Student-centered Professor with expertise in Mechatronics and Mechanical Engineering and Nanosensors, Finite Element Analysis, Vibration, MEMS . Offering 19-year background supporting students, developing instructional plans and organizing and grading exams and tests. Commended for sustaining effective learning environment through prepared classes and relevant assignments and consistently achieving Research and academic goals.
Research Domain : Nanosensors, Nanocomposites, Functional Nanomaterials, Nonconventional Manufacturing, Dynamics of nanostructures. Robotics & Automation
Investigating the tribological performance of polymer nanocomposites using synergistic effects of graphene oxide and silver nanoparticles Unnati Joshi, Sushila Vadu, Vishal Mehta, Jaivik Pathak, Anand Joshi Discover Mechanical Engineering, 2026 The objective of the proposed study was to evaluate the suitability of GO-Ag-reinforced PEEK hybrid nanocomposites for applications that necessitate remarkable tribological properties. The study focuses upon how the synergistic effect of Ag-GO nanoparticles is affecting the structural and material properties of the proposed nanocomposite. The synergistic effect of GO and Ag nanoparticles embedded within PEEK polymer matrix material is assessed in terms of frictional force and wear rate analysis under a variety of process parameters, including variations in load, track diameter, and speed. The test is carried out by applying the pin-on-disc wear testing approach with multiple nanocomposite compositions. Over the test, the frictional force and wear rate were regularly under observation. The synergistic impact of Ag with GO in the synthesis of nanocomposites produces enhanced characteristics. The polymer nanocomposites reinforced with 5 wt.% GO and 5 wt.% Ag was found to be the most suitable for frictional and wear properties. Based on the used characterisation results, this research offers valuable insights into the development of high-performance, self-lubricating hybrid polymer nanocomposites, which could be advantageous to industries such as aerospace, automotive, and biomedical engineering.
Machine learning-assisted design of a microwave metamaterial absorber using PMMA epoxy/MWCNT/Fe3O4 nanocomposite for X-band applications Prince Jain, Pujita Bhatt, Sanketsinh Thakor, Anand Joshi, Mohamad A. Alawad, Mohammad Tariqul Islam Journal of Science Advanced Materials and Devices, 2026 A PMMA-epoxy nanocomposite reinforced with multi-walled carbon nanotubes (MWCNTs) and Fe3O4 nanoparticles is developed for multiband microwave metamaterial absorbers (MMAs). The hybrid nanocomposite exhibits stable dielectric behavior over a wide frequency range, with low losses at lower frequencies and enhanced dielectric and magnetic losses in the X-band, arising from interfacial polarization and magnetic dipolar effects. With these properties, a compact MMA is designed and demonstrates multiband absorption at 7.71, 7.94, 8.79, 8.89, and 13.30 GHz, achieving an average absorptivity of 96.16% with polarization-insensitive performance. Unlike conventional FR4-based absorbers, the proposed design combines magnetic-dielectric nanocomposite functionality with geometric compactness and angular stability. Furthermore, absorptivity is predicted using machine learning models such as CatBoost, Extra Trees, XGBoost, Random Forest, K-Nearest Neighbors, and an ensemble regressor, which reduce computational cost. The findings show that combining experimentally validated nanocomposites with data-driven optimization is an effective way to design high-performance, multiband absorbers for radar stealth, EMI shielding, and sensing.
Design and machine learning driven optimization of tunable periodic cross-diamond terahertz metamaterial absorber Pujita Bhatt, Prince Jain, Anand Joshi Next Materials, 2026 A tunable absorber is proposed using a periodic cross-diamond (PCD) resonator array integrated with vanadium dioxide (VO 2 ) for terahertz applications. Electromagnetic simulations shows absorption peaks at 2.83 THz and 8.28 THz, with a full-width at half-maximum of 7.43 THz, corresponding to a relative absorption bandwidth of 126.7%. The absorption mechanism is analyzed through magnetic and electric field distributions along with parametric analyses are conducted to assess the effect on the absorber’s performance. To improve design optimization and reduce computational cost, regression-based machine learning (ML) models K-Nearest Neighbors, XGBoost, and Random Forest are employed to predict absorptivity across intermediate frequencies. The KNN model achieves excellent performance with an R 2 of 0.9997 and an MAE of 0.0002, reducing the required CST simulations by nearly 50 % and significantly accelerating the EM design process for terahertz applications.
