The rapid advancement of electrical and telecommunication facilities has resulted in increasing requirements for the development of electromagnetic interference (EMI) shielding composites. Accordingly, an experimental study was conducted to evaluate the EMI shielding performance of carbon nanomaterial (CNM)-embedded carbon-fiber-reinforced polymer (CFRP) or glass-fiber-reinforced polymer (GFRP) composites. Nine combinations of CNMs and carbon or glass fibers were used to fabricate the composites. The synergistic effects of CNMs on the EMI shielding performance were systematically investigated. The results indicated that plate-type CNMs (i.e., graphene and graphite nanoplatelets) have more prominent effects than fiber-type CNMs (carbon nanofibers). The composites fabricated with CFRP afforded higher EMI shielding than the GFRP-based composites. Among the eighteen samples, 3% CNT-GNP in CFRP composites, which included plate-typed CNM, exhibited the best EMI shielding performances, showing 38.6 dB at 0.7 GHz. This study helps understand the shielding performance of CNM-embedded CFRP and GFRP composites in electrical and telecommunication facilities.

In this study, hybridized carbon nanomaterials (CNMs), such as carbon nanotubes (CNTs)–graphene, CNT–carbon nanofibers (CNFs), or CNT–graphite nanoplatelet (GNP) materials were embedded in glass-fiber-reinforced plastic (GFRP) or carbon-fiber-reinforced plastic (CFRP) composites to obtain electrical/piezoresistive sensing characteristics that surpass those of composites with only one type of CNM. In addition, to quantitatively assess their sensing characteristics, the materials were evaluated in terms of gauge factor, peak shift, and R-squared values. The electrical property results showed that the GFRP samples containing only CNTs or both CNTs and graphene exhibited higher electrical conductivity values than those of other composite samples. By evaluating piezoresistive sensing characteristics, the CNT–CNF GFRP composites showed the highest gauge factor values, followed by the CNT–graphene GFRP and CNT-only GFRP composites. These results are explained by the excluded volume theory. The peak shift and R-squared value results signified that the CNT–graphene GFRP composites exhibited the best sensing characteristics. Thus, the CNT–graphene GFRP composites would be the most feasible for use as FRP composite sensors.

Carbon fiber (CF) exhibit extraordinary properties, such as high specific and tensile strength, high elastic modulus, light weight, and weather resistance, which has led to a rapid increase in the use of CF in sectors such as aerospace, sports equipment, energy storage, automotive, construction, and wind energy applications. However, the increase in CF applications has led to a massive production of CF waste. As CF is non-biodegradable, it results in CF accumulation in landfills. CF waste is a rapidly growing ecological hazard because of its high energy consumption and expensive production methods. The properties of carbon fibers can be preserved even after recycling given the development of recycling technology; therefore, multiple studies have been conducted to demonstrate the effect of recycled carbon fiber (RCF) in different composites such as cement-based composites. This review presents the results of studies conducted on the application of RCF to cement composites and analyzes those results to investigate the effect of RCF on the properties of cement composites such as mechanical properties (compressive strength, flexural strength, and tensile strength), fracture characteristics (fracture toughness and fracture energy), electrical properties, and workability. Overall, the studies demonstrated a positive trend in the application of RCF to cement composites.

Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM models that integrate LSTM with complementary algorithms, including CNN, GRU, ARIMA, and SVM. These hybrid architectures aim to enhance prediction accuracy, integrate diverse data sources, and improve computational efficiency. This study systematically reviews principles, trends, and real-world applications, quantitatively evaluating hybrid LSTM models using performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²), while identifying key study limitations. The case studies considered include traffic management, environmental monitoring, energy forecasting, public health, infrastructure assessment, and urban waste management. For example, hybrid models have achieved substantial accuracy improvements in traffic congestion forecasting, reducing their mean absolute error by up to 29%. Despite the inherent challenges related to structural complexity, interpretability, and data requirements, ongoing research on attention mechanisms, model compression, and explainable AI has significantly mitigated these limitations. Thus, hybrid LSTM models have emerged as vital analytical tools capable of robust spatiotemporal prediction, effectively supporting sustainable urban development and data-driven decision-making in evolving smart city environments.

In this study, cement composites were fabricated with various contents of added nano-silica (NS) and multi-walled carbon nanotubes (MWNTs). The compressive and flexural strengths of the resultant cement composites were examined. To explore the microstructures and MWNT distribution, electrical conductivity tests, and scanning electron microscopy were conducted. In addition, the strength results were analyzed based on thermal analysis and porosity evaluations. The electrical conductivity results indicated that MWNTs were satisfactorily distributed in the cement composites. In the mechanical strength tests, the composite with a 0.6% MWNT and 5% NS content and another with a 0.3% MWNT and 5% NS content yielded enhancements in the compressive and flexural strengths of 17.2% and 52% compared with the control samples, respectively. However, composites containing relatively large amounts of both NS and MWNTs showed degradation in the mechanical strength. The enhancement or degradation of the strength was supported by porosity evaluations and thermal analysis results. In particular, the degradation of the strength due to the incorporation of large amounts of both MWNTs and NS was explained by thermogravimetric analysis, which indicated a limited generation of calcium silicate hydrate (C-S-H) hydration products. The lower generation of C-S-H was likely due to the dense microstructure of MWNT/NS-incorporated cement hindering the reactions between calcium hydroxide and the NS.

This study investigates the mechanical and piezoresistive sensing properties of recycled carbon-fiber-reinforced polymer composites (rCFRPs) for self-sensing applications, which were prepared from recycled carbon fibers (rCFs) with fiber lengths of 6, 12, 18, and 24 mm using a vacuum infusion method. Mechanical properties of the rCFRPs were examined using uniaxial tensile tests, while sensing characteristics were examined by monitoring the in situ electrical resistance under cyclic and low fatigue loads. Longer fibers (24 mm) showed the superior tensile strength (92.6 MPa) and modulus (8.4 GPa), with improvements of 962.1% and 1061.1%, respectively. Shorter fibers (6 mm) demonstrated enhanced sensing capabilities with the highest sensitivity under low fatigue testing (1000 cycles at 10 MPa), showing an average maximum electrical resistance change rate of 0.7315% and a gauge factor of 4.5876. All the composites displayed a stable electrical response under cyclic and low fatigue loadings. These results provide insights into optimizing rCF incorporation, balancing structural integrity with self-sensing capabilities and contributing to the development of sustainable multifunctional materials.