Smart Photocatalysis with ZnO: AI, Machine Learning and Sustainable Pollutant Remediation
From ZnO structure and photocatalytic kinetics to data-driven performance prediction, feature importance and catalyst optimization using Python and machine learning.
About This Course
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.
Workshop Objectives
- 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.
Workshop Structure
📅 Day 1: ZnO Photocatalysis & Nanomaterial Characterisation
Focus: Understand ZnO photocatalysis, nanomaterial design, characterization, and structure–performance relationships.
Topics Covered
- Fundamentals of heterogeneous and semiconductor photocatalysis
- ZnO nanostructures, doped ZnO, nanocomposites, and heterojunctions
- Chemical and green synthesis: sol–gel, precipitation, hydrothermal, and microwave-assisted methods
- Visible-light enhancement, defect engineering, and sustainable photocatalyst design
- ZnO characterisation using XRD, UV–Vis, FTIR, SEM/TEM, EDX, BET, photoluminescence, and zeta potential
- Linking structural, optical, morphological, and surface properties with photocatalytic performance
🛠️ Hands-on Lab
Analyse ZnO characterization datasets, calculate crystallite size using the Scherrer equation, determine optical band gap, and compare photocatalyst properties.
Workflow:
ZnO Synthesis → Characterisation → Band Gap → Surface/Structural Analysis → Performance Interpretation
🧰 Tools: Python, Google Colab, Characterisation Datasets
📅 Day 2: Pollutant Degradation & Photocatalytic Kinetic Analysis
Focus: Evaluate ZnO photocatalytic performance against environmental pollutants and quantify degradation kinetics.
Topics Covered
- Photocatalytic remediation of dyes, pharmaceuticals, personal-care pollutants, phenolics, and pesticides
- Photocatalytic experimental design and control conditions
- Effects of catalyst dosage, pollutant concentration, pH, temperature, ionic strength, light intensity, and wavelength
- Dark adsorption, photocatalytic controls, reproducibility, and data quality
- UV–Vis monitoring, calibration curves, degradation efficiency, and mineralization
- COD/TOC assessment and identification of degradation intermediates/pathways
- Pseudo-first-order kinetics, rate constants, and half-life
- Photocatalyst stability, recyclability, and comparative performance analysis
🛠️ Hands-on Lab
Process degradation datasets, generate degradation profiles, calculate kinetic parameters, compare operating conditions, and prepare ML-ready descriptors.
Workflow:
Experimental Data → Degradation Analysis → Kinetics → Performance Comparison → Descriptor Preparation
🧰 Tools: Python, pandas, NumPy, matplotlib, WebPlotDigitizer, Google Colab
📅 Day 3: AI/ML for Smart Photocatalysis & Process Optimization
Focus: Build predictive ML models to understand photocatalytic performance and optimize ZnO degradation conditions.
Topics Covered
- AI/ML applications in photocatalysis and dataset development
- Material, pollutant, and experimental feature engineering
- Data cleaning, missing values, outliers, scaling, normalization, and exploratory analysis
- Decision Trees, Random Forest, Support Vector Regression, Gradient Boosting, and ANN concepts
- Model selection, hyperparameter optimization, and cross-validation
- Model evaluation using R², MAE, MSE, and RMSE
- Feature importance and identification of key photocatalytic parameters
- Prediction of pollutant degradation under new conditions
- AI-based screening and optimization of ZnO photocatalytic conditions
🛠️ Hands-on ML Lab
Develop and preprocess a photocatalysis dataset, train predictive models, evaluate performance, identify important parameters, visualize predictions, and optimize photocatalytic conditions.
Workflow:
Photocatalysis Dataset → Preprocessing → Feature Engineering → ML Models → Validation → Feature Importance → Prediction → Optimization
🧰 Tools: Python, pandas, NumPy, scikit-learn, XGBoost/Gradient Boosting, matplotlib, Google Colab
Who Should Enrol?
- 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.
Important Dates
Registration Ends
September 24, 2026
IST 4: 30 PM
Workshop Dates
September 24, 2026 – September 26, 2026
IST 5:30 PM
Workshop 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.
Fee Structure
Student Fee
₹2199 | $60
Ph.D. Scholar / Researcher Fee
₹3299 | $70
Academician / Faculty Fee
₹3999 | $85
Industry Professional Fee
₹4599 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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