AI-Enabled High-Performance Biopolymer Nanocomposites: Modern Tools, Data Interpretation & Application Pathways
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Course Overview
Program Syllabus
Module 1
About This Course
Biopolymer nanocomposites—combining bio-based polymers with nanoscale reinforcements such as nanocellulose, graphene, clays, CNTs, and bio-derived nanoparticles—are emerging as sustainable alternatives to conventional plastics. These materials can achieve enhanced mechanical strength, barrier performance, thermal stability, and multifunctionality. However, optimizing nanocomposite formulations is complex due to the large design space involving polymer chemistry, filler type, dispersion, interfacial interactions, and processing conditions.
This workshop introduces AI-enabled workflows for biopolymer nanocomposite development, showing how experimental data, simulations, and material descriptors can be integrated into predictive models. Participants will explore how machine learning assists in property prediction, formulation screening, performance interpretation, and decision-making for targeted applications such as packaging, coatings, biomedical materials, and water treatment. Dry-lab sessions emphasize data interpretation, model-driven insights, and translation from lab-scale results to application pathways.
Module 2
Aim
This workshop aims to train participants in designing and analyzing high-performance biopolymer nanocomposites using AI- and data-driven approaches. It focuses on how machine learning and data analytics accelerate formulation optimization, property prediction, and performance evaluation. Participants will learn to connect material composition, processing parameters, and nanofiller characteristics with real-world applications. The program bridges advanced materials science with modern computational tools for scalable innovation.
Module 3
Workshop Objectives
- Understand structure–property relationships in biopolymer nanocomposites.
- Learn how AI/ML models predict mechanical, thermal, and barrier properties.
- Interpret experimental and simulated data for nanocomposite optimization.
- Explore formulation screening and decision-making using data-driven tools.
- Translate lab-scale nanocomposite data into application-ready pathways.
Module 4
Day 1: Nanocomposite Foundations
- What makes a nanocomposite “nano”: fillers, interfaces, surface chemistry, percolation
- Common systems: PLA/PBS/PHA/starch/cellulose blends + nanocellulose, MMT/clays, silica, biochar, graphene/CNT , lignin nanoparticles
- Key failure points: agglomeration, weak interface, moisture sensitivity, processing damage
- Characterization roadmap: choosing tests for structure → properties → application
Core microscopy concepts: dispersion, aspect ratio, orientation, interphase - Hands-on: Decision matrix + sample micrograph interpretation.
- Mini task: choose one nanocomposite system and draft a characterization plan (3 structure tests + 3 performance tests).
Day 2: Advanced Characterization Techniques (Data → Insight)
- Morphology & dispersion: SEM/TEM/AFM (what each reveals, common artifacts)
- Structure & crystallinity: XRD/WAXS, DSC (crystallization changes due to nano-fillers)
- Chemistry & interfaces: FTIR/Raman, XPS (overview), surface functionalization verification
- Thermal stability & degradation: TGA, oxidative stability, moisture effects
- Mechanical & viscoelastic: tensile basics + DMA (storage/loss modulus, Tg shift)
- Barrier and transport trends: WVTR/OTR concepts, tortuosity link to dispersion
- Hands-on: Excel templates for plotting/interpretation + “artifact checklist”
- Mini task: interpret a provided dataset (DSC + TGA + tensile)
Day 3: Application Translation + Reporting & Future Trends
- Application mapping: Packaging: barrier + sealability + recyclability/compostability reality, Biomedical, Environmental: membranes/adsorbents, reuse, leaching concerns
- Emerging trends: bio-based nano-fillers, green surface modification, multi-functional nanocomposites (antimicrobial, conductive, sensing), AI/DoE optimization
- Scale-up considerations: melt compounding, masterbatching, process window, QC metrics
- Best practices in reporting: controls, thickness normalization, statistics, reproducibility, clear figures
- Hands-on : 1-page report template + “minimum publishable dataset” checklist
- Mini task: create a 1-page nano-composite insight report (system → method → key data → mechanism → application fit → next steps).
Module 5
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Module 6
Important Dates
Module 7
Registration Ends
02/04/2026
IST 07:00 PM
Module 8
Workshop Dates
02/04/2026 – 02/06/2026
IST 08:00 PM
Module 9
Workshop Outcomes
Participants will be able to:
- Analyze nanocomposite datasets and extract meaningful performance insights.
- Understand how AI models link composition and processing to properties.
- Compare nanofillers and formulations for targeted applications.
- Interpret mechanical, thermal, and barrier data using data-driven logic.
- Propose optimized biopolymer nanocomposite designs for real applications.
Module 10
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Meet Your Mentor(s)
Module 11
Fee Structure
Module 12
Student Fee
₹1799 | $70
Module 13
Ph.D. Scholar / Researcher Fee
₹2799 | $80
Module 14
Academician / Faculty Fee
₹3799 | $95
Module 15
Industry Professional Fee
₹4799 | $110
Module 16
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Module 17
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 18
Publication Opportunity
Get published in a prestigious open-access journal.
Module 19
Centre of Excellence
Become part of an elite research community.
Module 20
Networking & Learning
Connect with global researchers and mentors.
Module 21
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 22
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Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
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AI-Enabled High-Performance Biopolymer Nanocomposites: Modern Tools, Data Interpretation & Application Pathways
Professional Certification Program
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FormatLive + Recorded
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Duration8 Weeks
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CertificationVerified
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