Experimental and machine learning assisted predictive modelling of tribological performance in TiO2/Al/TiO2 multilayer thin film coated stainless steel Vishal Mehta, Mahendra Singh Rathore, Prince Jain, Anand Joshi, Unnati Joshi Next Materials, 2026 Tri-layer TiO 2 /Al/TiO 2 thin films deposited using radio frequency (RF) and direct current (DC) magnetron sputtering on to stainless steel 304 rod. Effects of RF Power and annealing on physical and tribological properties of coated SS 304 have been investigated. The surface morphology of films was examined by a scanning electron microscope. Cross-sectional SEM analysis performed to obtained the thickness of films, which increased from 176 to 562 nm with RF power. The elemental EDS mapping confirms the coated layer elements. Pin-on-disc tribometer used to evaluate the wear and coefficient of friction (COF) of the coated SS. The results reveal that the film deposited at higher power shows better wear and COF. Furthermore, machine learning models, including Gradient Boosting, LightGBM, XGBoost, and CatBoost, were employed to predict wear and frictional responses based on experimental data. The strong correlation between predicted and observed results validates the robustness of the ensemble models and demonstrates the potential of ML-assisted approaches for optimized the coating parameters and forecasting tribological performance, which demonstrates the potential application in the tribological application.
Machine Learning-Driven Compact Hexa-Band THz Metamaterial Absorber Pujita Bhatt, Prince Jain, Anand Joshi, Tatiana Razuvaeva, Olga Kuznetsova Proceedings of the 17th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering Apeie 2025, 2025
Investigating the tribological performance of polymer nanocomposites using synergistic effects of graphene oxide and silver nanoparticles U Joshi, S Vadu, V Mehta, J Pathak, A Joshi Discover Mechanical Engineering 5 (1), 27 , 2026 2026
Characterization of polymethyl methacrylate-multi-walled carbon nanotubes doped epoxy resins through infrared, ultraviolet and machine learning-enhanced dielectric spectroscopy S Thakor, A Joshi, P Jain, M Tannarana, CR Vaja, B Shingala, P Panchal, ... Journal of Macromolecular Science, Part B 65 (6), 922-935 , 2026 2026 Citations: 6
Machine Learning Assisted Sustainable Biochar-Reinforced Epoxy Nanocomposites for Improved Dielectric Performance M Khan, S Thakor, UA Joshi, P Jain, MS Rathore, A Joshi, CR Vaja Journal of Macromolecular Science, Part B, 1-19 , 2026 2026
Experimental and machine learning assisted predictive modelling of tribological performance in TiO2/Al/TiO2 multilayer thin film coated stainless steel V Mehta, MS Rathore, P Jain, A Joshi, U Joshi Next Materials 11, 101846 , 2026 2026 Citations: 1
Design and machine learning driven optimization of tunable periodic cross-diamond terahertz metamaterial absorber P Bhatt, P Jain, A Joshi Next Materials 11, 101673 , 2026 2026 Citations: 5
Experimental investigation of tribological and dielectric behavior in hybrid polymer nanocomposites for insulated bearings with machine learning assisted optimization U Joshi, S Vadu, N Patel, V Mehta, J Pathak, A Joshi, S Thakor, P Rathi, ... International Journal of Polymer Analysis and Characterization, 1-29 , 2026 2026
Thermo-electric characterization of hydroxyapatite/multi-walled carbon nanotube reinforced polyetheretherketone polymer composites U Joshi, S Das Lala, P Deb, J Pathak, A Joshi, S Thakor, C Vaja Journal of Macromolecular Science, Part B 65 (3), 363-377 , 2026 2026 Citations: 2
