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Publication Metadata only Reliability Assessment using Electrical and Mechanical Characterization of Stretchable Interconnects on Ultrathin Elastomer for Emerging Flexible Electronics System(IEEE, 10-07-2025) Bhatti, Gulafsha; Sharma, Rohit; Kumar, Mekala Girish; Palaparthy, Vinay; Agrawal, Yash; DA-IICT, GandhinagarPublication Metadata only In-House Developed Graphene-Based Leaf Wetness Sensor With Enhanced Stability(IEEE, 01-06-2025) Patle, Kamlesh; Yogi, Pooja; Maru, Devkaran; Palaparthy, Vinay; Moez, Kambiz; Agrawal, Yash; DA-IICT, GandhinagarPublication Metadata only Signal Integrity Analysis of Biodegradable Stretchable Interconnect for Wearable Application(IEEE, 01-07-2025) Bhatti, Gulafsha; Maru, Devkaran; Patle, Kamlesh; Shah, Kinnaree; Palaparthy, Vinay; Agrawal, Yash; DA-IICT, GandhinagarPublication Metadata only Explicit Analytical Model of Stretchable Interconnects for Flexible Electronics System(IEEE, 24-07-2025) Bhatti, Gulafsha; Kumar, Mekala Girish; Sharma, Rohit; Palaparthy, Vinay; Agrawal, Yash; DA-IICT, GandhinagarA printed circuit board (PCB) is one of the strong backbones to execute electronic system designs. Due to fast and reliable communication requirements between integrated circuit and other peripheral components over the PCB, there is a quest for the development of board-level designs and layouts. The advancement in technology has led to inventions from conventional rigid to flexible PCBs or flexible electronics (FE). The conformability of FE circuitry majorly depends upon the stretchable interconnects. An interconnect is the medium through which a signal is transmitted. The characteristic of stretchable interconnects is determined through their electrical and mechanical properties. The analytical model and parasitic extraction of the interconnect for rigid PCB structures have been widely explored earlier. However, the analytical formulation of the stretchable interconnect still remains a challenge and meagerly explored till date. Consequently, in this work, an explicit analytical model for the parasitic extraction of stretchable interconnects, viz., resistance (R), inductance (L), and capacitance (C), under stretching and bending effects has been novelly proposed. Five different interconnect materials have been considered for the analysis. The analytical model results have been validated with the ANSYS EDA tool. It is investigated that the proposed analytical model results are in very close agreement with the ANSYS results for all the considered cases.Publication Metadata only Understanding the Influence of Film Thickness on rGO-Based Flexible Capacitive Leaf Wetness Sensors for In-Situ Agriculture Applications(IEEE, 01-07-2025) Yogi, Pooja; Yadav, Rohit; Kumari, Kusum; Borkar, Hitesh; Roy, Anil; Palaparthy, Vinay; DA-IICT, GandhinagarIntegrated plant disease management is pivotal in abating crop loss. For this purpose, leaf wetness sensors (LWSs) are widely used to measure the leaf wetness duration. This work focuses on fabricating the LWS on flexible polyimide substrates and understanding its sensor transfer characteristics using reduced graphene oxide (rGO) as the sensing film with varying thickness. For this purpose, three different concentrations, viz, 0.001 mg (Device A), 1 mg (Device B), and 10 mg (Device C) of rGO are dispersed in 0.5 mL deionized water, and these are drop-casted on the fabricated LWS. Subsequently, sensor properties such as response, recovery/recovery time, hysteresis, and temperature effects are studied. Initial laboratory readings demonstrate that the fabricated LWS response for devices A, B, and C is 607866%, 6541%, and 780%, sensing area wetness, respectively. Further, the response times for devices A, B, and C are 10, 15, and 6 s, respectively. Interestingly, the recovery times of devices A, B, and C are approximately 15, 16, and 2462 s, respectively. Further, it has been observed that over the temperature range of 30 °C–60 °C, the sensor response changes by 2%, 5%, and 17% for devices A, B, and C, respectively.Publication Metadata only Detection of Small Water Droplets on Flexible Leaf Wetness Sensor Considering Effect of Spatiotemporal Variation(IEEE, 10-07-2025) Yogi, Pooja; Pawar, Avinash D; Khaparde, Priyanka; Garg, Pooja; Kalita, Hemen; Palaparthy, Vinay; DA-IICT, GandhinagarPublication Metadata only Impact of Electrode Patterns Variation on the Response Characteristic of Leaf Wetness Sensors(IEEE, 05-08-2024) Patle, Kamlesh S; Sharma, Neha; Khaparde, Priyanka; Varshney, Harsh; Bhatti, Gulafsha; Agrawal, Yash; Palaparthy, Vinay; DA-IICT, Gandhinagar; Patle, Kamlesh S(202121017); Sharma, Neha (202211051); Varshney, Harsh (202211001); Bhatti, Gulafsha (202021005)Prediction of plant diseases is essential to reduce crop loss. Early disease prediction models have been investigated for this purpose, where data on leaf wetness duration (LWD) is one of the key components. Leaf wetness sensors (LWSs) are used to better understand how foliar wetness affects plant disease cycles and epidemic development. LWS can be fabricated on printed circuit boards (PCBs), where interdigitated electrode patterns are widely used. However, it is important to understand the efficacy of these patterns for in-situ measurements. For this purpose, in this work, we have fabricated three different patterns viz. circular, oval, and rectangular on the PCB and tested their efficacy during lab and field measurements. Lab measurements indicate that the circular patterned LWS offers a sensitivity of about 1600% over the dry-to-wet range, which is about 2 and 1.5 times more than oval and rectangular patterns, respectively. Besides this, circular patterned LWS offers the hysteresis of about 2%, whereas the oval and rectangular patterned LWS show about 3% and 7%, respectively. Field measurement results specify that circular patterned LWS and commercial