Ahsanullah University of Science and Technology (AUST), through the AUST Applied Simulation and Modeling Lab - AASML, cordially invites students and aspiring researchers to an exclusive academic webinar titled "Building Strong Profiles for Higher Studies with Full Funding."
We are deeply honored to host Prof. Dr. Saidur Rahman as our distinguished speaker. Dr. Rahman serves as a Distinguished Research Professor and the Head of the Research Centre for Nano-Materials and Energy Technology (RCNMET) at Sunway University, Malaysia, alongside holding a full professorship in the Department of Engineering at Lancaster University, UK. A globally recognized authority in renewable energy, nanofluids, and heat transfer, he is currently ranked 1st in Asia and 7th globally among the top scientists in Mechanical Engineering by the AD Scientific Index 2025. With a prolific portfolio of over 350 high-impact journal publications and a history of successfully mentoring more than 60 postgraduate students, Dr. Rahman brings unparalleled expertise to this session.
This comprehensive seminar is designed to provide strategic guidance for students aiming to pursue funded postgraduate education. Dr. Rahman will outline the foundational steps of getting started with research, including how to identify a research niche, initiate undergraduate projects, and develop essential technical and problem-solving skills. He will also emphasize the importance of consistency, active networking, and hands-on experience in building a highly competitive academic profile.
Furthermore, the session will delve into the practicalities of preparing for MS and PhD applications. Attendees will learn how to navigate scholarship opportunities, effectively contact international professors and research groups, and prepare compelling CVs and Statements of Purpose. Dr. Rahman will highlight common mistakes applicants should avoid.
Don't miss this rare opportunity to get expert academic advice from a top-tier global scientist and set yourself on the path to a fully funded postgraduate (MS/PhD) journey!
We highly encourage all students interested in advancing their academic careers to attend.
Session Details:
Date: 24 May 2026
Time: 3:00 PM (BD Time)
Platform: Zoom
Registration Link: https://forms.gle/MDWWMcJu1meW6peJA
AUST Applied Simulation and Modeling Lab - AASML
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AASML is Ahsanullah University of Science and Technology’s first research hub for computational modeling and simulation, empowering students with mentorship, practical workshops, and research experience
13/05/2026
Ahsanullah University of Science and Technology (AUST) through the AUST Applied Simulation and Modeling Lab - AASML warmly invites students and aspiring researchers to attend an exclusive academic webinar titled “Building Strong Profiles for Higher Studies with Full Funding.”
We are honored to welcome Prof. Dr. Saidur Rahman as our distinguished speaker. He is a globally recognized researcher in renewable energy, nanofluids, and heat transfer, currently ranked 1st in Asia and 7th globally in mechanical engineering by the AD Scientific Index 2025.
In this session, Dr. Rahman will share practical guidance on starting research, building strong academic and technical skills, preparing competitive CVs and SOPs, finding scholarship opportunities, and contacting international professors for MS and PhD positions. He will also discuss common mistakes students should avoid while applying for higher studies abroad.
This webinar will be a valuable opportunity for students who aim to pursue fully funded postgraduate studies (MS/PhD) and strengthen their research profiles. We highly encourage all interested students to join this insightful session.
Session Details:
Date: 24 May 2026
Time: 3:00 PM (BD Time)
Platform: Zoom
Registration Link: https://forms.gle/MDWWMcJu1meW6peJA (session link will be sent via email)
30/04/2026
Beyond the Break: Extending Component Life via Self-Healing Polymers
In the world of biology, healing is a fundamental part of life. When we get a minor cut on our finger, our body starts off with a complex process to sew the tissue back together and eventually fix the integrity of the skin. As in the realm of engineering, materials traditionally were static. When a piece of steel or plastic cracks it is forever and tends to deepen to the breakage.
Self-healing polymers are a novel group of smart materials that have been developed to replicate biological systems. They have the inbuilt capability of detecting and healing the damage of any type including microscopic cracks automatically, not requiring the involvement of the human hand or arm repair kits.
