Electrical and Electronic Engineering, Sensory Systems, Materials Science, Environmental Science
19
Scopus Publications
42
Scholar Citations
3
Scholar h-index
Scopus Publications
Sustainable farming systems integrating soil health water use efficiency and crop productivity for climate resilience Kiran Keshyagol, Mohan Vanarotti, Santosh Madiwal, Prasad Kulkarni, Anupkumar M. Bongale Discover Sustainability, 2026 Climate variability and extremes, together with soil degradation, water scarcity, and unstable yields, pose major challenges to global food security and progress towards the Sustainable Development Goals (SDGs) and Land Degradation Neutrality (LDN). This review synthesises evidence on integrated and sustainable farming practices that jointly improve soil organic carbon (SOC) sequestration, water-use efficiency (WUE), agricultural productivity, and resilience to climate shocks. Peer-reviewed literature was identified, screened, and analysed following PRISMA guidelines. The selected studies were classified into four thematic categories: (C1) soil protection and soil health enhancement, (C2) erosion control and water management, (C3) productivity and economic viability, and (C4) emerging digital and technology-driven frameworks. Across agro-ecological settings, conservation tillage, cover cropping, mulching, green manuring, vermicomposting, and biochar application increased SOC stocks and improved soil structure by 10–30% over multi-year periods. Water-management and erosion-control interventions, including contour farming, terracing, mulching, hydrogel application, and optimised irrigation, reduced runoff and soil loss while improving WUE by 15–40%. Diversified and integrated production systems improved yield stability (typically ~ 15–25%) and sand increased yield relative to conventional monocropping. The synthesized comparisons indicate that integrated practice bundles deliver the highest mean gains across core indicators (≈ 25% SOC, ≈ 35% WUE, and ≈ 28% yield over conventional baselines), whereas single-cluster strategies produce more targeted improvements. Adoption remains constrained by upfront costs, infrastructure and training gaps, and uneven policy support. These findings underline the value of integrated practice bundles for advancing SDGs 2, 6, 13 and 15 and LDN targets, and for designing context-specific climate-smart farming systems.
Quantitative assessment of alkali and carbon nanotube reinforcement effects on the tensile reliability of sustainable sisal fiber bio-based epoxy composites Kishor Joshi, Pavan Hiremath, Shivashankarayya Hiremath, D. V. Ghewade, H. M. Vishwanatha, Kiran Keshyagol Scientific Reports, 2026 The present study investigates a two-stage reinforcement strategy to enhance the tensile performance and reliability of sisal fiber–reinforced bio-based epoxy composites, aligning material development with sustainability-driven design principles. In the first stage, sisal fiber mats were treated with 4 wt% and 5 wt% NaOH to improve fiber–matrix interfacial efficiency, while in the second stage, multi-walled carbon nanotubes (MWCNTs) were incorporated into the epoxy matrix at low weight fractions of 0.15, 0.25, and 0.35 wt% using a combined mechanical stirring and ultrasonication approach. Tensile testing conducted in accordance with ASTM D3039 revealed a systematic increase in ultimate tensile strength (UTS) from 71.24 MPa for untreated composites to 103.32 MPa for 5 wt% NaOH-treated composites, corresponding to an improvement of approximately 45% due to enhanced interfacial bonding. Subsequent CNT modification further improved tensile performance, with an optimum response observed at 0.25 wt% MWCNT, achieving a maximum UTS of 129.36 MPa and an elastic modulus of 8.1 GPa. Regression-based mathematical modelling captured the near-linear strengthening behavior induced by alkali treatment and the non-linear saturation-dominated response associated with CNT addition, with model predictions remaining within experimental scatter. Statistical reliability assessment using Weibull analysis demonstrated reduced strength variability for alkali-treated and optimally CNT-modified composites. Fracture surface analysis using scanning electron microscopy revealed a clear transition from interfacial debonding and fiber pull-out to cohesive fracture, crack bridging, and crack deflection mechanisms at optimized reinforcement levels. This study quantifies the combined effect of alkali treatment and low-loading CNTs on sisal bio-epoxy tensile behavior, achieving ~ 82% strength improvement with an optimum at 0.25 wt% CNT, while enhancing stiffness and maintaining controlled variability within the tested range. By integrating renewable natural fibers, low nanofiller content, and data-driven modelling, this study contributes to sustainable materials innovation (SDG 9), responsible material utilization (SDG 12), and reduced environmental impact through lightweight composite design (SDG 13).
