Publications

Authors: Abdullah Al Mahmud Nafiz, Afra Anika Protiva, Mohammed Aster, Sheikh Montasir Mahbub, Rakibul Hasan Sagor and M. Shah Alam
Journal: Optics and Lasers in Engineering, Volume 203, 109812 (2026)
Journal metrics: Q1 (Elsevier, Impact factor ~4.2, Cite score 7.1 ).
DOI: https://doi.org/10.1016/j.optlaseng.2026.109812

Summary: This research endeavor presents a novel Hollow-Core Photonic Crystal Fiber (HC-PCF) sensor designed for dual disease detection, specifically targeting glucose and creatinine levels. The sensor operates across two distinct refractive index (RI) ranges, 1.33–1.41 and 2.589–2.661, enabling precise detection. When silica is used as the background material, the sensor detects glucose level, while chalcogenide glass (As₂S₃) allows for creatinine detection, making it highly suitable for kidney dysfunction monitoring. Utilizing a nonlinearity-enhanced pulse compression approach, the sensor balances nonlinearity and higher-order dispersion (β2 and β3 ), enabling distinct pulse compression for both glucose and creatinine samples. When ultrashort pulses ranging from 1 to 3 picoseconds propagate through the fiber, each disease sample exhibits unique pulse compression, enabling precise detection of glucose and creatinine levels in blood. The sensor demonstrates an impressive compression sensitivity of 24.2% for an input power of 700 W with 3-picosecond pulses, 

Authors: Sheikh Montasir Mahbub, Abdullah Al Mahmud Nafiz and Rakibul Hasan Sagor.
Journal: Materials Today Electronics, Volume 11, 100137 (2025), ISSN 2772-9494.
Journal metrics: Q1 (Elsevier, Impact factor ~7.4, Cite score 6.8 ).
DOI: https://doi.org/10.1016/j.mtelec.2025.100137

Summary: In this research, our team investigated fiber nonlinearity and ultra-short pulse compression in hollow-core photonic crystal fiber for advanced refractive-index sensing. We focused on how negative group velocity dispersion and self-phase modulation enable controlled pulse compression for analytes with refractive indices ranging from 1.40 to 1.45, using a 1550 nm, 1 ps input pulse with 1 kW peak power. A key outcome of the study was the achievement of a minimum compression sensitivity of 11.6%, corresponding to nearly nine-fold pulse compression, along with a maximum power upsurge of 2313.918 W when the analyte refractive index was 1.45 at a 0.3 m fiber length. We also analyzed the critical propagation length near the fission length to determine where efficient, undistorted pulse compression is possible. The work demonstrates a compact and nonlinear-optics-based biosensing approach with potential detection capability for RBC, ETFE, ethylene glycol, low-grade glioma abnormal tissue, hexanol, and chloroform.

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

Journal: The European Physical Journal Plus (2025)

Journal metrics: Q2 (Springer, Impact Factor 2.9)

DOI: https://doi.org/10.1140/epjp/s13360-025-07063-9

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.

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 proposes a compact, electrically tunable graphene–based metamaterial absorber featuring a triangular graphene pattern on a 3 μm ultrathin SiO₂ substrate with integrated gold layers. The proffered graphene meta material absorber (GMMA) operates well within the 5–10 THz range, demonstrating high absorption efficiency at multiple resonant frequencies. The absorption characteristics of the proposed GMMA can be precisely tuned through the alteration of fermi energy of graphene through an externally applied gate voltage, making the device highly adaptable for a multitude of applications. Numerical simulations using Lumerical FDTD reveal four notable absorption peaks at 5.98 THz, 7.12 THz, 8.257 THz, and 9.32 THz, achieving near-perfect absorption with efficiencies of 99.7%, 99.4%, 97.97%, and 92.41%, respectively. 

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.

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.

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.

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: In this work, a unique Photonic Crystal Fiber based on the Localized Surface Plasmon Resonance is designed for multipurpose refractive index (RI) sensing-based applications. The sensor incorporates a novel structural configuration with two slits introduced along the horizontal axis with a hexagonal arrangement of air holes at the core. A distinctive combination of plasmonic materials namely Aluminum doped Zinc Oxide (AZO) and Silver (Ag), combined with a layer of TiO2 is implemented to enable resonance at two separate wavelengths within a particular refractive index. The sensor exhibits resonant conditions at separate wavelengths along the x and y-polarization, which can be used to broaden the scope of analyte detection at a particular RI. Two modes of operation are introduced, of which mode-1 is optimized to achieve the highest Amplitude Sensitivity (AS), and mode-2 is to maximize the Wavelength Sensitivity (WS). The mode-1 is examined for a range of RI from 1.35-1.42, while the other is 1.35-1.41. Using the mode-1 configuration, the sensor displays two high values of AS at each resonant wavelength for each material present. The sensor can showcase an AS of 4641.51 RIU-1 for Ag at RI 1.36, which is the highest value of AS found in literature when Ag is used as plasmonic material. Furthermore, AZO is able to display a high AS of 3204.51 RIU-1 at 1.41 RI. 

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: Hepatitis C is a major global health concern and one of the leading causes of chronic liver disease, liver cirrhosis, and liver cancer worldwide. In this paper, an intelligent machine-learning algorithm-based Hepatitis C diagnosis system has been proposed. A publicly available Hepatitis C dataset has been utilized to develop the prediction framework. The dataset shows a significant imbalance between positive and negative cases, which complicates the classification process, a common challenge in healthcare datasets. Appropriate strategies have been undertaken to achieve a reliable decision framework. Two feature selection techniques, Sequential Feature Selection (SFS) and Recursive Feature Elimination (RFE), were incorporated to identify the most representative set of attributes and improve the system’s diagnostic performance. Six different classifiers were employed for prediction, among which Adaboost with the SFS algorithm achieved the highest MCC score of 92.6%, providing the most effective prediction of Hepatitis C. This framework offers a robust approach for early diagnosis, potentially benefiting patient care and optimizing healthcare resources.