The Challenge of Fatigue in Engineering
The majority of mechanical failures occur not due to one, huge force. Instead, they occur due to fatigue. Think about bending a piece of paper back and forth, the first time nothing breaks, but after some time, the same force of the repetitive stress creates some kinds of tiny tiny cracks. With time such cracks expand until the part breaks.
The same phenomenon is observed with mechanical parts in airplanes, cars and bridges. Each time a part is loaded or unloaded, such as a landing gear landing or a moving blade of a turbine, that part is stressed. Micro-cracks develop during thousands of cycles, which are small and unseen. As these cracks gradually increase, the component is unable to endure the weight in time and bursts, giving rise to sudden and frequent disastrous collapse.
Self-healing polymers can provide us with a groundbreaking solution to this issue by reacting to the cracks as long as they remain microscopic and preventing their subsequent growth.
How Do Materials "Heal" Themselves?
Engineering researchers have developed several clever ways to integrate healing capabilities into polymers. While the chemistry is complex, the concepts are remarkably intuitive:
Microcapsule Healing: Imagine there are small, microscopic bubbles filled with a liquid glue (a healing agent) incorporated into the material. Once a crack starts to develop, it tears these capsules. The glue drips into the crack, reacts with the material and hardens, in effect, sealing the wound.
Vascular Networks: This technique is based upon human veins and involves the use of a network of small hollow channels within the component. These channels drive healing forces to any place where damage is detected. This enables repair to be made in the same place many times like the way a blood flows to an injury that comes back.
Intrinsic Reversible Bonding: There are certain polymers that have molecular chains which are sticky. With the subjugated material, the chemical bonding at the site of the crack is programmed to seek each other and rejoin. When some conditions are met, say slight temperature change, the molecules will simply re-zip themselves up again.
Applications in High-Stress Engineering
Self-healing materials are especially critical in the context of fatigue-prone components whose maintenance is not easily achievable or safety is of utmost concern:
Aerospace: Parts such as torque links or fuselage panels vibrate all the time and their pressure varies. Self-healing composites may also greatly increase the life cycle of such parts and minimize the number of costly, tear down inspection cycles.
Automotive: Lightweight polymer engine components or structural frame may be safer and more robust, taking the place of the wear and tear of daily driving, and fixing themselves independently.
Renewable Energy: The turbine blades of wind turbines are exceedingly tricky to service and are additionally in a state of wind fatigue at all times. The concept of self-healing surfaces would avoid erosion and inside cracking thereby ensuring the turbine continues to rotate.
Through these materials, we are heading to a future of positive engineering. We are able to self-maintain structures, which will result in less waste, less expenditure and increased safety of operations than ever before.
The Durability Dilemma: Navigating Strength Recovery in Variable Environments
Regardless of the massive potential, there are challenges. It is currently difficult in the self-healing materials to restore 100 percent of the original strength in the materials once they are repaired and the healing process may take long in extreme temperatures. The direction of future research is the development of polymers that are more flexible in their functionality and thus achieve quicker healing as well as increased load bearing capability. As these technologies continue to develop, we are nearer to a day and age when our machines would be as resilient as the biological systems that inspire them.