Sustainable dielectric materials for energy storage: Processing, properties, and performance evaluation Kiran Keshyagol, Shivashankarayya Hiremath, H.M. Vishwanatha, Pavan Hiremath Materials Today Sustainability, 2026 The growing demand for sustainable electrical and electronic technologies has accelerated the search for environmentally benign dielectric materials with high-performance characteristics suited for applications such as electromagnetic shielding, energy storage, and electroactive devices.. In this work, a naturally extracted dielectric (NED) material derived from cuttlefish bone was processed via lyophilization and thermal calcination at various temperatures to enhance structural consistency. Structural evolution from aragonite-based calcium carbonate to calcium oxide (CaO) was confirmed through X-ray diffraction (XRD) and Fourier-transform infrared (FTIR) spectroscopy. Dielectric behavior and ion transport mechanisms were assessed using electrochemical impedance spectroscopy (EIS). Among all samples, the material calcined at 750 °C (NED-750) demonstrated the best performance, exhibiting strong Maxwell–Wagner interfacial polarization, high permittivity at low-frequency , and a peak DC conductivity of 5.4 × 10 -3 S/m. A reduction of 8.2 % in material density with increasing calcination temperature further indicated enhanced porosity and polarization sites. The correlation of structural data with dielectric response establishes a comprehensive framework for evaluating bio-derived ceramics. These results highlight NED as a promising candidate for next-generation, sustainable dielectric energy storage system and electronic device.
Multi-Scale Tribo–Thermo–Viscoelastic Engineering of Sustainable Bio-Based Epoxy Through Hybrid Carbon Nano Architectures and Energy Partition Modeling Kiran Keshyagol, Pavan Hiremath, Rakesh Sharma, Muralishwara K, Santhosh K, Suhas Kowshik, Nithesh Naik Polymers, 2026 This study investigates the multi-scale tribo–thermo–viscoelastic performance of a sustainable bio-based FormuLITE epoxy reinforced with single and hybrid carbon nanofillers (0.1 wt.% total loading) under dry sliding up to 50 N. Pin-on-disk tests at 10, 30, and 50 N showed a consistent reduction in contact pressure and wear volume in the order: neat epoxy > 0.1 CNT > 0.1 GNP > 0.1 ND > 0.1 CNT/GNP > 0.1 CNT/ND > 0.1 GNP/ND. At 50 N and 1500 m sliding distance, neat epoxy exhibited a wear volume of 13.43 mm3 and contact pressure of 13.4 N/cm2, while the GNP/ND hybrid reduced wear to 4.86 mm3 and contact pressure to 6.2 N/cm2, corresponding to reductions of 64% and 54%, respectively. The accelerating wear coefficient decreased from 2.9 × 10−6 to 8.5 × 10−7, confirming slower damage accumulation in hybrid systems. Time-dependent contact pressure analysis revealed reduced asymptotic intensity and suppressed mid-cycle pressure spikes, indicating enhanced tribolayer stability. Effective surface hardness increased from 0.18 GPa (neat epoxy) to 0.30 GPa (GNP/ND), while normalized wear decreased from 1.00 to 0.36. Enhanced damping behavior and improved thermal conductivity in hybrid systems promoted stress redistribution and minimized flash-temperature localization. An interfacial energy-partition framework calibrated to experimental wear data quantitatively linked effective driving pressure, tribofilm stabilization, and surface hardness to material removal. The results demonstrate that wear mitigation in sustainable bio-epoxy systems is governed by coupled mechanical, viscoelastic, and thermal energy redistribution, with GNP/ND hybrids providing the most stable tribological interface under severe sliding. The findings contribute to the development of durable and sustainable bio-epoxy composite systems for engineering applications, supporting broader goals of responsible material utilization and sustainable industrial innovation aligned with the United Nations Sustainable Development Goals (SDG 9 and SDG 12).
