Materials (CSEM) to develop materials for sensors that could be used for detection and prevention of scaling in the UAE’s oil extraction facilities. The student will learn and operate specific experimental setups that provide analysis of oil under simulated environments close to those encountered in real conditions.

Application of standard chemical techniques for the preparation of oil fraction analysis using different analytical methods, acquiring and processing of analytical data, and presenting the results orally and in writing.

Collecting data with human participants in perception and cognition experiments (behavioral, eyetracking, or neuroimaging) using Matlab/Psychtoolbox or similar systems.

The successful applicant will play a crucial role in supporting primary research efforts within the Center. This includes contributing to data collection and analysis, managing equipment and consumables procurement, and preparing manuscripts. Familiarity with high-performance computing environments, as well as machine learning and artificial intelligence applications, is advantageous. The candidate will work in a multidisciplinary environment consisting of Faculty, Researchers, PhD-level scientists, graduate students and undergraduate students. Applicants must exhibit attention to detail, exceptional problem-solving and decision-making skills, and effective oral and written communication abilities. The willingness and ability to learn various techniques used in the lab are essential.

Delve into the forefront of Social Artificial Intelligence by harnessing the power of Large Language Models (LLMs) and Vision-Language Models (VLMs). This research aims to advance AI systems capable of understanding, interpreting, and engaging with human social and emotional dynamics across diverse modalities, including text, images, and multimodal inputs. By combining the natural language understanding of LLMs with the contextual richness of VLMs, the project seeks to develop human-centered AI that is empathetic, socially intelligent, and adaptable to real-world interactions. Potential applications include enhancing conversational agents, improving social robotics, enabling emotion-aware systems, and creating more intuitive tools for human-AI collaboration. This work not only pushes the boundaries of multimodal AI but also explores its role in shaping the future of effective and socially intelligent technologies.

The program spans approximately 8 weeks, with structured milestones to ensure a productive and enriching research experience. Below is an overview of the timeline and associated activities:

Week 1 (May 20 – May 24): Onboarding and Orientation
- Goals: Familiarize participants with the project, tools, and resources.
- Activities:
- Introduction to LLMs, VLMs, and their relevance in Social Artificial Intelligence.
- Overview of research objectives, datasets, and methodologies.
- Set up work environment (access to software, compute resources, datasets).
- Define individual goals and align expectations.

Week 2–3 (May 27 – June 7): Background Research and Skill Development
- Goals: Build a strong foundation in project-specific concepts and tools.
- Activities:
- Literature review on LLMs, VLMs, affective computing, and social AI.
- Training on necessary frameworks, such as PyTorch, TensorFlow, HuggingFace, etc.
- Experimentation with pre-trained LLMs and VLMs to understand their functionality.
- Selection of datasets and/or identification of problem-specific applications.

Week 4–5 (June 10 – June 21): Model Development and Experimentation
- Goals: Begin hands-on work on the project, focusing on data integration and model fine-tuning.
- Activities:
- Preprocessing and preparing datasets for model training or fine-tuning.
- Fine-tuning or adapting LLMs and/or VLMs for the chosen problem.
- Preliminary experimentation and evaluation of models.
- Weekly meetings to discuss progress, troubleshoot, and refine methodologies.

Week 6–7 (June 24 – July 5): Testing, Analysis, and Optimization
- Goals: Refine the developed models and analyze performance.
- Activities:
- Conduct thorough testing of models on validation datasets.
- Analyze results, including quantitative metrics and qualitative insights.
- Optimize models for better performance (e.g., hyperparameter tuning).
- Begin drafting documentation of methods and findings.

Week 8 (July 8 – July 12): Finalization and Presentation
- Goals: Wrap up the project and showcase the work.
- Activities:
- Compile a final report or research summary.
- Prepare a short presentation of findings and insights.
- Share results with supervisor.
- Reflect on the research experience and provide feedback.

As part of the research program, students will have the following responsibilities:
1. Research and Literature Review:
- Conduct an in-depth review of relevant literature on Large Language Models (LLMs), Vision-Language Models (VLMs), and their applications in Social Artificial Intelligence and Affective Computing.
- Identify gaps and opportunities in current research to inform project direction.

2. Data Collection and Preparation:
- Collect, preprocess, and curate datasets relevant to the project’s objectives.
- Ensure datasets are clean, well-structured, and ready for experimentation with LLMs and VLMs.