LWS Phytos 31 indicate the same number of LWD events. However, oval and rectangular patterned LWS shows extra false events.Publication Metadata only Soil Moisture Sensing Properties of the Ti3C2T x Mxene-Based Soil Moisture Sensor on Vadose Zone Soils(ACS Publications, 04-01-2024) Maru, Devkaran; Pani, Jitesh; Borkar, Hitesh; Palaparthy, Vinay; DA-IICT, GandhinagarOne of the crucial variables for accurate irrigation models is soil moisture data. Recent advancement in microsensors has opened avenues to fabricate low-cost and highly sensitive soil moisture sensors for in situ measurements. For these microsensors, sensing films play a pivotal role, considering the selectivity and sensitivity. In this work, we have explored the Ti3C2Tx MXene two-dimensional (2D) nanomaterials as the soil moisture sensor�s sensing film. For this purpose, the interdigitated electrodes (IDEs) have been fabricated on the silicon substrate using the micromanufacturing method. To understand the characteristics of the Ti3C2Tx MXene 2D nanomaterials, X-ray diffraction (XRD) is adopted to confirm the structural analysis. Fourier transform infrared (FTIR) spectroscopy is carried out to identify the exciting chemical bonds for Ti3C2Tx MXene. Scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) analysis are used to study the morphology and elemental composition of Ti3AlC2 MAX and Ti3C2Tx MXene phases, respectively. Further, the sensor transfer function has been studied for three different soil samples, viz., clayey soil, loamy sand soil, and sandy loam soil, under laboratory conditions. The performed measurements indicate that the response of fabricated soil moisture sensors is about 500, 2400, and 1700% for the clayey, loamy sand, and sandy loam soils at 300 Hz, respectively, for the gravimetric water content (GWC) ranging from 1 to 18% GWC. Interestingly, the hysteresis of the fabricated sensor on the loamy sand soil is about �1% GWC, whereas for the sandy loam and clayey soils, it is around �3 and �6% GWC, respectively. Further, the fabricated sensor shows a high selectivity toward the water molecule when compared with the other ions (Cu+, Cd+, Na+, and K+) in the soil samples.Publication Metadata only Investigating an Impact of Leaf Bending Radius and Angle for Flexible Leaf Wetness Sensor(IEEE, 01-03-2024) Khaparde, Priyanka; Patle, Kamlesh S; Borkar, Hitesh; Gangwar, Jitendra; Roy, Anil; Palaparthy, Vinay; DA-IICT, Gandhinagar; Patle, Kamlesh S (202121017)It is pivotal to monitor and examine the plant disease during in situ measurements to abate the crop loss. For this purpose, leaf wetness sensors (LWS) are widely used. However, for the LWS during in situ measurements, operational exposure is always a concern considering the plant growth at different stages. During the plant growth, the stem angle changes and even the leaf canopy bends either inward or outward due to environmental factors or physical trauma. Thus, LWS placed on the leaf canopy may produce erroneous results. In this letter, we have examined the effect of leaf bending radius (outward or inward) and angle (from 0� to 90�) on the flexible LWS fabricated on the polyamide substrates. LWS comprises of interdigitated electrodes (IDEs) having interelectrode spacing 0.05 cm. Fabricated LWS are 3.5 cm long and 1.5 cm wide in dimension. We have used the two LWS viz. one bare IDEs and another with molybdenum disulfide (MoS2) coated LWS. Lab experiments indicated that sufficient wetness remained on the bare IDEs and MoS2-coated IDEs till 40� and 70� of bending angle, respectively. Subsequently, when the LWS are bended outward or inward, bare IDEs and MoS2-coated IDEs retain water molecules till 0.7 and 1 cm, respectively, when bended from its initial length (3.5 cm).Publication Metadata only Identifying the Source of Water on Plant Using the Leaf Wetness Sensor and via Deep Learning-Based Ensemble Method(IEEE, 01-01-2024) Saini, Riya; Garg, Pooja; Kumar, Naveen Chaudhary; Joshi, Manjunath V; Palaparthy, Vinay; Kumar, Ahlad; DA-IICT, Gandhinagar; Garg, Pooja (202021011)Plant disease detection and management is one of the pivotal areas in the agriculture sector, which needs attention to abate crop loss. The recent trends in machine learning and deep learning have played a significant role in reducing crop loss with the help of early plant disease detection. For plant disease detection prior information on soil moisture, ambient temperature, relative humidity, leaf wetness sensor (LWS), rainfall are crucial parameters. In this work, the objective is to identify the source of leaf wetness on the leaf canopy, which can arise due to irrigation, rainfall, or dew. To identify the source of wetness on the leaf canopy, either rainfall or humidity/mist sensors are used, which substantially increases the cost of the system. For this purpose, we have used the LWS, which is deployed in the field and various patterns for the irrigation, rainfall, or dew has been analyzed by using the in-house developed the Internet of Things (IoT)-enabled sensor system. The data collected from the field is used as a learning dataset for the proposed ensemble neural network (NN) developed to identify the source of leaf wetness. Short-time Fourier transform (STFT) has been employed to enhance data representation by transforming numerical data from the LWS into informative images. The provided ensemble model incorporates convolutional NN (CNN) and multilayer perceptron (MLP), which process image and numerical data (ambient temperature, relative humidity, leaf wetness duration, and maximum magnitude of frequency of images) as input. Their outputs combined in an artificial neural network (ANN) sub-model for precise leaf wetness event detection (dew, rainfall, or irrigation). The proposed model achieved an accuracy of 96.13% with average precision, recall, and F1 score for the leaf wetness events is about 84%, 85%, and 83%, respectively.