Machine learning-assisted design of a microwave metamaterial absorber using PMMA epoxy/MWCNT/Fe3O4 nanocomposite for X-band applications P Jain, P Bhatt, S Thakor, A Joshi, MA Alawad, MT Islam Journal of Science: Advanced Materials and Devices, 101119 , 2026 2026 Citations: 4
Influence of 100MeV Ag7+ ion irradiation on Photoluminescence and Dielectric properties of bilayer structured Au/GeO2 thin films for Optoelectronics applications MS Rathore, AY Joshi, SR Nelamarri Vacuum, 115169 , 2026 2026
Comparative study of catalytic oxidation performance of Ni-Co/Al 2 O 3 and Ni-Co/SiO 2 catalysts for real pharmaceutical wastewater treatment: process optimisation … N Kulshreshtha, VK Sandhwar, U Joshi, A Joshi, M Tannarana, ... International Journal of Environmental Analytical Chemistry, 1-23 , 2026 2026
Optimization of MIG welding parameters for dissimilar metals IS 2062 and SS 304 using response surface methodology H Patel, U Joshi, A Joshi Welding International 40 (1), 22-37 , 2026 2026 Citations: 2
Challenges and limitations in ozone gas sensing M Khan, AY Joshi, S Thakor Ozone Gas Sensing Technologies, 425-454 , 2026 2026
DoE-driven development, characterisation, in-silico ADME, and evaluation of surface functionalized SPIONs for enhanced blood–brain barrier permeability and targeted delivery of … N Kushwaha, A Patel, K Akarte, N Shukla, Z Li, S Patel, V Patel, ... Journal of Nanoparticle Research 28 (1), 15 , 2026 2026 Citations: 1
Hybrid AHP-TOPSIS approach for selecting optimal aluminium matrix in casting of metal matrix composites V Mehta, P Potdar, A Joshi OPSEARCH, 1-21 , 2025 2025 Citations: 1
Advancing machine learning tools for early prediction and clinical diagnosis of pre-eclampsia P Jain, J Saxena, A Joshi, V Gorbachenko, A Kuzmin Pregnancy Hypertension 42, 101269 , 2025 2025 Citations: 1
Machine Learning-Driven Compact Hexa-Band THz Metamaterial Absorber P Bhatt, P Jain, A Joshi, T Razuvaeva, O Kuznetsova 2025 IEEE XVII International Scientific and Technical Conference on Actual … , 2025 2025 Citations: 1
Future of Perovskite Solar Cells and Challenges in Large-Scale Energy Production MS Rathore, AY Joshi, UA Joshi Perovskite Solar Cells: Reshaping the Future Energy Landscape, 249-278 , 2025 2025
Bi-functional carbonaceous hybrid nanocomposites with anticancer and antibacterial potential: synthesis, characterization, and cytotoxicity assessment J Saxena, TK Upadhyay, A Jyoti, U Joshi, A Joshi, MS Rathore, S Thakor, ... Naunyn-Schmiedeberg's Archives of Pharmacology 398 (11), 16059-16080 , 2025 2025
Green Synthesis of Ag-NP from De-fatted Microalgae Biomass for Advancing Bioenergy Applications P Saraswat, J Saxena, A Dey, J Pathak, U Joshi, A Joshi International Conference on Microelectronics, Electromagnetics and … , 2025 2025
Multifunctional SrO₂–Sodium Alginate–L-Arginine Nanocomposite: A Green Approach against Colon Cancer and Pathogenic Microbes G PadmaPriya, A Joshi, A Sachdeva, JK Arun, AAA AlGhamdi, ... Journal of Polymers and the Environment 33 (10), 4378-4394 , 2025 2025 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
A systematic review on powder mixed electrical discharge machining AY Joshi, AY Joshi Heliyon 5 (12) , 2019 2019 Citations: 191
Vibration signature analysis of single walled carbon nanotube based nanomechanical sensors AY Joshi, SP Harsha, SC Sharma Physica E: Low-dimensional Systems and Nanostructures 42 (8), 2115-2123 , 2010 2010 Citations: 134
Comparative analysis of machine learning models for predicting dielectric properties in MoS2 nanofiller-reinforced epoxy composites AD Watpade, S Thakor, P Jain, PP Mohapatra, CR Vaja, A Joshi, ... Ain Shams Engineering Journal 15 (6), 102754 , 2024 2024 Citations: 65
