Attribute

Detail

Format

Recorded Lectures (Self-Paced)

Level

Intermediate

Duration

3 Days

Certification

e-Certification + e-Marksheet

Tools

Python, Machine Learning libraries, QSAR, Graph ML, Bayesian Optimization, Active Learning

About the Co2 Mitigation Course

The rapid rise in atmospheric CO₂ has led to severe climate change impacts worldwide, demanding urgent and innovative solutions. Catalysis and Artificial Intelligence (AI) present a unique convergence—where advanced materials science meets intelligent data-driven optimization—to accelerate breakthroughs in CO₂ mitigation.

This 3-day mentor-led program introduces participants to state-of-the-art techniques for CO₂ capture and conversion, covering sorbents like graphene, MOFs, COFs, and zeolites, alongside catalytic reduction methods ranging from borohydrides to electrocatalysis, photocatalysis, and biocatalysis. AI’s role in predictive catalyst design, process optimization, and simulation will also be discussed, preparing participants to tackle both academic and industrial challenges in carbon management.

Program Highlights

• Comprehensive coverage of Catalysis and Artificial Intelligence (AI) for CO₂ Mitigation from fundamentals to advanced applications

• Hands-on projects and real-world case studies in Nanotechnology

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: Python, Machine Learning libraries, QSAR, Graph ML

• Career-oriented training for academic and professional growth in Nanotechnology

Course Curriculum

Module 1: Foundations of CO₂ Emissions & Capture

Understand climate science basics, including GHGs and radiative forcing.
Explore CO₂ capture methods like absorption, adsorption, and membranes.
Analyze advanced sorbents such as Graphene and Activated Carbon, focusing on pore structure and selectivity.
Introduce AI fundamentals for CO₂ capture, including dataset preparation and baseline ML for adsorption prediction.

Module 2: Advanced Catalytic Conversion & AI Design Principles

Examine advanced materials like MOFs, COFs, Zeolites, and POMs for CO₂ applications.
Investigate catalytic pathways for CO₂ conversion into valuable fuels and chemicals.
Discover diverse catalytic reduction methods: electrocatalysis, photocatalysis, and biocatalysis.
Apply AI techniques for predictive catalyst design, including QSAR/graph ML and rapid candidate ranking.

Module 3: Hands-on AI for Catalyst Optimization

Prepare and clean real-world adsorption and catalyst datasets for machine learning.
Build and evaluate machine learning models using Python, focusing on key metrics and feature importance.
Implement optimization strategies for electrocatalysis, including Bayesian and active learning approaches.
Execute an end-to-end mini-project to shortlist optimal CO₂-reduction candidates.

Tools, Techniques, or Platforms Covered

Python
Machine Learning libraries
QSAR
Graph ML
Bayesian Optimization
Active Learning
Graphene
Activated Carbon
MOFs
COFs

Real-World Applications

Apply Catalysis and Artificial Intelligence (AI) for CO₂ Mitigation skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical Nanotechnology competencies
Solve industry-relevant problems using Catalysis and Artificial Intelligence (AI) for CO₂ Mitigation methodologies and tools
Contribute to open-source projects and collaborative research in Nanotechnology
Prepare for competitive examinations, interviews, and professional certifications in Nanotechnology

Who Should Attend & Prerequisites

Industry-recognized e-Certification + e-Marksheet from NSTC
Hands-on training with practical projects and industrial datasets
Dedicated expert mentorship and doubt resolution

Prerequisites:

Frequently Asked Questions

1. What is the format of this Catalysis and Artificial Intelligence (AI) for CO₂ Mitigation course?

This is an Recorded Lectures (Self-Paced) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of Nanotechnology concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Days. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Nanotechnology. Our mentors are industry experts and experienced professionals.

Enroll in Catalysis and Artificial Intelligence (AI) for CO₂ Mitigation today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Nanotechnology skills that matter.

Brand	NSTC
Format	Recorded Lectures
Duration	3 Days
Level	Intermediate
Domain	Nanotechnology
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	Python, Machine Learning libraries, QSAR, Graph ML, Bayesian Optimization, Active Learning, Graphene, Activated Carbon, MOFs, COFs, Zeolites, POMs, Electrocatalysis, Photocatalysis, Biocatalysis

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