Technologies

Research

16 pages, 4220 KB

Open AccessCommunication

Static Verification of the FA125 Hydraulic Drilling Rig Mast Under a Code-Based Load Combination: A Beam–Shell Finite Element Study

by Andrei Dimitrescu, Claudiu Babiș, Iulian Sorin Munteanu and Sorin Alexandru Fica

Technologies 2026, 14(7), 431; https://doi.org/10.3390/technologies14070431 - 14 Jul 2026

Viewed by 71

Abstract

This paper presents a code-based static verification of the FA125 hydraulic drilling rig mast under its governing design load combination. Unlike the previously published dynamic investigation of the same platform, the present work establishes the baseline static load path, identifies the governing structural [...] Read more.

This paper presents a code-based static verification of the FA125 hydraulic drilling rig mast under its governing design load combination. Unlike the previously published dynamic investigation of the same platform, the present work establishes the baseline static load path, identifies the governing structural members, evaluates the local stress state in the mast-to-support connection plates, and computes the effective safety coefficients. The mixed finite element model integrates the lattice mast, the support frame, and the base assembly, utilizing beam elements for the slender load-bearing members and shell elements for the localized plate-type connection regions. The governing load combination encompasses structural self-weight, maximum hook load (14.90 kN), and the reactive torque transmitted by the drilling head (0.50 kNm). The maximum mast-top displacement was limited to 4.75 mm. The critical beam elements were located within the lateral base-support region, developing peak compressive and tensile stresses of 70.08 MPa and 69.21 MPa, respectively. The highest localized shell-level von Mises stress (23.62 MPa) was concentrated within the mast-to-support interface connection plates. The results mathematically confirm that the existing FA125 steel structure satisfies the active design criteria, providing a distinct static reference map required for subsequent structural optimization, lightweighting, and selective material substitution. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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25 pages, 23465 KB

Open AccessArticle

Enhancing Embedded Systems Education Through Remote Laboratories with Real-Time Firmware Observability

by Fabián García-Vázquez, Héctor A. Guerrero-Osuna, Luis F. Luque-Vega, Jesús Antonio Nava-Pintor, Jorge A. Lizarraga, Salvador Castro-Tapia, Mario Molina-Almaraz, Ana G. Castañeda-Miranda and Carlos A. Olvera-Olvera

Technologies 2026, 14(5), 281; https://doi.org/10.3390/technologies14050281 - 6 May 2026

Viewed by 987

Abstract

Microcontroller programming is a key element in engineering education, enabling students to acquire practical skills in embedded systems. Despite its importance, limited access to physical laboratories at many institutions restricts hands-on learning experiences. Remote laboratories have been introduced as a solution; however, most [...] Read more.

Microcontroller programming is a key element in engineering education, enabling students to acquire practical skills in embedded systems. Despite its importance, limited access to physical laboratories at many institutions restricts hands-on learning experiences. Remote laboratories have been introduced as a solution; however, most rely solely on external visual feedback, which constrains observability and limits analysis of internal program behavior. This paper introduces a remote laboratory for microcontroller education utilizing an STM32 platform within a Cloud–IoT framework. The proposed system allows real-time monitoring of internal firmware variables through compilation artifacts, without requiring direct interaction with traditional debugging tools. A pilot study involving 40 students in a microcontrollers course, who engaged with the system remotely using standard development workflows, was conducted. Usability was measured via the System Usability Scale (SUS), yielding an average score of 75.19 and reflecting high satisfaction. The platform enables real-time observation of internal system states during program execution, facilitating program analysis while maintaining an experience similar to traditional on-site laboratories. These findings indicate that the system offers a robust and scalable alternative for embedded systems education, with improved observability over existing remote laboratory solutions. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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22 pages, 2687 KB

Open AccessArticle

WASO as a Stage-Resolved Window for Detectable HRV Differences in Paradoxical Insomnia

by Ye Eun Kong, A Hyun Jung and Se Dong Min

Technologies 2026, 14(3), 192; https://doi.org/10.3390/technologies14030192 - 22 Mar 2026

Viewed by 812

Abstract

Paradoxical insomnia (PI) is characterized by a discrepancy between subjective sleep complaints and objectively preserved sleep, yet its autonomic mechanisms remain poorly understood. This study examined stage-specific autonomic characteristics of PI using heart rate variability (HRV) analyses in a large population-based cohort. HRV [...] Read more.