Key References
[1] S. Wang, M. W. Urban, and J. C. Gaulding, "Self-healing polymers: Mechanisms and applications in engineering," Progress in Polymer Science, vol. 100, p. 101182, Jan. 2024. https://doi.org/10.1039/C3PY90046K
[2] S. R. White et al., "Autonomic healing of polymer composites," Nature, vol. 409, no. 6822, pp. 794–797, Feb. 2001. https://doi.org/10.1038/35057232
[3] D. G. Bekas, K. Tsirka, D. Baltzis, and A. S. Paipetis, "Self-healing materials: A review of advances in materials, manufacturing, and characterization," Composites Part B: Engineering, vol. 87, pp. 92–119, Feb. 2016. https://doi.org/10.1016/j.compositesb.2015.09.057
[4] Y. Yang and M. W. Urban, "Self-healing polymeric materials," Chemical Society Reviews, vol. 42, no. 17, pp. 7446–7467, Aug. 2013. https://doi.org/10.1039/c3cs60109a
[5] M. D. Hager, P. Greil, C. Leyens, S. van der Zwaag, and U. S. Schubert, "Self-Healing Materials," Advanced Materials, vol. 22, no. 47, pp. 5424–5430, Dec. 2010. https://doi.org/10.1002/adma.201003036
Image Reference:
[6] S. Wang and M. W. Urban, "Self-healing polymers," Nature Reviews Materials, vol. 5, no. 8, pp. 562–583, Aug. 2020. https://doi.org/10.1038/s41578-020-0202-4
Written By,
Fyroze Ripa
3rd year, 2nd semester (ME 24)
BSc in Mechanical Engineering, AUST
Blog link: https://www.aasmlab.org/resources/blog/beyond-the-break-extending-component-life-via-self-healing-polymers
From Feynman’s Vision to Quantum AI: Bridging QM, MD, and Machine Learning for Next-Generation Discovery
Once the great American physicist Richard Feynman said, “Nature isn’t classical… and if you want to make a simulation of nature, you’d better make it quantum mechanical.” The "Quantum Man" said it right. Quantum mechanics is a huge, versatile scope of applied physics. It can bring together molecular dynamics and AI in a single line as well. Just for an example, in modern drug discovery, Quantum Mechanics (QM) and Molecular Dynamics (MD) combined help the investigator to orchestrate the medicine precisely. It can prevent a virus from spreading by analyzing a mixture with a specific protein chain. This modified methodology is known as QM/MM. Here, the quantum part behaves like a microscopic simulator to ensure the electronic mix-up where the drug meets the protein. On the other hand, molecular dynamics deal with the physical constant movement of the thousands of surrounding atoms. For more accuracy, the modern-day wizard, AI, comes to play its role. Many quantum programming languages have been launched, such as IBM's Qiskit, Google's Cirq, and Microsoft's Q #. These languages are able to build QML (Quantum Machine Learning) models, which help to create quantum circuits to analyze molecular interactions. Companies like Volkswagen and DHL are using QML to control the traffic flow. IBM is trying to implement QML in Li-S battery for the best way to store energy. Variational Quantum Eigensolver (VQE) algorithms can predict the energy level of ground-state molecules. Quantum AI is the next powerful uprising tool; it will help researchers accelerate material discovery. By predicting properties and interactions, such as conductivity and stability, faster than ever before, quantum computing will guide us from material simulation to physical-world application at a next-generation level. Quantum computing is more than technology.
References:
[1] Feynman (1982). Simulating Physics with Computers. International Journal of Theoretical Physics.
[2] IBM Quantum. Qiskit Documentation.
https://qiskit.org
[3] Google AI Quantum. Cirq Documentation.
https://quantumai.google/cirq
[4] Microsoft Quantum. Q # Documentation.
https://learn.microsoft.com/quantum
[5] Variational Quantum Eigensolver – Peruzzo et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications.
[6] Quantum Mechanics/Molecular Mechanics – Senn, H. M., & Thiel, W. (2009). QM/MM Methods for Biomolecular Systems. Angewandte Chemie.
[7] Quantum Machine Learning – Biamonte et al. (2017). Quantum Machine Learning. Nature.
Written By,
Mahmud Hasin Azwad
3rd year, 2nd semester
BSc in Mechanical Engineering, AUST
To read the blog, visit: https://www.aasmlab.org/resources/blog/from-feynmans-vision-to-quantum-ai-bridging-qm-md-and-machine-learning
03/04/2026
Join us tomorrow for an insightful technical session on Data-Driven Optimization in Engineering, organized by the AUST Applied Simulation and Modeling Lab (AASML).
Our guest speaker, Asif Karim Khan, will dive deep into integrating Design of Experiments (DOE), Statistical Analysis, and Machine Learning to solve complex engineering challenges.
