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.
Skills you will gain:
About Program:
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.
Program 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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program 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.