Paradoxical insomnia (PI) is characterized by a discrepancy between subjective sleep complaints and objectively preserved sleep, yet its autonomic mechanisms remain poorly understood. This study examined stage-specific autonomic characteristics of PI using heart rate variability (HRV) analyses in a large population-based cohort. HRV features were extracted from non-overlapping five-minute windows across non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wake after sleep onset (WASO). Group differences were evaluated using FDR-corrected univariate analysis, multivariate embedding, and supervised machine learning. Whole-night, NREM, and REM features showed substantial overlap among groups. In contrast, the most consistent between-group differences emerged during WASO. Multivariate analysis showed the greatest group displacement during WASO, with UMAP centroid distances exceeding those observed during NREM and REM sleep. Supervised models trained on WASO-specific features achieved the highest classification performance, yielding an accuracy of 0.629 and an F1-score of 0.683 for PI versus normal sleep. Taken together, these findings suggest that WASO is the stage in which between-group HRV differences are most consistently detectable across complementary analyses, although several dispersion-based findings were substantially influenced by WASO window count. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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11 pages, 1760 KB

Open AccessCommunication

Dynamic Behavior and Structural Optimization of Drilling Rig Masts Using Composite and Metallic Materials: A Finite Element Approach

by Andrei Dimitrescu, Claudiu Babis, Iulian Sorin Munteanu and Sorin Alexandru Fica

Technologies 2026, 14(3), 154; https://doi.org/10.3390/technologies14030154 - 3 Mar 2026

Viewed by 782

Abstract

This study investigates the dynamic behavior and structural optimization of hydraulic water well drilling rig masts through a comparative finite element analysis (FEA) of metallic and composite configurations. The reference model, manufactured from structural steel (S355J2/E315), was compared with two optimized lightweight alternatives [...] Read more.

This study investigates the dynamic behavior and structural optimization of hydraulic water well drilling rig masts through a comparative finite element analysis (FEA) of metallic and composite configurations. The reference model, manufactured from structural steel (S355J2/E315), was compared with two optimized lightweight alternatives made of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) laminates. Simulations were performed in MSC Visual Nastran using identical geometric and loading conditions, including the critical dynamic event of drill string pull-out. The results demonstrate that substituting steel with composite materials significantly decreases the overall mass by up to 55%, while increasing the first natural frequency by 20–25% and reducing dynamic stress amplification by approximately 15–20%. Furthermore, the maximum tip displacement of the mast was reduced by 35–45% for the composite variants, indicating improved stiffness and vibration damping capability. These findings confirm that polymer composite structures offer superior dynamic performance, lower inertial loads, and enhanced operational safety, providing a viable route for next-generation lightweight drilling rig designs integrating advanced macromolecular materials. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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19 pages, 12656 KB

Open AccessArticle

Automatic Detection of TiO₂ Nanoparticles Using Dual-Coupled Microresonators and Deep Learning

by Andrés F. Calvo-Salcedo, Marin B. Marinov, Neil Guerrero González and Jose A. Jaramillo-Villegas

Technologies 2026, 14(1), 65; https://doi.org/10.3390/technologies14010065 - 15 Jan 2026

Cited by 1 | Viewed by 601

Abstract

The detection of titanium dioxide (

{TiO}_{2}

) nanoparticles is a significant challenge due to their extensive industrial use and potential health and environmental impacts, which demand accurate, label-free approaches. This work presents an automatic detection system based on spectroscopy with optical [...] Read more.