📅 Event Details
Topic: Data-Driven Optimization in Engineering: Integrating DOE, Statistical Analysis, and Machine Learning
Date: Tomorrow | Saturday, 4th April, 2026
Time: 9:30 PM (BD Time)
Platform: Google Meet (will be sent via email)
Don’t miss out! Secure your spot now to receive the meeting link:
🔗 Registration Link: https://forms.gle/aYPTynHiYQCq6jUL6
Learn more about the event here: https://www.aasmlab.org/recent-events/data-driven-optimization-in-engineering-integrating-design-of-experiments
Event Announcement: Technical Session on Data-Driven Engineering Optimization
The 𝐀𝐔𝐒𝐓 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐋𝐚𝐛 (𝐀𝐀𝐒𝐌𝐋) is pleased to announce an upcoming technical session focused on the intersection of computational modeling, statistical analysis, and experimental design.
Title: 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐃𝐞𝐬𝐢𝐠𝐧 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬, 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬, 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠.
Speaker: Asif Karim Khan
Former Research Assistant, Mechanical Engineering Program (19th Batch), Ahsanullah University of Science and Technology (AUST).
Session Overview
This session will explore the application of modern data-driven methodologies to solve complex engineering optimization problems. The presentation will detail the systematic integration of Design of Experiments (DOE), Taguchi analysis, Response Surface Methodology (RSM), machine learning models, and genetic algorithms.
Additionally, the session will emphasize the critical role of statistical validation in experimental research, specifically covering techniques such as the t-test and Tukey’s Honestly Significant Difference (HSD) for rigorous data analysis.
In this session, the speaker will present insights from his published research paper, “𝐒𝐭𝐫𝐚𝐢𝐧-𝐫𝐚𝐭𝐞-𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐥𝐤𝐚𝐥𝐢 𝐓𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭 𝐢𝐧 𝐒𝐮𝐠𝐚𝐫𝐜𝐚𝐧𝐞 𝐁𝐚𝐠𝐚𝐬𝐬𝐞 𝐅𝐢𝐛𝐞𝐫𝐬 𝐔𝐬𝐢𝐧𝐠 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬,” published in 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐢𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (𝐄𝐥𝐬𝐞𝐯𝐢𝐞𝐫).
His other recent work includes “𝐀𝐧 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐥-𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐟𝐨𝐫 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐒𝐋𝐀 𝟑𝐃 𝐏𝐫𝐢𝐧𝐭𝐢𝐧𝐠 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐯𝐢𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬,” published in 𝐉𝐨𝐮𝐫𝐧𝐚𝐥 𝐨𝐟 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐬 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 (𝐄𝐥𝐬𝐞𝐯𝐢𝐞𝐫).
Objective
This event is designed for students, faculty, and academic researchers seeking a clear, practical understanding of how to synthesize experimental design, statistical evaluation, and machine learning to develop highly optimized engineering solutions.
Event Date & Time:
Date: 4th April, 2026
Time: 9:30 PM (Bangladesh Standard Time)
Venue: Online via Google Meet
Registration & Additional Information
Registration Link: https://forms.gle/Nqi1E32xAyo5BJsL8
[N.B. Register with a valid email to get the session virtual link.]
Full Event Details:https://www.aasmlab.org/recent-events/data-driven-optimization-in-engineering-integrating-design-of-experiments
28/03/2026
The 𝐀𝐔𝐒𝐓 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐋𝐚𝐛 (𝐀𝐀𝐒𝐌𝐋) is pleased to organize a technical session titled “𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐃𝐞𝐬𝐢𝐠𝐧 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬, 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬, 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠.” The session will be conducted by 𝐀𝐬𝐢𝐟 𝐊𝐚𝐫𝐢𝐦 𝐊𝐡𝐚𝐧, a former research assistant in the Mechanical Engineering Program at Ahsanullah University of Science and Technology (ME 19th Batch).