Zeptogram scale mass sensing using single walled carbon nanotube based biosensors AY Joshi, SC Sharma, SP Harsha Sensors and Actuators A: Physical 168 (2), 275-280 , 2011 2011 Citations: 64
Brain computer interface: A review P Prashant, A Joshi, V Gandhi 2015 5th Nirma University International Conference on Engineering (NUiCONE), 1-6 , 2015 2015 Citations: 59
Photoluminescence and antibacterial performance of sol–gel synthesized ZnO nanoparticles MS Rathore, H Verma, SB Akhani, J Pathak, U Joshi, A Joshi, C Prakash, ... Materials Advances 5 (8), 3472-3481 , 2024 2024 Citations: 47
Random forest regression analysis for estimating dielectric properties in epoxy composites doped with hybrid nano fillers B Shingala, P Panchal, S Thakor, P Jain, A Joshi, CR Vaja, RK Siddharth, ... Journal of Macromolecular Science, Part B 63 (12), 1297-1311 , 2024 2024 Citations: 43
Vibration analysis of double wall carbon nanotube based resonators for zeptogram level mass recognition AM Patel, AY Joshi Computational materials science 79, 230-238 , 2013 2013 Citations: 42
Dynamic analysis of a clamped wavy single walled carbon nanotube based nanomechanical sensors AY Joshi, SC Sharma, SP Harsha 2010 Citations: 41
Multi-walled carbon-nanotube-reinforced PMMA nanocomposites: an experimental study of their friction and wear properties V Patel, U Joshi, A Joshi, BK Matanda, K Chauhan, AD Oza, ... Polymers 15 (13), 2785 , 2023 2023 Citations: 39
Comparative analysis of machine learning techniques for predicting wear and friction properties of MWCNT reinforced PMMA nanocomposites P Jain, U Joshi, A Joshi, V Patel, S Thakor Ain Shams Engineering Journal 15 (9), 102895 , 2024 2024 Citations: 36
Strength evaluation of functionalized MWCNT-reinforced polymer nanocomposites synthesized using a 3D mixing approach V Patel, U Joshi, A Joshi, AD Oza, C Prakash, E Linul, RDSG Campilho, ... Materials 15 (20), 7263 , 2022 2022 Citations: 35
Investigating the influence of surface deviations in double walled carbon nanotube based nanomechanical sensors AM Patel, AY Joshi Computational materials science 89, 157-164 , 2014 2014 Citations: 30
Modeling and analysis of a manufacturing system with deadlocks to generate the reachability tree using petri net system AM Patel, AY Joshi Procedia Engineering 64, 775-784 , 2013 2013 Citations: 27
Chaotic response analysis of single-walled carbon nanotube due to surface deviations AY Joshi, SC Sharma, SP Harsha Nano 7 (02), 1250008 , 2012 2012 Citations: 26
Machine learning-driven analysis of dielectric response in polymethyl methacrylate nanocomposites reinforced with multi-walled carbon nanotubes P Jain, S Thakor, A Joshi, KV Chauhan, CR Vaja Journal of Materials Science: Materials in Electronics 35 (20), 1419 , 2024 2024 Citations: 24
Versatile photo-sensing ability of paper based flexible 2D-Sb0. 3Sn0. 7Se2 photodetector and performance prediction with machine learning algorithm K Rawal, PD Devendrabhai, P Pataniya, P Jain, A Joshi, GK Solanki, ... Optical Materials 152, 115547 , 2024 2024 Citations: 24
Vibration response analysis of doubly clamped single walled wavy carbon nanotube based nanomechanical sensors AY Joshi, A Bhatnagar, SP Harsha, SC Sharma 2010 Citations: 23
XGBoost regression analysis of dielectric properties of epoxy resin with inorganic hybrid nanofillers P Panchal, B Shingala, S Thakor, P Jain, CR Vaja, A Joshi, KN Shah, ... Journal of Macromolecular Science, Part B 64 (3), 347-363 , 2025 2025 Citations: 22
Effect of waviness on the dynamic characteristics of double walled carbon nanotubes AM Patel, AY Joshi Nanoscience and Nanotechnology Letters 6 (1), 1-9 , 2014 2014 Citations: 22