Surface Aware Triboinformatics Framework for Wear Prediction of MWCNT Reinforced Epoxy Composites Using Run-Wise AFM Descriptors and Machine Learning Kiran Keshyagol, Pavan Hiremath, Sushan Shetty, Jayashree P. K., Srinivas Shenoy Heckadka, Suhas Kowshik, Arunkumar H. S. Journal of Composites Science, 2026 Accurate prediction of wear behavior in polymer nanocomposites remains challenging due to the coupled influence of operating conditions and evolving surface morphology. In this study, a surface-aware triboinformatics framework is proposed to predict the dry sliding wear behavior of multi-walled carbon nanotube (MWCNT) reinforced epoxy composites by integrating operating parameters with run-wise atomic force microscopy (AFM) surface descriptors. Wear experiments were conducted using a Taguchi L16 design by varying CNT content (0–0.75 wt.%), applied load (10–40 N), sliding speed (183–458 rpm), and sliding distance (500–1250 m). AFM-derived parameters, including Ra, Rq, Z-range, and surface area difference, were extracted from the worn surface corresponding to each experimental run. Multiple regression-based machine learning models were evaluated using leave-one-out cross-validation, with ensemble-based models providing the best predictive performance (R2 > 0.85 with low RMSE and MAE). Feature importance and partial dependence analyses identified CNT content as the dominant factor controlling wear reduction, followed by Z-range and Ra, highlighting the critical role of surface damage severity. Neat epoxy exhibited a maximum wear loss of 0.444 mg, whereas the 0.75 wt.% CNT composite showed values as low as 0.003 mg under comparable conditions, corresponding to a reduction of approximately 99%. The proposed framework enables mechanistically interpretable wear prediction and supports the design of durable polymer composites, contributing to SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production).
Energy-Aware Tribology of Nanoclay-Reinforced Biobased-Epoxy Integrating Taguchi Optimization, Machine Learning, and Surface Morphology Kiran Keshyagol, Prateek Jain, Pavan Hiremath, Satisha Prabhu, Gurumurthy B M, G. Divya Deepak, Arunkumar H S Journal of Composites Science, 2026 The dry sliding wear behaviour of nanoclay-filled bio-based epoxy composites was systematically investigated using a Taguchi L16 experimental design by varying nanoclay content (0–0.35 wt.%), normal load, sliding speed, and sliding time against an EN24 steel counterface. Wear loss, specific wear rate (SWR), frictional response, thermal rise, and energy-based descriptors were quantified, followed by mathematical and machine-learning (ML) based modelling. The results demonstrate that nanoclay addition significantly improves tribological performance up to an optimal content of 0.25 wt.%, beyond which wear instability increases. Compared with neat epoxy, the 0.25 wt.% nanoclay composite exhibited a reduction in steady-state coefficient of friction from ~0.53 to ~0.42, along with a 25–30% decrease in specific wear rate and the lowest energy-to-wear conversion efficiency, indicating more effective utilization of frictional energy. Taguchi analysis identified normal load as the dominant factor governing wear variation (~68% contribution), followed by sliding speed (~17%), while nanoclay content contributed ~5%. An energy-based wear model showed improved correlation with experimental wear volume (R2 ≈ 0.93) compared to a classical Archard-type formulation. ML prediction using a random forest model with leave-one-out cross-validation achieved an R2 ≈ 0.64 for SWR. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) analyses confirmed a transition from severe abrasive wear in neat epoxy to stable tribofilm formation at 0.25 wt.% nanoclay, followed by heterogeneous debris-mediated wear at higher filler content. The observed reduction in wear loss and frictional energy dissipation supports sustainable materials innovation aligned with SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production), while improved operational efficiency is consistent with SDG 7 (Affordable and Clean Energy).