In this session, the speaker will present insights from his published research paper, “𝐒𝐭𝐫𝐚𝐢𝐧-𝐫𝐚𝐭𝐞-𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐥𝐤𝐚𝐥𝐢 𝐓𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭 𝐢𝐧 𝐒𝐮𝐠𝐚𝐫𝐜𝐚𝐧𝐞 𝐁𝐚𝐠𝐚𝐬𝐬𝐞 𝐅𝐢𝐛𝐞𝐫𝐬 𝐔𝐬𝐢𝐧𝐠 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬,” published in 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐢𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (𝐄𝐥𝐬𝐞𝐯𝐢𝐞𝐫).
His other recent work includes “𝐀𝐧 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐥-𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐟𝐨𝐫 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐒𝐋𝐀 𝟑𝐃 𝐏𝐫𝐢𝐧𝐭𝐢𝐧𝐠 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐯𝐢𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬,” published in 𝐉𝐨𝐮𝐫𝐧𝐚𝐥 𝐨𝐟 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐬 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 (𝐄𝐥𝐬𝐞𝐯𝐢𝐞𝐫).
The talk will focus on how modern data-driven approaches can be applied to solve engineering optimization problems by integrating Design of Experiments (DOE), Taguchi analysis, Response Surface Methodology (RSM), machine learning models, and genetic algorithms. The session will also highlight the role of statistical validation techniques such as the t-test and Tukey’s Honestly Significant Difference (HSD) in analyzing experimental results.
This event aims to provide students and researchers with a clear understanding of how experimental design, statistical analysis, and machine learning can work together to develop efficient and optimized engineering solutions.
𝐃𝐚𝐭𝐞: 𝟒𝐭𝐡 𝐀𝐩𝐫𝐢𝐥, 𝟐𝟎𝟐𝟔
𝐓𝐢𝐦𝐞: 𝟗:𝟑𝟎 𝐏𝐌 (via Google Meet)
𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤: https://forms.gle/aYPTynHiYQCq6jUL6
Click here to learn more about the event:
https://www.aasmlab.org/recent-events/data-driven-optimization-in-engineering-integrating-design-of-experiments
20/03/2026
Eid Mubarak! 🌙 ✨
From all of us at the AUST Applied Simulation and Modeling Lab!
This Eid, we hope your joy is maximized, your stress is minimized, and your celebrations run perfectly without a single error! Wishing all a highly successful and peaceful holiday.
Molecular Dynamics Investigation of Ciprofloxacin Adsorption on Carbon Nanotubes
The research article “Adsorption of Ciprofloxacin on Carbon Nanotubes: Insights from Molecular Dynamics Simulations” by Daniele Veclani and Andrea Melchior, published in the Journal of Molecular Liquids, explores the molecular-level interactions between the antibiotic Ciprofloxacin (CIP) and Single-Walled Carbon Nanotubes using molecular dynamics simulations. The study investigates both the neutral and zwitterionic forms of ciprofloxacin and evaluates their adsorption behavior under different environmental conditions. The results demonstrate that ciprofloxacin molecules strongly adsorb onto the nanotube surface primarily through π–π stacking interactions and van der Waals forces, making the adsorption process thermodynamically favorable. These findings highlight the potential of carbon nanotubes as promising nanomaterials for the removal of antibiotic contaminants from aqueous environments.
As part of an ongoing research initiative, a validation study is being conducted to reproduce a portion of the simulation methodology presented in the paper. The focus of this work is the vacuum adsorption stage, specifically examining the interaction between the neutral form of ciprofloxacin and the outer wall of a carbon nanotube. Molecular dynamics simulations are performed using the GROMACS simulation package with the GROMOS 54A7 force field. The system is stabilized at 298.15 K using a V-rescale thermostat, ensuring appropriate thermal control during the simulation process.
This validation effort aims to reproduce the adsorption characteristics reported in the original study and to gain deeper insight into the molecular-scale interaction mechanisms between ciprofloxacin molecules and carbon nanotube surfaces. Such computational investigations contribute to advancing the understanding of nanomaterial-based solutions for environmental remediation, particularly in addressing pharmaceutical pollution in water systems.
Performed by
MD Sadman Shabab Kabir
4th year, 2nd semester (ME-22)
Ahsanullah University of Science and Technology
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