3. Model Experimentation and Development:
- Work with pre-trained LLMs and VLMs, fine-tuning them for specific tasks related to social and emotional AI.
- Develop custom solutions or modifications to enhance model performance for multimodal or affective tasks.

4. Performance Analysis and Optimization:
- Test and evaluate models using appropriate metrics (e.g., accuracy, F1-score, BLEU, etc.) for the specific tasks.
- Analyze results to identify strengths, weaknesses, and areas for improvement.
- Implement optimizations such as hyperparameter tuning or alternative architectures as necessary.

5. Collaboration and Communication:
- Participate in regular team meetings to discuss progress, troubleshoot issues, and align with project goals.
- Actively collaborate with mentors and peers, sharing insights and ideas to improve outcomes.

6. Documentation and Reporting:
- Document all experiments, including methodologies, parameters, and results, for reproducibility and transparency.
- Draft a final report summarizing key findings, challenges, and potential future directions for the research.

7. Presentation of Results:
- Prepare and deliver a presentation showcasing the research process, results, and impact to peers, mentors, and stakeholders during the final week.

8. Adherence to Ethical Standards:
- Ensure that all work adheres to ethical guidelines, including data privacy, fairness, and responsible AI practices.

Required skills include proficiency in Python and AI frameworks like PyTorch or TensorFlow, understanding of machine learning concepts in NLP and computer vision, experience with data preprocessing, strong critical thinking and communication abilities, research aptitude for literature reviews, and adaptability to learn new tools and methods.

This project explores how working memory supports behavior for uncertain future outcomes. Using a working memory task, we will ask participants to remember an item’s location and later report either its original or mirrored position. In one condition, participants will know in advance which location they need to report, allowing them to have a readily prepared movement. In a second condition, participants will only learn this information at response, preventing them from planning a movement in advance. This approach allows us to vary the amount of certainty about future action and, by extension, the ability to prepare movement in advance. We will examine how working memory performance is affected by uncertainty for the future by measuring and comparing participants' accuracy and precision for the two conditions.

The student will be responsible for carrying out literature searches about the project topic and providing thorough summaries of existing studies. They will be responsible for scheduling and communicating with participants, collecting data, compensating participants for their time, keeping record of study forms, copying and backing up data, and reporting back to research assistants in the lab. They will be required to take an active part in data analysis and interpretation.

Applicant must show efficient time management and ability to meet deadlines; show an ability to work independently with little supervision; show great attention to detail; have good social and communication skills; be punctual and well-organized.

Technology plays an increasingly vital role in the lives of both children and adults, with today’s children entering the digital space at younger ages than previous generations. Despite digital proliferation and concerns regarding potential negative impacts of excessive screen time on child development, there is a noticeable lack of research focused on wellbeing effects of technology use and the digital habits of young children in conjunction with their parents and caregivers, particularly in the UAE.

Current discourse primarily revolves around “screen time” — how long children should engage with digital devices. However, understanding the overall impact of technology on young children necessitates an exploration of the quality and nature of their interactions with these devices. Key questions include what applications can enhance cognitive development and learning, how technology can be involved in positive parent-child interactions, how policymakers and parents can identify high-quality resources, and how developers can create suitable interventions.

This research project strives to (1) assess the current landscape of digital technology use among children 0-8 years old through a literature review and comparison of international media guidelines, (2) investigate empirically the effects of digital media use through assessing caregivers and young children in the Emirate of Abu Dhabi, and (3) develop a rating system based on theory and empirical findings for digital interventions tailored to early childhood.

Week 1:
Introduction to the research project; Intensive training in the data collection and/or coding procedures (behavioral coding, physiological data cleaning) and the software used in the lab.

Week 2:
Accomplish reliability in the coding and data collection procedures to ensure the quality of the data;
Remaining weeks.

Support the ongoing project activities with regard to coding and data collection (with supervision of the project’s research assistants).

Responsibilities include active involvement in all stages of the data collection and processing, in particular: participation in data collection; coding of video, and physiological data following standardized procedures and manuals; prepare data for analyses.

Strong interpersonal and communication skills;

The ability to handle tasks requiring high levels of attention to detail, to carry complex research tasks to completion, and to efficiently manage work in the time required.

Stressors are external agents that can cause stress. These stressors can be physical, emotional or environmental. Stress response is the normal physiological change that happens to our body when we encounter various stressors. In most situations, this response is temporary and the physiological change returns to its baseline state. Children living in various adversities are continuously exposed to stressors. This prolonged exposure to stressors cause toxic stress which can negatively affect children’s development. 

