The growing demand for sustainable and distributed energy solutions has driven increasing interest in triboelectric nanogenerators (TENGs) as platforms for energy harvesting and self-powered sensing. Biowaste-based triboelectric nanogenerators (BW-TENGs) represent an attractive strategy by coupling renewable energy generation with waste valorization under the principles of the circular bioeconomy. This review provides a comprehensive overview of BW-TENGs, encompassing fundamental triboelectric mechanisms, material categories, processing and surface-engineering strategies, device architectures, and performance evaluation metrics. A broad spectrum of biowaste resources—including agricultural residues, food and marine waste, medical plastics, pharmaceutical waste, and plant biomass—is critically assessed in terms of physicochemical properties, triboelectric behavior, biodegradability, biocompatibility, and scalability. Recent advances demonstrate that BW-TENGs can achieve electrical outputs comparable to conventional synthetic polymer TENGs while offering additional advantages such as environmental sustainability, mechanical compliance, and multifunctionality. Key application areas, including environmental monitoring, smart agriculture, wearable and implantable bioelectronics, IoT networks, and waste management systems, are highlighted. The review also discusses major challenges limiting large-scale deployment, such as material heterogeneity, environmental stability, durability, and lack of standardization, and outlines emerging solutions involving material engineering, hybrid energy-harvesting architectures, artificial intelligence-assisted optimization, and life cycle assessment frameworks.

Self-powered sensors are critically important for IoT, yet most rely on synthetic polymers that lack environmental sustainability. This work presents a triboelectric nanogenerator (TENG) made from marine biowaste which operates as both an energy generator and humidity sensor. Abalone shell powder (ASP) majorly composed of calcium carbonate (CaCO₃) was used as its tribopositive layer in combination with polytetrafluoroethylene (PTFE) as tribonegative layer. The developed ASP-TENG device generated 410 V peak to peak open-circuit voltage (V_OC) and 2.79 W·m⁻² peak power density at an operating frequency of 4 Hz. These obtained results match or surpass existing biowaste-based TENGs. ASP-TENG efficiently worked as a self-powered humidity sensor because its output voltage decreased steadily from 410 V to 176 V in response to an increase in relative humidity (%RH) from 40% to 80% (decreases of 5.8 V for every 1%RH). The triboelectric charges become screened by water molecules that adsorb onto the porous CaCO₃ surface which leads to faster leakage current. This work demonstrates a sustainable method to create TENGs with multiple functions while developing environmentally friendly sensing systems for environmental tracking and sustainable energy harvesting.

Accurate short-term forecasting of solar photovoltaic (PV) power output is essential for efficient grid integration and energy management, especially given the widespread global adoption of PV systems. To address this research gap, the present study introduces a scalable, interpretable ensemble learning model of PV power prediction with respect to a large PVOD v1.0 dataset, which encompasses more than 270,000 points representing ten PV stations. The proposed methodology involves data preprocessing, feature engineering, and a hybrid ensemble model consisting of Random Forest, XGBoost, and CatBoost. Temporal features, which included hour, day, and month, were created to reflect the diurnal and seasonal characteristics, whereas feature importance analysis identified global irradiance, temperature, and temporal indices as key indicators. The hybrid ensemble model presented has a high predictive power, with an R² = 0.993, a Mean Absolute Error (MAE) = 0.227 kW, and a Root Mean Squared Error (RMSE) = 0.628 kW when applied to the PVOD v1.0 dataset to predict short-term PV power. These findings were achieved on standardized, multi-station, open access data and thus are not in an entirely rigorous sense comparable to previous studies that may have used other datasets, forecasting horizons, or feature sets. Rather than asserting numerical dominance over other approaches, this paper focuses on the real utility of integrating well-known tree-based ensemble techniques with time-related feature engineering to derive real, interpretable, and computationally efficient PV power prediction models that can be used in smart grid applications. This paper shows that a mixture of conventional ensemble methods and extensive temporal feature engineering is effective in producing consistent accuracy in PV forecasting. The framework can be reproduced and run efficiently, which makes it applicable in the integration of smart grid applications.

This study develops a high-performance triboelectric nanogenerator (TENG) through Citrullus lanatus rind powder (CLP) which originates from watermelon waste to generate sustainable power and detect humidity. The SEM and FTIR results showed that CLP contains a natural porous structure and multiple polar functional groups which improve both the charge transfer and retention capabilities. The CLP-TENG device operated in vertical contact–separation mode with PTFE as the counter layer to generate a 255 V open-circuit voltage and 30 µA short-circuit current and 35 µW peak power output at 4 Hz and 20 MΩ load. The device successfully charged a 4.7 µF capacitor to 5 V during a 80 s period and operated low-power electronic devices to prove its ability as a sustainable power source. The device output increased with increasing operating frequency while showing operation stability throughout more than 1000 cycles and seven days of continuous operation. The device demonstrated a strong humidity detection ability through its voltage response which decreased from 250 V to 120 V when the relative humidity rose from 30% to 90%. The research proves that agricultural waste can be transformed into environmentally friendly materials which perform well in green energy systems and environmental monitoring applications.

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies.

Hydrogen produced through electrochemical water splitting is considered one of the most promising energy carriers for achieving a sustainable and carbon-neutral future. However, the practical implementation of water electrolysis remains limited by the sluggish kinetics of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), as well as the high cost and limited availability of conventional noble-metal catalysts. Single-atom catalysts (SACs), which feature isolated metal atoms anchored on suitable supports, have emerged as an attractive class of electrocatalysts owing to their nearly complete atomic utilization, well-defined active sites, and tunable electronic structures. This review provides a comprehensive overview of recent advances in SACs for electrochemical water splitting. The fundamental mechanisms of HER and OER are first discussed, followed by the influence of the unique electronic structure, coordination environment, and metal–support interactions on catalytic performance. Various bottom-up and top-down synthesis strategies, together with advanced characterization techniques for identifying atomically dispersed active sites and elucidating structure–activity relationships, are systematically summarized. Furthermore, recent progress in noble-metal, non-noble-metal, and dual-atom catalysts is critically reviewed, with emphasis on their roles in regulating electronic structure, reaction intermediate adsorption, catalytic activity, HER/OER kinetics, and long-term stability. Finally, the remaining challenges and future perspectives for the scalable and practical application of SACs in water electrolysis are discussed. Overall, this review highlights the potential of SACs to maximize metal utilization while maintaining high electrocatalytic performance and provides valuable insights for the rational design of next-generation electrocatalysts for sustainable hydrogen production.