Flexible Sensors from Nanocomposites
IIT Jodhpur
Flexible Sensing Systems for Health, Fluids and Intelligent Structures
We develop flexible and printed sensor systems that combine materials, mechanics, instrumentation and data-driven analysis for real-world applications.
CORE CAPABILITIES
Flexible Sensors
Printed Electronics
Nano-composites
Hierarchical Micro-structures
Machine Learning
Signal Processing
Pressure. Strain. Temperature. Touch.
Screen-printing. patterned sensors.
CNT. Graphene. Metallic & lami-nated thin films.
Laser-engineered surfaces
Classification. Interpretable ML.
FFT. Time-freq analysis. Feature analysis.
LAB FUNDED BY:
SELECTED FUNDED PROJECTS
​*PhD aspirants application link Click Here
FERN team meeting with Director Dr. Abhay Pashilkar at NAL, Bangalore
FERN team demonstrating to Hon'ble Minister, Science and Technology Dr. Jitendra Singh, Delhi
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1. Flexible & Printed Sensor Technologies
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Nanoparticle Synthesis for Thermal Sensing: Substrate-dependent thermal sensing behavior observed in LASiS-synthesized Ag NPs; NTCR on paper opens possibilities for flexible, low-cost thermal sensors.
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Touch-Controlled Assistive Devices: Flexible touch-sensing patches for device control; complex gesture recognition using DTW-heatmaps and CWT-scalograms.
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Strain Monitoring in Aerospace Structures: Flexible sensing skins for monitoring morphing aircraft structures under real-world strain conditions.
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2. Physiological Sensing & Health Monitoring
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Neo-Patch for NICU: Multi-parameter monitoring in neonates using a single patch; integrates temperature, respiration, and heart rate tracking for improved NICU care.
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Skin Temperature Statistical Analysis: Large-scale population study to redefine normal body temperature based on gender, occupation, and health conditions.
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3. Nanomaterials & Nanocomposites
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Nanocomposite Characterization: Visualizing stress transfer mechanisms in bilayer graphene–PDMS nanocomposites to understand mechanical reinforcement.
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Electrospun Fiber Analysis: Predicting PVDF fiber properties like diameter using interpretable machine learning; investigating impact of Taylor cone height.
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4. Data-Driven Materials & ML for Sensing
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ML for Electrospinning: Interpretable machine learning models to predict properties of electrospun fibers based on process parameters and solution properties.
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Gesture Recognition: Time-frequency domain representations (e.g., CWT-scalograms) and dynamic time warping improve accuracy in gesture-based interfaces.
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ONGOING WORK
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