The detection of titanium dioxide (

{TiO}_{2}

) nanoparticles is a significant challenge due to their extensive industrial use and potential health and environmental impacts, which demand accurate, label-free approaches. This work presents an automatic detection system based on spectroscopy with optical frequency combs (OFC) in dual-coupled microresonators. The OFC generation was modeled through the Lugiato-Lefever equation, while propagation in distilled water containing

{TiO}_{2}

was simulated using the finite element method (FEM). The water–

{TiO}_{2}

mixture was described with the Yamaguchi model in a 5 × 5 mesh to represent non-uniform concentrations. From the norm of the electric field at a probe, a database of 11 classes (0–100%) with controlled Gaussian noise was constructed. A Transformer-based classifier was trained and compared with 1D-CNN and SVM under Monte Carlo validation (100 random 70/30 splits). The Transformer achieved 99.84 ± 0.01% accuracy with an inference time of 0.793 ± 0.05 s, while the 1D-CNN reached 99.64 ± 0.09% and the SVM 84.73 ± 1.48%. A repeatability test with 200 iterations confirmed deterministic DKS trajectories. The results demonstrate that combining dual-coupled microresonators, FEM, and Transformer architectures enables precise and efficient detection of

{TiO}_{2}

nanoparticles in aqueous solutions. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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17 pages, 28042 KB

Open AccessArticle

Renewable Energy Technology: Transient 3D CFD and Experimental Electrical Evaluation of a Cycloidal-Enhanced Rotor Versus a Savonius and Gorlov-Savonius Rotor with Blade Rotation Angle

by María Angélica Luján Vega, José Eli Eduardo González-Duran, Juan Manuel Olivares-Ramírez, Leonel Estrada Rojo, Jesus Alejandro Franco and Juvenal Rodríguez Reséndiz

Technologies 2026, 14(1), 27; https://doi.org/10.3390/technologies14010027 - 1 Jan 2026

Viewed by 595

Abstract

This study presents a numerical and experimental analysis of vertical-axis Cycloidal rotors (

R_{C}

) versus Savonius rotors (

R_{S}

), with and without coupling to a Gorlov rotor (

R_{G}

), designed to operate under low wind speed conditions [...] Read more.

This study presents a numerical and experimental analysis of vertical-axis Cycloidal rotors (

R_{C}

) versus Savonius rotors (

R_{S}

), with and without coupling to a Gorlov rotor (

R_{G}

), designed to operate under low wind speed conditions of 2.5 m/s. Using transient Computational Fluid Dynamics (CFD), numerical mesh stability was evaluated as a function of rotor power, achieving convergence with 8,199,923 nodes and a stable angular momentum after 10 s. In the experimental phase, electrical characterization was conducted by coupling the rotors to a direct current generator, allowing for the determination of the optimal electrical load as a function of rotational speed (RPM). The results show that electrical power output and power coefficient (Cp) increased with rotational speed, reaching a maximum of 39.22 mW and Cp = 0.126 for the Cycloidal rotor (

R_{C} θ_{R} 45

), which exhibited the best overall performance. When coupling a Gorlov rotor with a torsion angle of 90° (

R_{G} θ_{G} 90

), maximum power of 52.45 mW and Cp = 0.168 were obtained for the hybrid configuration

R_{C}, θ_{R} 0

-

R_{G} θ_{G} 90

, confirming the aerodynamic and electrical performance improvement due to geometric coupling compared to a standalone Savonius rotor. The comparison between the numerical and experimental results showed consistent trends in Cp values, with slight deviations attributed to friction and alignment effects during physical testing. This study proposes an integrated methodology in renewable energy technologies that combines 3D transient CFD simulation with experimental characterization under variable electrical load conditions to determine the optimal operating power of novel Cycloidal rotors for low-wind-speed applications. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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12 pages, 1893 KB

Open AccessArticle

Bandgap-Tuned Yttrium-Doped Indium Oxide Alloy Thin Films for High-Performance Solar-Blind Ultraviolet Photodetectors

by Lu Gan, Peicheng Jiao, Zhengdong Jiang, Yutao Xiong and Yanghui Liu

Technologies 2026, 14(1), 23; https://doi.org/10.3390/technologies14010023 - 1 Jan 2026

Viewed by 798

Abstract

Yttrium oxide (Y₂O₃) has emerged as a key material for advanced solar-blind ultraviolet (SBUV) photodetectors, attributable to its large bandgap energy (~5.5 eV), high dielectric constant, excellent silicon compatibility, and robust thermal stability. To precisely tune its optical bandgap [...] Read more.

