Publications

2. Sensing fuel adulteration level in diesel analyzing the change in shape of ultra-short pulse sent through Hollow Core Photonic Crystal Fiber

Authors: Abdullah Al Mahmud Nafiz, Sheikh Montasir Mahbub, Afra Anika Protiva and Rakibul Hasan Sagor

Journal: The European Physical Journal Plus (Conditional acceptance, 2025)

Journal metrics: Q2 (Springer, Impact Factor 2.9)

DOI: Forthcoming

Summary: This work proposes an innovative method to detect fuel adulteration using HC-PCF by tracking distortion in ultrafast pulse propagation. The study highlights a cost-effective and accurate sensing route for fuel integrity monitoring. It represents a maximum Compression sensitivity of 16% without any significant pulse distortion.

3. Numerical Investigation of an Electrically Tunable Graphene–Based Perfect Metamaterial Absorber in the Terahertz Regime

Authors: Russel Reza Mahmud, Abdur Rahman Akib, Abdullah Al Mahmud Nafiz, Ahmed Afif Rafsan, Md. Faysal Nayan, Shah Md. Salimullah

Journal: Plasmonics (2025)

Journal metrics: Q3 (Springer, Impact Factor 4.3)

DOI: https://doi.org/10.1007/s11468-025-02940-6

Summary: This study demonstrates a tunable metamaterial absorber in the terahertz regime using graphene integration. The results show high absorption efficiency and tunability, paving the way for applications in THz imaging, communication, and photonic device engineering. 

4. Hollow-core PCF-based optofluidic sensor: a soliton pulse-enhanced approach for low refractive index detection

Authors: Russel Reza Mahmud, Abdullah Al Mahmud Nafiz, Ali Ahnaf Hassan, Shah Md. Salimullah

Journal: The European Physical Journal Plus, 140, 533 (2025)

Journal metrics: Q2 (Springer, Cite Score ~2.7, IF: 2.9)

DOI: https://doi.org/10.1140/epjp/s13360-025-06480-0

Summary: This paper proposes a soliton pulse–enhanced sensing scheme in HC-PCF leveraging low refractive index optofluids. The integration of nonlinear soliton dynamics improves detection capability for low refractive index fluids, advancing biophotonic and chemical sensing technologies.

5. Asteroid family classification with machine learning: Investigative analysis of a novel two-step approach for categorizing known small asteroid families⋆.

Authors: Fatin Abrar Shams, Abdullah Al Mahmud Nafiz, Salman Mohosiu Siam, Samiur Rashid Abir, Ratul Mahjabin et. al

Journal: Experimental Astronomy, Volume 59, Article 11 (2025)

Journal metrics: Q2 (Springer, Impact Factor ~2.2)

DOI: https://doi.org/10.1007/s10686-025-09982-y

Summary: This paper proposes a two-step supervised model combining XGBoost and Random Forest to improve classification of small and tiny asteroid families in imbalanced datasets, achieving significantly higher F1 scores and even perfect classification for most families.

6. Ultra-short pulse: A comprehensive way of sensing pure solvents through hollow core photonic crystal fiber sensor

Authors: Sheikh Montasir Mahbub, Abdullah Al Mahmud Nafiz, Afra Anika Protiva and Rummanur Rahad
Journal: Optical Materials, Vol. 156, 116028 (2024)
Journal metrics: Q1 (Elsevier, Impact Factor ~4.2)
DOI: https://doi.org/10.1016/j.optmat.2024.116028
Summary: This work presents solvent sensing using HC-PCF with ultrafast pulses. The results demonstrate the robustness of nonlinear pulse propagation in discriminating solvent properties for chemical and optical sensing applications.

7. Investigation of dual plasmonic material integrated wrench-shaped PCF sensor with broadband resonance for cancer cell and chemical detection

Authors: Ali Ahnaf Hassan, Abdullah Al Mahmud Nafiz, R. R. Mahmud, M. F. Nayan, S. M. Salimullah
Journal: Optik, Volume 318, 172092 (2024)
Journal metrics: Q2 (Elsevier, Impact Factor ~2.8)
DOI: https://doi.org/10.1016/j.ijleo.2024.172092
Summary: This article introduces a novel PCF-based sensor combining dual plasmonic materials with wrench-shaped geometry. Its broad resonance enables detection of cancer cells and chemical analytes with high accuracy, showing strong biomedical and sensing potential.

Conferences

1. An Enhanced Diagnostic Framework for Hepatitis C Prediction Using Machine Learning with Advanced Feature Selection Techniques

Authors: Asif ur Rahman Adib, Asib Rahman Jahin, Abdullah Al Mahmud Nafiz and Asif Newaz

Conference: 2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI)

Publisher: IEEE Xplore
DOI: https://doi.org/10.1109/ESAI62891.2024.10913772

Summary: This paper presents a machine-learning framework for Hepatitis C diagnosis using feature selection (SFS, RFE) and multiple classifiers. The Adaboost–SFS model achieved the best performance with an MCC of 92.6%, offering a robust tool for early detection and healthcare support.