🚀 Live Demo: https://magnesiumimplantdigitaltwin-96wkxuah2fmz2pbkhphhux.streamlit.app/
Digital Twin platform for biodegradable magnesium implants integrating corrosion analysis, FESEM morphology, FTIR characterization, XRD phase analysis, and AI-assisted implant suitability prediction.
An interactive Streamlit-based Digital Twin framework developed for evaluating biodegradable magnesium implant coatings using experimental corrosion, wettability, adhesion, FESEM, FTIR, and XRD characterization data.
The platform integrates material characterization with data analytics to recommend the most suitable coating condition for biomedical implant applications.
✅ Hydrogen Evolution Analysis
✅ Contact Angle & Wettability Assessment
✅ Adhesion Strength Comparison
✅ Implant Performance Ranking
✅ Digital Twin Recommendation Engine
✅ FESEM Morphology Explorer
✅ FTIR Spectrum Interpretation
✅ XRD Phase Analysis
✅ AI Corrosion Prediction
✅ Implant Suitability Radar Chart
✅ Automated Implant Assessment Report
| Sample | Description |
|---|---|
| Pure Mg | Uncoated Magnesium |
| PMMA 5 Dips | PMMA-coated Magnesium |
| PMMA 10 Dips | PMMA-coated Magnesium |
| PMMA 15 Dips | PMMA-coated Magnesium |
Surface morphology evaluation of coated and uncoated magnesium samples.
Identification of PMMA functional groups and coating confirmation.
Phase identification and structural stability analysis.
Wettability and surface hydrophobicity assessment.
Corrosion performance evaluation in simulated physiological conditions.
The Digital Twin framework combines:
- Corrosion Resistance
- Wettability
- Adhesion Strength
- Surface Morphology
- Structural Stability
to generate:
- Implant Performance Score
- Corrosion Risk Prediction
- Coating Recommendation
- Implant Suitability Assessment
✔ Highest overall implant performance
✔ Excellent corrosion resistance
✔ Balanced hydrophobicity
✔ Strong coating adhesion
✔ Best suitability for biodegradable implant applications
- Python
- Streamlit
- Pandas
- NumPy
- Plotly
- Materials Characterization Data
Magnesium_Implant_Digital_Twin/
│
├── app.py
├── requirements.txt
├── README.md
│
├── data/
│
├── images/
│ ├── FESEM/
│ ├── FTIR/
│ ├── XRD/
│ └── screenshots/
│
└── notebooks/
git clone https://github.com/biotech-py/Magnesium_Implant_Digital_Twin.git
cd Magnesium_Implant_Digital_Twin
pip install -r requirements.txt
streamlit run app.py- Machine Learning-based Corrosion Prediction
- Real-Time Implant Monitoring
- Patient-Specific Digital Twin Models
- Biomedical Decision Support System
- Cloud-Based Implant Analytics
Nirupam Joarder
Biotech | Biomaterials | Data Analytics