Yttrium oxide (Y₂O₃) has emerged as a key material for advanced solar-blind ultraviolet (SBUV) photodetectors, attributable to its large bandgap energy (~5.5 eV), high dielectric constant, excellent silicon compatibility, and robust thermal stability. To precisely tune its optical bandgap for optimal alignment with the intrinsic solar-blind region, this study prepared Y_1.5In_0.5O₃ ternary alloy films via co-sputtering, achieving an optimized bandgap of 4.70 eV. After optimizing the photosensitive layer, we fabricated a self-powered Pt/Y_1.5In_0.5O₃/p-GaN back-to-back heterojunction SBUV photodetector was fabricated based on the optimized photosensitive layer. Under photovoltaic operation (0 V), the resulting device exhibited impressive performance metrics: a narrow spectral response (FWHM ~50 nm), quick rise/decay times of 30 and 75 ms, respectively, and high operational durability (less than 0.8% photocurrent degradation over 100 cycles). The detector also maintained a low noise current level (2.95 × 10⁻¹² A/Hz^1/2 at 1 Hz) and a low noise-equivalent power (NEP) of 4.42 × 10⁻⁹ W/Hz^1/2, indicating high sensitivity to weak optical signals. These results establish Y_xIn_2−xO₃ ternary alloy as a viable material platform for SBUV detection and provide a new design strategy for developing highly sensitive, low-noise and spectrally selective ultraviolet photodetectors. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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31 pages, 51329 KB

Open AccessArticle

Numerical Simulation and Optimization of Spray Cooling on High-Temperature Surfaces in Industrial Rotary Coolers

by Fangshuo Fan, Zuobing Chen, Yanhui Lai, Jiawei Liu and Ya Mao

Technologies 2025, 13(12), 589; https://doi.org/10.3390/technologies13120589 - 15 Dec 2025

Cited by 2 | Viewed by 1231

Abstract

Spray cooling efficiency plays a critical role in the heat dissipation process from the external surface of industrial low-carbon cement rotary coolers. This study numerically investigated the thermal performance of high-temperature zones by examining four spray parameters: spray angle, nozzle distance, spray height, [...] Read more.

Spray cooling efficiency plays a critical role in the heat dissipation process from the external surface of industrial low-carbon cement rotary coolers. This study numerically investigated the thermal performance of high-temperature zones by examining four spray parameters: spray angle, nozzle distance, spray height, and mass flow rate. Multi-objective optimization design (MOD) was subsequently performed using response surface methodology (RSM). RSM reveals spray angle as the most significant parameter affecting heat transfer. With temperature uniformity as a constraint, MOD yields the following optimal parameters: 89° spray angle, 380 mm nozzle distance, and 663.5 mm spray height. This configuration achieves an average surface temperature of 814.33 K and a heat flux of 131,588.3 W/m². The optimized spray parameters ensure high heat flux and uniform surface temperature while enlarging the heat transfer area and strengthening the synergistic heat transfer between dual nozzles. This approach provides a reliable technical pathway for efficient thermal management in industrial rotary cooler exteriors. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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26 pages, 3749 KB

Open AccessEditor’s ChoiceArticle

Synthesis of Pectin Hydrogels from Grapefruit Peel for the Adsorption of Heavy Metals from Water

by Vinusiya Vigneswararajah, Nirusha Thavarajah and Xavier Fernando

Technologies 2025, 13(9), 403; https://doi.org/10.3390/technologies13090403 - 5 Sep 2025

Cited by 5 | Viewed by 2975

Abstract

The increasing presence of heavy metals in aquatic environments, driven by the production of industrial waste and consumer products, poses serious environmental and health risks due to their toxicity and persistence. Copper (Cu(II)) and nickel (Ni(II)) are particularly harmful, with high concentrations linked [...] Read more.