A Geometry-Driven Modeling Framework for Matrix-Filler Interactions Governing Dielectric Response and Electrical Conductivity in Composite Materials Kiran Keshyagol, Shivashankarayya Hiremath, H. M. Vishwanatha, Pavan Hiremath IEEE Access, 2026 The interaction between filler and matrix materials in a composite structure significantly influences the effective performance of electronic devices employed in capacitive and resistive applications. In this study, a theoretical model is proposed to describe filler–matrix interactions as a function of geometric factors, considering filler alignment along both electrode and non-electrode directions. The effective capacitance and resistance of the composite are examined with respect to filler shape, and orientation. Geometric parameters, including filler length and cross-sectional area, are shown to strongly affect the overall dielectric response and conductive properties of the composite. Inclusions aligned along the electrode direction enhance capacitance while reducing resistivity, with spherical fillers exhibiting a greater effect than cubic fillers due to their geometry. The proposed model is benchmarked against classical effective medium theories, demonstrating that filler orientation can be used to tune dielectric and conductivity response. Electrical parameters can be tailored by varying filler geometry and alignment within the matrix. This new model provides a foundation for designing advanced composite materials, where filler orientation can be precisely controlled using modern fabrication approaches such as 3D printing, enabling improved performance in electrical and electronic device applications.
A Review on Dielectric Materials and Composites: Polarisation Effects, Synthesis Methods and Applications Kiran Keshyagol, Shivashankarayya Hiremath, Vishwanatha HM, Pavan Hiremath Iet Nanodielectrics, 2026 Dielectric materials are key elements in modern electronic applications such as sensors, actuators and communication systems. This review consolidates the importance of dielectric polarisation effects, dielectric parameters, synthesis methods, matrix–filler interactions and emerging applications based on dielectric composite materials. This study provides a structured overview by identifying critical aspects of dielectric research, with emphasis on polarisation behaviour, synthesis approaches and practical applications. The interaction of electronic materials produces different polarisation effects that help in evaluating the dielectric nature of composites. A major factor in dielectric materials is the influence of energy storage capability with minimal electric field loss. The Preferred Reporting Items for Systematic reviews and Meta‐Analysis (PRISMA) flow has been used to identify keywords and consolidate the most influential parameters of dielectric systems. Metallic, ceramic and carbon‐based fillers are highly promising for tailoring the properties of dielectric composites. Recent studies have focused on the effects of the matrix, filler type, frequency, dielectric permittivity and dielectric loss. Furthermore, different synthesis methods are discussed in a simplified manner, highlighting the potential of advanced approaches, such as printing technologies, for preparing dielectric composites. Some composite systems demonstrate property tailoring according to research needs, offering reduced dielectric losses and improved permittivity for enhanced energy storage. Overall, dielectric composites have emerged as highly promising candidates for future applications, potentially as replacements for conventional dielectric materials.
Artificial intelligence embedded platform for battery management and autonomous navigation in a prototype electric vehicle Kiran Keshyagol, Sandeep Kulkarni Discover Sustainability, 2025 Electric Vehicle (EV) platforms require tight integration of energy management and perception to operate safely and efficiently on low-cost hardware. This work presents the prototype for an embedded, Artificial Intelligence (AI) driven framework that co-designs a microcontroller-based battery management system with a lightweight Convolutional Neural Network (CNN) for object detection and closed-loop actuation. The Battery Management System (BMS) performs real-time voltage, current, and temperature acquisition with differential sensing and implements a Recursive Least Squares (RLS) – Open Circuit Voltage (OCV) hybrid estimator for state-of-charge (SoC), cell-balancing control, and thermal safeguards. The perception module executes on an embedded processor and feeds an actuation layer that enables adaptive braking and speed control. Bench and on-vehicle tests demonstrate the SoC estimation error ≤ 2% under dynamic drive profiles, effective thermal regulation with cell-to-cell voltage dispersion constrained to ≤ 0.05 V during balancing, and object-detection accuracy of ~ 95% with typical end-to-end inference latency of 40–60 ms. The integrated system reduces computational and cost overheads while maintaining robustness across varying lighting and target distances. By improving battery safety, energy utilization, and perception-to-actuation timing on affordable hardware, the framework advances practical, sustainable EV prototyping aligned with Sustainable Development Goals (SDG) 7, 9, 11, and 13.