Understanding the impact of toxic stress on the healthy development of children, our lab partnered with a Jordan based NGO called Taghyeer to work with Syrian Refugee families living in Jordan. Through Taghyeer, we are collaborating with Professor Rana Dajani from Hashemite University who is also the founder and Director of We Love Reading (WLR) program. WLR promotes parent-child interactions through read-aloud sessions.

Our current project targets Syrian refugee families with young children in Jordan living under displacement, overcrowded living conditions, and limited resources. Believing in the vital role parents play to engage in stimulating activities that help children build skills needed for school, this project explores the positive impact of reading intervention on these children.

Reading aloud is not a common habit, thus WLR program offers a unique opportunity for these families. Participants, many of who do not own children’s books, receive colorful, age appropriate books in Arabic. Families in the project were randomly divided into three groups: one received the reading intervention, another received books but no training, and the third group received no books or intervention. We collected data at three points to assess the impact, using a mixed-method approach. By including physiological data, the research explores how family stress processes can affect the success of early interventions. The results of this project aim to guide social policies that support the development and well-being of refugee children, helping to reduce the long-term effects of displacement.

Week 1:
Introduction to the research project and the coding that has been done. Accomplish reliability in the coding to ensure the quality of the data.

Week 2:
Support the project’s research assistant in ongoing analysis of the coded data.

Remaining weeks:
Support the project’s research assistant in the process of writing a manuscript that will be submitted for publication (This project will focus on a specific aspect of the project – cultural parental expectation of child-rearing through the book reading intervention).

Responsibilities include active involvement in all stages of the coding reliability check, thematic analysis, and various process of writing a manuscript.

Carbon dioxide (CO2) is currently considered to be the most important greenhouse gas (GHG) in our Earth’s atmosphere, as it contributes the most to the greenhouse effect (GHE). GHGs trap heat in the atmosphere and warm the Earth and its importance is measured with two factors: heat-trapping efficiency and atmospheric lifetime. To reduce the GHE, it is essential to either reduce GHG emissions or to remove the GHG present in the atmosphere. Although CO2 does not have the highest efficiency, the amount of time it takes to naturally degrade in the atmosphere is comparatively long, therefore the amount of CO2 in the atmosphere is constantly increasing. Thus, removing CO2 that is preexisting should be the focus, rather than simply trying to reduce emissions and allowing nature to remove the gases. Although there are preexisting CO2 removal technologies such as DAC (direct air capture), BECCS (bioenergy with carbon capture and storage), COF (covalent organic framework), MOF (metal-organic framework), or ionic liquids, oftentimes these processes are energy-intensive or require substantial land and water. This, in turn, leads to increased pollution near the sites (torres).

To solve this problem, we will optimize the synthesis for a material that is able to adsorb CO2 and trap it in its pores. We will use a PEI (polyethyleneimine)-impregnated silica material to do so. PEI is a polymer with monomers consisting of the repeating units CH2CH2NH2. PEI is used over other polymers such as PEG as the PEI nitrogen is more Lewis basic than the PEG oxygen. The PEI enters the silica pores and creates active sites that are able to adsorb and retain CO2 within these pores. It is important to note whether CO2 will stay trapped or will desorb with time. With preexisting knowledge of the PEI and silica system model, we plan to optimize the amount of CO2 that can be adsorbed and clearly understand the mechanism by which this happens. Although the interaction of the active sites on CO2 is well-known in theory, it has yet to be proven using spectroscopy. The spectroscopic evidence for the formation of carbamate and bicarbonate species during CO2 adsorption, both in the absence and presence of H2O, is lacking. The solid-state NMR can selectively probe various chemical environments of nuclei such as 14N, 29Si, 15N, 13C, 1H, and 17O. SSNMR examines the environment of nuclei with a magnetic spin and helps to determine the interactions and thus the structure.

Assisting research associates with experiments, sample synthesis

Basic synthetic skills

This project focuses on the development and application of advanced functional materials, specifically Covalent Organic Frameworks (COFs), for the removal of persistent organic pollutants such as per- and polyfluoroalkyl substances (PFAS) from water systems. The research involves material synthesis, characterization using techniques like SEM, XRD, and FTIR, and performance testing for contaminant removal via adsorption. The student will contribute to optimizing material performance and understanding the mechanisms involved in pollutant removal.

• Assist in the synthesis and functionalization of COF materials.
• Perform material characterization using advanced techniques.
• Conduct batch and column adsorption experiments.
• Record and analyze experimental data.
• Prepare progress reports and present findings to the research group.