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Experimental Methodology

1.Preparation of Dysprosium Precursor

Dysprosium oxide was used as the starting material for preparing a dysprosium-containing precursor through reaction with nitric acid. The resulting precursor was used in subsequent nanoparticle synthesis.

2.Chemical Precipitation

Dysprosium-based nanoparticles were prepared using a chemical precipitation approach involving controlled reaction conditions. Precipitation conditions, including temperature, pH, stirring, and reagent addition, were considered during synthesis. Iron-doped and manganese-doped Dy₂O₃ formulations were also investigated at selected concentrations to explore the effects of compositional modification.

3.Calcination

The precipitated materials were calcined at 650 °C for 3 hours to facilitate oxide formation and improve the structural properties of the synthesized nanoparticles.

4.Preparation of Dysprosium-Containing Mesoporous Silica Nanoparticles

Mesoporous silica nanoparticles were prepared using a surfactant-assisted synthesis route involving cetyltrimethylammonium bromide (CTAB), ammonia, and tetraethyl orthosilicate (TEOS). Dysprosium precursor incorporation was investigated at selected molar concentrations.

5.Characterization and Antimicrobial Evaluation

The synthesized materials were examined using the characterization techniques available for the study. Selected formulations were also evaluated for antimicrobial activity to investigate their potential functional properties.

        Figure 1: Synthesis of Fe and Mn doped Dy₂O₃ nanoparticles.                                                                          Figure 2: Synthesis of Dy-doped mesoporous silica nanoparticles.                

Experimental Characterization

The synthesized dysprosium-based nanomaterials were characterized using complementary analytical techniques to investigate their particle-size distribution, optical properties, and antibacterial performance in the development of potential MRI contrast agents.

Key Findings

The experimental investigation provided insights into the particle-size characteristics and optical properties of the synthesized dysprosium-based nanomaterials. Antibacterial testing was also conducted to evaluate the effects of selected formulations on bacterial growth. These investigations contributed to understanding the properties of the prepared materials.

Research Significance

This project explored the synthesis and characterization of dysprosium-based nanomaterials, with the broader research objective of investigating their potential for MRI contrast-agent development. The work provided practical experience in nanoparticle synthesis, optical characterization, and functional evaluation, establishing a foundation for further research into lanthanide-based materials for biomedical applications.

Future Research Directions

Further research could investigate the magnetic properties and MRI relaxivity of the synthesized materials to determine their potential for MRI contrast enhancement. Additional studies of colloidal stability, biocompatibility, and biological interactions would also be needed to assess their suitability for biomedical applications.

Project 2: Machine-Learning Prediction of r₂ Relaxivity for Lanthanide-Based MRI Contrast Agents

Project Overview

This project explores machine learning to predict the transverse relaxivity (r2) of lanthanide-based MRI contrast agents using literature-derived data and material descriptors. The research aims to identify material–property relationships and establish a computational framework for screening potential MRI contrast agents.

Research Methodology

1. Data Collection: Compile MRI contrast-agent data from published scientific literature.

2. Data Preprocessing: Clean and standardize the dataset for computational analysis.

3. Feature Engineering: Organize material composition, host matrix, particle size, surface modification, and magnetic field strength into computational descriptors.

4. Machine-Learning Modelling: Develop and compare regression models for (r2)prediction.

5. Model Evaluation: Assess predictive performance using a consistent evaluation workflow.

Research Significance

The project investigates how machine learning can support materials research by predicting MRI-related properties and guiding the computational screening of potential contrast-agent candidates.

Technical Skills

Python · Scikit-learn · Data Preprocessing · Feature Engineering · Machine Learning · Regression Modelling

Research status: Manuscript preparation is ongoing.

Project 3: Computational Design of Upconversion Nanoparticle–Photosensitizer Systems for Potential Glioblastoma Therapy

Project Overview

This project develops a computational screening framework for upconversion nanoparticle–photosensitizer combinations for potential biomedical and photodynamic therapy applications. It integrates materials descriptors and machine-learning approaches to explore candidate formulations relevant to research targeting glioblastoma, IDH-wildtype, CNS WHO grade 4.

Research Methodology

1. Dataset Preparation: Use a published dataset of 190 upconversion nanoparticle host lattices.

2. Photosensitizer Selection: Consider nine photosensitizers for computational screening.

3. Formulation Design: Construct 1,710 host-lattice–photosensitizer combinations.

4. Feature Engineering: Incorporate descriptors related to material composition, optical properties, energy transfer, and quenching.

5. Machine-Learning Screening: Apply computational models to investigate and prioritize candidate formulations.

Research Significance

This research explores how machine learning can accelerate early-stage nanomaterial design and help prioritize promising formulations for future experimental investigation.

Technical Skills

Python · Machine Learning · Feature Engineering · Computational Materials Screening · Dataset Integration · Data Analysis

Research status: Manuscript preparation is ongoing.