Parametric study on the pyroelectric response of LiNbO3 sensor for laser light: a geometrical and electrical perspective Kiran Keshyagol, Prasad Kulkarni, Santosh Madiwal Engineering Research Express, 2025 This study presents a simulation-driven investigation into the optimization of a lithium niobate (LiNbO3)-based pyroelectric sensor for pulsed laser detection. Using COMSOL Multiphysics 6.0, a transient multiphysics model was developed to analyze the sensor’s thermal and electrical responses under a 1 s laser energy flux of 500 W m−2. The effects of geometrical parameters, including crystal thickness and disk radius, as well as the external load resistance, were systematically evaluated. A time-dependent energy input profile was implemented as a step-function laser pulse, and its impact on voltage, current, power, and temperature profiles was examined in detail. The study revealed that increasing the disk radius from 1 mm to 5 mm enhanced the charge-generating area and improved output performance, albeit at the cost of slower thermal decay. Similarly, increasing the crystal thickness from 0.01 mm to 0.04 mm significantly improved voltage sensitivity, reaching up to 2.0 μV K−1, while also minimizing temperature gradients across the crystal. Electrical response analysis showed that higher load resistances increased output voltage and reduced current, with peak power output (12.18 fW) observed at 500 MΩ, indicating optimal impedance matching. The thermal behavior was largely unaffected by electrical loading, confirming that it is primarily governed by the material’s physical properties and the applied laser energy. Additionally, the comparison of thermal and electrical time constants illustrated how sensor performance can shift from thermally to electrically limited, depending on design parameters. The study provides detailed insights into the sensor’s operational dynamics and establishes an optimized design space. A configuration of 5 mm radius, 0.04 mm thickness, and 500 MΩ load resistance offers a promising balance of signal amplitude, sensitivity, and temporal stability. These findings offer valuable guidance for the design and future experimental realization of high-performance pyroelectric sensors.
Cost Effective Solution for Energy Meter Calibration Santosh Madiwal, Prasad Kulkarni, Kiran Keshyagol, Deepak Jagtap, Aishwarya Dandekar, Disha Ved 2025 International Conference on Future Technologies Icft 2025, 2025
Naturally derived nano-ceramic composites with enhanced dielectric performance for electronics applications K Keshyagol, S Hiremath, HM Vishwanatha, P Hiremath, TW Kim Discover Applied Sciences , 2026 2026
Multi-Scale Tribo–Thermo–Viscoelastic Engineering of Sustainable Bio-Based Epoxy Through Hybrid Carbon Nano Architectures and Energy Partition Modeling K Keshyagol, P Hiremath, R Sharma, M K, S K, S Kowshik, N Naik Polymers 18 (6), 752 , 2026 2026 Citations: 1
Geometry Driven Modeling Approach to Matrix-Filler Interactions for Dielectric Response and Electrical Conductivity of Composite Materials K Keshyagol, S Hiremath, HM Vishwanatha IEEE Access , 2026 2026
Quantitative assessment of alkali and carbon nanotube reinforcement effects on the tensile reliability of sustainable sisal fiber bio-based epoxy composites K Joshi, P Hiremath, S Hiremath, DV Ghewade, HM Vishwanatha, ... Scientific Reports , 2026 2026 Citations: 1
Sustainable farming systems integrating soil health water use efficiency and crop productivity for climate resilience K Keshyagol, M Vanarotti, S Madiwal, P Kulkarni, AM Bongale Discover Sustainability 7 (1) , 2026 2026 Citations: 1
Surface Aware Triboinformatics Framework for Wear Prediction of MWCNT Reinforced Epoxy Composites Using Run-Wise AFM Descriptors and Machine Learning K Keshyagol, P Hiremath, S Shetty, JP K, S Shenoy Heckadka, S Kowshik, ... Journal of Composites Science 10 (2), 113 , 2026 2026
