arXiv subjects

Turash Haque Pial

Publications and source records attributed to Turash Haque Pial.

3 recordsLinked to original sources

arXiv2026

Mechanistic Framework for Multicomponent Nanoparticle Assembly: Predicting RNA-lipid and PEI-DNA nanoparticle assembly

The assembly of multicomponent nanoparticles is often kinetically controlled and exhibits strong pathway dependence. Transport, solvent exchange, nucleation/growth, and collision-driven coalescence together determine not only ensemble-averaged properties but also particle-to-particle compositional heterogeneity. Here, we present a computational modeling framework for predicting nanoparticle property distributions by coupling processing conditions, early-stage self-assembly physics, and molecular chemical details with kinetic Monte Carlo (kMC) simulations. The framework combines (i) mixing conditions with solvent-exchange-mediated particle initialization and growth, and (ii) kMC simulations that resolve stochastic collision histories, electrostatics-controlled coalescence, and composition at the level of individual particles. Applied to mRNA lipid nanoparticles, the model predicts size-loading correlations and provides insight into how processing-dependent assembly pathways lead to heterogeneous payload distributions. The kMC simulations further provide merging lineage histories, which explain the emergence of log-normal volume and payload distributions through multiplicative particle-growth pathways. The same framework is also applied to PEI-DNA polyelectrolytic complexation, yielding single-particle-resolved DNA-PEI stoichiometry distributions. The framework and its open-source implementation, FormLNP, provide a process-aware route to predicting and controlling single-particle property distributions across a broad range of multicomponent nanoparticle systems.

cond-mat.soft↗

arXiv2022

Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images

In the modern world, satellite images play a key role in forest management and degradation monitoring. For a precise quantification of forest land cover changes, the availability of spatially fine resolution data is a necessity. Since 1972, NASAs LANDSAT Satellites are providing terrestrial images covering every corner of the earth, which have been proved to be a highly useful resource for terrestrial change analysis and have been used in numerous other sectors. However, freely accessible satellite images are, generally, of medium to low resolution which is a major hindrance to the precision of the analysis. Hence, we performed a comprehensive study to prove our point that, enhancement of resolution by Super-Resolution Convolutional Neural Network (SRCNN) will lessen the chance of misclassification of pixels, even under the established recognition methods. We tested the method on original LANDSAT-7 images of different regions of Sundarbans and their upscaled versions which were produced by bilinear interpolation, bicubic interpolation, and SRCNN respectively and it was discovered that SRCNN outperforms the others by a significant amount.

cs.CV↗

arXiv2020

Atomistic Modelling of Functionally Graded Cu-Ni Alloy and its Implication on the Mechanical Properties of Nanowires

Functionally graded materials (FGM) eliminate the stress singularity in the interface between two different materials and therefore have a wide range of applications in high temperature environments such as engines, nuclear reactors, spacecrafts etc. Therefore, it is essential to study the mechanical properties of different FGM materials. This paper aims at establishing a method for modelling FGMs in molecular dynamics (MD) to get a better insight of their mechanical properties. In this study, the mechanical characteristics of Cu-Ni FGM nanowires (NW) under uniaxial loading have been investigated using the proposed method through MD simulations. In order to describe the inter-atomic forces and hence predict the properties properly, EAM (Embedded atom model) potential has been used. The nanowire is composed of an alloying constituent in the core and the other constituent graded functionally along the outward radial direction. Simple Linear and Exponential functions have been considered as the functions which defines the grading pattern. The alloying percentage on the surface has been varied from 0% to 50% for both Cu-cored and Ni-cored nanowires. All the simulations have been carried out at 300 K. The L/D ratios are 10.56 and 10.67 for Cu-cored and Ni-cored NWs, respectively. This study suggests that Ultimate Tensile Stress and Young's modulus increase with increasing surface Ni percentage in Cu-cored NWs. However, in Ni-cored NWs these values decrease with the increase of surface Cu percentage. Also, for the same surface percentage of Ni in Cu-cored NW, the values are higher in linearly graded FGMs than that in exponentially graded FGMs. While in Ni-cored NWs, exponentially graded FGM shows higher values of UTS and E than those in linearly graded FGM. Thus, grading functions and surface percentages can be used as parameters for modulating the mechanical properties of FGM nanowires.

cond-mat.mtrl-sci↗