Gel-Based Materials for Sensors, Self-Powered Nanogenerators, and Artificial Intelligence Prospects

Dear Colleagues,

In recent years, gel-based materials have emerged as a versatile class of soft materials with unique physicochemical properties. This property makes them promising candidates for next-generation sensors and self-powered nanogenerators. Depending on their composition and functionality, gels can be classified into hydrogels, ionogels, organogels, and hybrid gels. Each class offers distinct advantages in environmental stability, conductivity, and responsiveness to external stimuli. These stimuli include pressure, strain, temperature, and humidity. The application of gel-based materials in sensing devices has expanded significantly due to their ability to mimic biological tissues. Moreover, they exhibit intrinsic responsiveness to mechanical deformation, pH, or temperature variations. These features make them particularly attractive for health monitoring, soft robotics, and human–machine interfaces. Similarly, their incorporation into self-powered nanogenerators, including triboelectric, piezoelectric, and ionic–electronic hybrid systems has enabled energy harvesting from ambient mechanical motions, thereby addressing the growing demand for sustainable energy. This energy generated is a maintenance-free power source for wearable and implantable electronics. Furthermore, the rapid advancement of artificial intelligence (AI) offers transformative potential for optimization. This involves the performance prediction and intelligent control of gel-based sensors and nanogenerators. This enables the design of gels with tailored mechanical, electrical, and energy conversion characteristics. Deep learning models, in particular, facilitate pattern recognition in sensor data. These networks are capable of on-device learning and real-time environmental interaction.

Keeping these points in mind, this Special Issue involves the development of the synergy between gel-based materials and AI-driven analytics. This opens new opportunities for the development of autonomous soft electronic systems. This involves improving real-time signal processing, adaptive calibration, and decision-making in smart sensing networks. In addition, the other key points for this Special Issue are as follows:

• Gel-based materials for energy harvesting, sensors, and AI-driven analytics.
• Self-healing mechanisms, biocompatibility, and structure-property relations within gel-based materials.
• Factors influencing the response time, gauge factors, sensitivity, linearity, stretchability, shape, conformation, and real-time monitoring in gels.
• Factors influencing performances like conversion efficiency in nanogenerators, flexibility, and stiffness of gels,
• Reinforcing factors and efficiency of filler additives, energy coefficients, sustainable and green power source for gels.
• Algorithms in AI prospects such as Random Forest, Gradient Boosting, and Specific Neural Network architectures.
• Machine-learning algorithms that can analyze complex material–property–performance relationships.
• Low-power AI chips that facilitate autonomous, self-sustaining Internet of Things networks.

Dr. Vineet Kumar
Prof. Dr. Sang-Shin Park
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Gels is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• hydrogels, ionogels, organogels, hybrid gels, and soft elastomers
• energy harvesting and conversion efficiency
• tribology and wear fatigue properties
• durability in gels
• self-healing mechanisms
• biocompatibility
• stretchable electronics
• strain sensing
• portable electronics
• medical implants
• tissue engineering
• real-time monitoring
• low-power AI chips
• AI-driven analytics
• machine- and deep-learning algorithms
• artificial neural networking
• Internet of Things
• smart homes

• Gel-Based Materials for Intelligent Sensors and Self-Powered Nanogenerators in Gels (9 articles)