Energy-Aware Tribology of Nanoclay-Reinforced Biobased-Epoxy Integrating Taguchi Optimization, Machine Learning, and Surface Morphology K Keshyagol, P Jain, P Hiremath, S Prabhu, G BM, GD Deepak, A HS Journal of Composites Science 10 (2), 98 , 2026 2026 Citations: 1
Predictive Maintenance of Three-Phase Induction Motors Using AI and Machine Learning: A Smart Industry 4.0 Framework K Keshyagol, G Chougule, P Kulkarni, S Madiwal MDPI , 2026 2026
Energy-Aware Tribology of Nanoclay-Reinforced Biobased-Epoxy Integrating Taguchi Optimization, Machine Learning, and Surface Morphology K Kiran, J Prateek, H Pavan, P Satisha, BM Gurumurthy, DG Divya, ... Journal of Composites Science 10 (2), 98 , 2026 2026
A Geometry-Driven Modeling Framework for Matrix-Filler Interactions Governing Dielectric Response and Electrical Conductivity in Composite Materials K Keshyagol, S Hiremath, HM Vishwanatha, P Hiremath IEEE ACCESS 14, 42050-42063 , 2026 2026
A Review on Dielectric Materials and Composites: Polarisation Effects, Synthesis Methods and Applications K Keshyagol, S Hiremath, V HM, P Hiremath IET Nanodielectrics 9 (1), e70026 , 2026 2026
Sustainable Dielectric Materials for Energy Storage: Processing, Properties, and Performance Evaluation K Keshyagol, S Hiremath, HM Vishwanatha, P Hiremath Materials Today Sustainability, 101281 , 2025 2025 Citations: 1
Artificial intelligence embedded platform for battery management and autonomous navigation in a prototype electric vehicle K Keshyagol, S Kulkarni Discover Sustainability , 2025 2025 Citations: 1
Cost Effective Solution for Energy Meter Calibration S Madiwal, P Kulkarni, K Keshyagol, D Jagtap, A Dandekar, D Ved IEEE International Conference on Future Technologies-2025, 1-6 , 2025 2025
Parametric study on the pyroelectric response of LiNbO 3 sensor for laser light: a geometrical and electrical perspective K Keshyagol, P Kulkarni, S Madiwal Engineering Research Express 7 (3), 035405 , 2025 2025
Advances in Sustainable Soil Health Restoration through Chemical Biological Physical Integrated and Nano Remediation Techniques K Keshyagol, US Prabhu, P Hiremath, BM Gurumurthy, YM Shivaprakash, ... Journal of Sustainability Research 7 (3) , 2025 2025 Citations: 3
Finite element analysis of a high-sensitivity capacitive sensor with varied geometry of dielectric media K Keshyagol, S Hiremath, HM Vishwanatha, P Hiremath AIP Conference Proceedings 3299 (1), 050006 , 2025 2025
Enhancement of Mechanical and Tribological Properties of MWCNT-Reinforced Bio-Based Epoxy Composites Through Optimization and Molecular Dynamics Simulation P Hiremath, YM Shivaprakash, K Keshyagol, S Kowshik, BM Gurumurthy, ... Journal of Composites Science 9 (4), 176 , 2025 2025 Citations: 3
Investigation of the capacitance and energy storage properties of PDMS-barium titanate composites using numerical method K Keshyagol, S Hiremath, V HM, P Hiremath Interactions 245 (1), 377 , 2024 2024 Citations: 3
Numerical Simulation Analysis of a Capacitive Pressure Sensor for Wearable Medical Devices K Keshyagol Engineering Proceedings 82 (1), 14 , 2024 2024 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Analysis of polymer-ceramic composites performance on electrical and mechanical properties through finite element and empirical models K Keshyagol, S Hiremath, V HM, PK Rao, P Hiremath, N Naik Materials 17 (15), 3837 , 2024 2024 Citations: 9
Optimizing Capacitive Pressure Sensor Geometry: A Design of Experiments Approach with a Computer-Generated Model K Keshyagol, S Hiremath, V HM, A Kini U, N Naik, P Hiremath Sensors 24 (11), 3504 , 2024 2024 Citations: 9
Estimation of Energy Storage Capability of the Parallel Plate Capacitor Filled with Distinct Dielectric Materials K Keshyagol, S Hiremath, V HM, P Hiremath Engineering Proceedings 59 (1), 95 , 2023 2023 Citations: 8
