About Workshop
This three-day workshop, with 1.5-hour sessions each day, introduces participants to ZnO nanoparticles, structural and morphological characterisation, photocatalytic dye degradation, kinetic analysis, and machine learning-based performance prediction. Through focused lectures and practical demonstrations using tools such as VESTA, ImageJ, Python, pandas, scikit-learn, Materials Project, and Google Colab, participants will develop practical skills relevant to nanotechnology, materials science, environmental remediation, and data-driven materials research.
Aim
To provide participants with practical knowledge of ZnO nanoparticle characterisation, photocatalytic performance analysis, and machine learning techniques for predicting and optimising material properties.
What Participants Will Learn
- Understand the structural, morphological, optical, and defect-related properties of ZnO nanoparticles.
- Explore adsorption and photocatalytic mechanisms involved in dye degradation.
- Analyse experimental photocatalytic data using kinetic models and computational tools.
- Apply Python-based data analysis and machine learning techniques to materials datasets.
- Evaluate photocatalyst performance using experimental and predictive indicators.
- Develop basic machine learning models for material property and photocatalytic efficiency prediction.
Structure
Workshop Structure
📅 Day 1: ZnO Characterisation, Defect Engineering & Heterojunction Design
Core Objective: Understand how ZnO structure, morphology, optical properties, defects, and heterojunctions influence photocatalytic performance.Topics Covered
- ZnO crystal structure, morphology, and structure–property relationships
- XRD-based phase and crystallite-size analysis
- SEM/ImageJ-based particle-size and morphology analysis
- UV–Vis/DRS analysis and optical band-gap estimation
- Oxygen vacancies, defect engineering, and doping strategies
- ZnO-based Type-II, Z-scheme, and S-scheme heterojunctions
- Preparation of structural and material descriptors for computational analysis
🛠️ Hands-on Lab
Analyse ZnO structural and microscopy data, estimate particle/crystallite size and band gap, and prepare material descriptors for further analysis. Hands-on Workflow: ZnO Structure → XRD/SEM Analysis → Band Gap → Defect & Heterojunction Interpretation → Material Descriptors 🧰 Tools Covered: Materials Project, VESTA, ImageJ, Python, Google Colab📅 Day 2: Emerging Pollutant Degradation & Photocatalytic Kinetic Modelling
Core Objective: Analyse ZnO-mediated pollutant degradation, understand reaction mechanisms, and convert experimental results into an ML-ready dataset.Topics Covered
- Semiconductor photocatalysis and electron–hole mechanisms
- Reactive oxygen species and pollutant-degradation pathways
- Dyes, antibiotics, pharmaceuticals, pesticides, and emerging contaminants
- Effects of pH, catalyst dosage, concentration, irradiation time, and light conditions
- Degradation efficiency and pseudo-first-order kinetic modelling
- Rate-constant estimation and experimental-condition comparison
- Mineralization, catalyst stability, recyclability, and photocorrosion
- Preparation of catalyst, pollutant, and process descriptors for machine learning
🛠️ Hands-on Lab
Process photocatalytic data, calculate degradation efficiency and kinetic parameters, compare operating conditions, and build an ML-ready research dataset. Hands-on Workflow: Experimental Data → Cleaning → Degradation Efficiency → Kinetic Modelling → Rate Constant → Descriptor Preparation → ML Dataset 🧰 Tools Covered: Python, pandas, NumPy, matplotlib, PubChem, WebPlotDigitizer, Google Colab📅 Day 3: Explainable Machine Learning & Photocatalyst Optimization
Core Objective: Build interpretable machine-learning models to predict photocatalytic performance and identify optimized catalyst and experimental conditions.Topics Covered
- Materials informatics and feature engineering for photocatalysis
- Material, pollutant, and experimental descriptors
- Data preprocessing, train-test splitting, and cross-validation
- Random Forest and XGBoost regression models
- Model evaluation using R², MAE, and RMSE
- SHAP-based feature importance and explainable AI
- Prediction of degradation efficiency under new conditions
- Data-driven parameter screening and photocatalyst optimization
- Introduction to inverse design and DFT–ML-assisted catalyst discovery
🛠️ Hands-on Lab
Train and validate ML models, generate SHAP explanations, identify key performance drivers, and screen conditions for improved photocatalytic efficiency. Hands-on Workflow: Photocatalysis Dataset → Feature Engineering → RF/XGBoost → Validation → SHAP → Prediction → Optimization 🧰 Tools Covered: Python, pandas, scikit-learn, XGBoost, SHAP, matplotlib, Google ColabImportant Dates
Registration Ends
4: 30 PM
Workshop Dates
2026-09-10
5:30 PM
5:30 PM
What You Will Gain
Outcomes
- Interpret ZnO crystal structures, nanoparticle morphology, particle size, and common crystal defects.
- Analyse photocatalytic dye degradation data and calculate relevant kinetic parameters.
- Process and visualise experimental materials data using Python and pandas.
- Build and evaluate basic machine learning regression models using scikit-learn.
- Identify important parameters influencing photocatalytic performance.
- Compare ZnO and other photocatalysts using experimental and predicted performance indicators.
- Generate clear graphs and visualisations suitable for research presentations and reports.
- Integrate materials science concepts with data-driven and machine learning approaches.
Who Should Attend
- Graduate Students from chemistry, physics, biotechnology, nanotechnology, materials science, chemical engineering, environmental science, and related disciplines.
- Postgraduate Students seeking practical exposure to nanomaterials, photocatalysis, scientific data analysis, and machine learning.
- PhD Scholars and Researchers working in nanotechnology, photocatalysis, materials science, environmental remediation, energy materials, or computational materials research.
- Academicians and Faculty Members interested in incorporating computational analysis, machine learning, and modern materials tools into teaching and research.
- Industry Professionals working in materials development, nanotechnology, coatings, environmental technologies, chemical processing, R&D, quality analysis, or data-driven materials applications.
Basic familiarity with materials science, chemistry, or nanotechnology is helpful but not mandatory. Prior machine learning experience is not required.