@article{ahmed_fleming_hawari_2023, title={Effects of Hydrogen Bonding on Nuclear Data Development of Liquid Anhydrous HF}, volume={284}, ISBN={["*****************"]}, ISSN={["2100-014X"]}, DOI={10.1051/epjconf/202328417003}, abstractNote={Anhydrous Hydrogen Fluoride (HF) at high temperatures and pressures is used to process and manufacture nuclear fuel. As HF is often used directly with uranium, correct neutron thermal scattering cross sections are crucial to criticality safety applications. Classical molecular dynamics (CMD) simulation of the flexible HF system was used to create the thermal scattering law (TSL) and cross sections. The initial 2-site model is used in LAMMPS, and it can not capture the H-bond. To correctly represent the H-bond effects, a second, 3-site model was constructed in GROMACS. The 3-site model handled H-bonds by connecting a massless charge to the molecule. Key model parameters were compared to experimental data to verify the approach and models. To get the normalized VACF, the model was compared using hydrogen and fluorine bond length, density, potential energy, and diffusion coefficient. The phonon DOSs for both models were derived from the normalized VACF. DOSs were used to estimate the TSL (S(α,β)) and neutron thermal scattering cross sections for hydrogen in HF. The TSLs were evaluated using the FLASSH code with the Schofield diffusion model. It was observed that the representation of the hydrogen bonding changes the TSL's diffusional contributions. This is represented in the low energy scattering cross section, where intermolecular binding effects shift the cross section.}, journal={15TH INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY, ND2022}, author={Ahmed, Tanvir and Fleming, N. Colby and Hawari, Ayman I.}, year={2023} }
 @article{afrin_shahruzzaman_haque_islam_hossain_rashid_ahmed_takafuji_rahman_2022, title={Advanced CNC/PEG/PDMAA Semi-IPN Hydrogel for Drug Delivery Management in Wound Healing}, volume={8}, ISSN={["2310-2861"]}, url={https://www.mdpi.com/2310-2861/8/6/340}, DOI={10.3390/gels8060340}, abstractNote={A Semi Interpenetrating Polymer Network (semi-IPN) hydrogel was prepared and loaded with an antibiotic drug, gentamicin, to investigate the wound healing activity of this system. The semi-IPN hydrogel was synthesized by combining natural polymer cellulose nanocrystal (CNC) and synthetic polymer polyethylene glycol (PEG) and poly (N,N′-dimethyl acrylamide) (PDMAA), which was initially added as a monomer dimethyl acrylamide (DMAA). CNC was prepared from locally obtained jute fibers, dispersed in a PEG-NaOH solvent system and then mixed with monomer DMAA, where polymerization was initiated by an initiator potassium persulphate (KPS) and cross-linked by N,N′-methylenebisacrylamide (NMBA). The size, morphology, biocompatibility, antimicrobial activity, thermal and swelling properties of the hydrogel were investigated by different characterization techniques. The biocompatibility of the hydrogel was confirmed by cytotoxicity analysis, which showed >95% survival of the BHK-21, Vero cell line. The drug loaded hydrogel showed antimicrobial property by forming 25 and 23 mm zone of inhibition against Staphylococcus aureus (gram-positive) and Escherichia coli (gram-negative) bacteria, respectively, in antimicrobial analysis. At pH 5.5, 76% of the drug was released from the hydrogel within 72 h, as observed in an in vitro drug release profile. In an in vivo test, the healing efficiency of the drug loaded hydrogel was examined on a mice model with dorsal wounds. Complete healing of the wound without any scar formation was achieved in 12 days, which revealed excellent wound healing properties of the prepared drug loaded semi-IPN hydrogel. These results showed the relevance of such a system in the rapid healing of acute wounds.}, number={6}, journal={GELS}, author={Afrin, Samia and Shahruzzaman and Haque, Papia and Islam, Sazedul and Hossain, Shafiul and Rashid, Taslim Ur and Ahmed, Tanvir and Takafuji, Makoto and Rahman, Mohammed Mizanur}, year={2022}, month={Jun} }
 @article{afrin_islam_ahmed_2021, title={A Meteorology Based Particulate Matter Prediction Model for Megacity Dhaka}, volume={21}, ISSN={["2071-1409"]}, DOI={10.4209/aaqr.2020.07.0371}, abstractNote={ABSTRACT 

Dhaka, the capital of Bangladesh, is one of the megacities in the world with the worst air quality. In this study, we develop statistical models for predicting particulate matter (PM) concentration in ambient air of Dhaka using meteorological and air quality data from 2002 to 2004 of a continuous air quality monitoring station (CAMS). Model for finer fraction of PM (PM2.5) explains up to 57% variability of daily PM2.5 concentration, whereas model for coarser fraction (PM2.5-10) explains up to 35% of its variability, indicating that PM2.5 is influenced more by meteorology than PM2.5-10. Temperature, wind speed, and wind direction account for 94% of total PM2.5 variability explained by the model, while relative humidity contributes to 75% of total PM2.5-10 variability. Inclusion of PM lag effect increases models’ predictive power by 4-16%. In general, our developed models show promising performance in capturing the seasonal variability of Dhaka’s PM concentration, although overestimate the low concentrations during wet season (April to September). We validate these models using a recent dataset (2013-2017) from the same monitoring site, in which modeled PM show strong positive correlations with observed concentrations (r = 0.81 and 0.76 for PM2.5 and PM2.5-10 respectively). Models also exhibit strong predictive power in forecasting PM levels of two other CAMSs in Dhaka. Thus, the developed models have potentials to explain the temporal and spatial variability of daily PM within Dhaka. These models can be helpful to policymakers as they can predict daily PM at any location of Dhaka with reasonable accuracy if daily meteorological data and previous day’s PM concentration are available. The effect of climate change scenarios on air pollution dynamics of Dhaka can also be assessed using these models.}, number={4}, journal={AEROSOL AND AIR QUALITY RESEARCH}, author={Afrin, Sadia and Islam, Mohammad Maksimul and Ahmed, Tanvir}, year={2021}, month={Apr} }