Advances in Sustainable Soil Health Restoration through Chemical Biological Physical Integrated and Nano Remediation Techniques K Keshyagol, US Prabhu, P Hiremath, BM Gurumurthy, YM Shivaprakash, ... Journal of Sustainability Research 7 (3) , 2025 2025 Citations: 3
Enhancement of Mechanical and Tribological Properties of MWCNT-Reinforced Bio-Based Epoxy Composites Through Optimization and Molecular Dynamics Simulation P Hiremath, YM Shivaprakash, K Keshyagol, S Kowshik, BM Gurumurthy, ... Journal of Composites Science 9 (4), 176 , 2025 2025 Citations: 3
Investigation of the capacitance and energy storage properties of PDMS-barium titanate composites using numerical method K Keshyagol, S Hiremath, V HM, P Hiremath Interactions 245 (1), 377 , 2024 2024 Citations: 3
Multi-Scale Tribo–Thermo–Viscoelastic Engineering of Sustainable Bio-Based Epoxy Through Hybrid Carbon Nano Architectures and Energy Partition Modeling K Keshyagol, P Hiremath, R Sharma, M K, S K, S Kowshik, N Naik Polymers 18 (6), 752 , 2026 2026 Citations: 1
Quantitative assessment of alkali and carbon nanotube reinforcement effects on the tensile reliability of sustainable sisal fiber bio-based epoxy composites K Joshi, P Hiremath, S Hiremath, DV Ghewade, HM Vishwanatha, ... Scientific Reports , 2026 2026 Citations: 1
Sustainable farming systems integrating soil health water use efficiency and crop productivity for climate resilience K Keshyagol, M Vanarotti, S Madiwal, P Kulkarni, AM Bongale Discover Sustainability 7 (1) , 2026 2026 Citations: 1
Energy-Aware Tribology of Nanoclay-Reinforced Biobased-Epoxy Integrating Taguchi Optimization, Machine Learning, and Surface Morphology K Keshyagol, P Jain, P Hiremath, S Prabhu, G BM, GD Deepak, A HS Journal of Composites Science 10 (2), 98 , 2026 2026 Citations: 1
Sustainable Dielectric Materials for Energy Storage: Processing, Properties, and Performance Evaluation K Keshyagol, S Hiremath, HM Vishwanatha, P Hiremath Materials Today Sustainability, 101281 , 2025 2025 Citations: 1
Artificial intelligence embedded platform for battery management and autonomous navigation in a prototype electric vehicle K Keshyagol, S Kulkarni Discover Sustainability , 2025 2025 Citations: 1
Numerical Simulation Analysis of a Capacitive Pressure Sensor for Wearable Medical Devices K Keshyagol Engineering Proceedings 82 (1), 14 , 2024 2024 Citations: 1
Naturally derived nano-ceramic composites with enhanced dielectric performance for electronics applications K Keshyagol, S Hiremath, HM Vishwanatha, P Hiremath, TW Kim Discover Applied Sciences , 2026 2026
Geometry Driven Modeling Approach to Matrix-Filler Interactions for Dielectric Response and Electrical Conductivity of Composite Materials K Keshyagol, S Hiremath, HM Vishwanatha IEEE Access , 2026 2026
Surface Aware Triboinformatics Framework for Wear Prediction of MWCNT Reinforced Epoxy Composites Using Run-Wise AFM Descriptors and Machine Learning K Keshyagol, P Hiremath, S Shetty, JP K, S Shenoy Heckadka, S Kowshik, ... Journal of Composites Science 10 (2), 113 , 2026 2026
Predictive Maintenance of Three-Phase Induction Motors Using AI and Machine Learning: A Smart Industry 4.0 Framework K Keshyagol, G Chougule, P Kulkarni, S Madiwal MDPI , 2026 2026
Energy-Aware Tribology of Nanoclay-Reinforced Biobased-Epoxy Integrating Taguchi Optimization, Machine Learning, and Surface Morphology K Kiran, J Prateek, H Pavan, P Satisha, BM Gurumurthy, DG Divya, ... Journal of Composites Science 10 (2), 98 , 2026 2026
A Geometry-Driven Modeling Framework for Matrix-Filler Interactions Governing Dielectric Response and Electrical Conductivity in Composite Materials K Keshyagol, S Hiremath, HM Vishwanatha, P Hiremath IEEE ACCESS 14, 42050-42063 , 2026 2026
A Review on Dielectric Materials and Composites: Polarisation Effects, Synthesis Methods and Applications K Keshyagol, S Hiremath, V HM, P Hiremath IET Nanodielectrics 9 (1), e70026 , 2026 2026