The increasing presence of heavy metals in aquatic environments, driven by the production of industrial waste and consumer products, poses serious environmental and health risks due to their toxicity and persistence. Copper (Cu(II)) and nickel (Ni(II)) are particularly harmful, with high concentrations linked to neurological, dermatological and carcinogenic effects. This proof-of-concept study explores the synthesis of sustainable hydrogels derived from grapefruit peel (biosorbents) for the adsorption of Cu(II) and Ni(II) from aqueous solutions. Pectin was extracted from the peels and was used to synthesize pectin-based hydrogels (PH) and pectin hydrogel metal–organic frameworks (PHM composites). The hydrogels were characterized using FT-IR, SEM, diameter size and water absorption capacity. Lyophilized hydrogels were significantly smaller than their wet counterparts, and adsorption performance was analyzed using FAAS. PHs demonstrated high Cu(II) removal efficiency, achieving 95.11% adsorption and 97.75 mg/g capacity at pH 5. PHM composites showed comparable Cu(II) adsorption with a maximum capacity of 67.53 mg/g. Notably, PHs also exhibited rapid Ni(II) adsorption, reaching 92.62% efficiency and 28.189 mg/g capacity within one minute. These findings highlight the potential of pectin-based hydrogels as an effective, low-cost and environmentally friendly method for heavy metal remediation in water. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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Review

Jump to: Research, Other

27 pages, 2004 KB

Open AccessReview

Machine Learning in Personalized Medication Regimen Design for the Geriatric Population: Integrating Pharmacokinetic and Pharmacodynamic Modeling with Clinical Decision-Making

by Ahmad R. Alsayed, Mohanad Al-Darraji, Mohannad Al-Qaiseiah, Anas Samara and Mustafa Al-Bayati

Technologies 2026, 14(4), 241; https://doi.org/10.3390/technologies14040241 - 21 Apr 2026

Viewed by 1415

Abstract

Geriatric pharmacotherapy is usually challenged by physiological senescence. For instance, progressive declines in organ function and alterations in body composition can complicate drug disposition. However, conventional pharmacometrics models commonly have limited capacity to map these high-dimensional, nonlinear relationships. In this review, we are [...] Read more.

Geriatric pharmacotherapy is usually challenged by physiological senescence. For instance, progressive declines in organ function and alterations in body composition can complicate drug disposition. However, conventional pharmacometrics models commonly have limited capacity to map these high-dimensional, nonlinear relationships. In this review, we are examining the recent shift toward integrating machine learning (ML) with mechanistic pharmacokinetic (PK)/pharmacodynamic (PD) models to improve the accuracy and precision of dosing. Machine learning approaches like Random Forest and XGBoost consistently provided more accurate exposure predictions and significantly more efficient computational workflows than conventional methods. Nevertheless, concerns such as “black box” transparency and the potential of algorithmic bias toward specific patient demographics are challenging. It is important to incorporate explainability tools like SHAP, and adopting FAIR data principles is crucial for achieving professional trust and ensuring site-specific generalizability. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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36 pages, 1231 KB

Open AccessReview

Overview of Existing Multi-Criteria Decision-Making (MCDM) Methods Used in Industrial Environments

by Tanya Avramova, Teodora Peneva and Aleksandar Ivanov

Technologies 2025, 13(10), 444; https://doi.org/10.3390/technologies13100444 - 1 Oct 2025

Cited by 28 | Viewed by 11052

Abstract

The selection of an appropriate technological process is essential to achieve optimal results in manufacturing companies. This affects quality, efficiency and competitiveness. In the modern industry, multi-criteria decision-making (MCDM) methods are increasingly used to evaluate, optimize and solve various manufacturing challenges. In this [...] Read more.

The selection of an appropriate technological process is essential to achieve optimal results in manufacturing companies. This affects quality, efficiency and competitiveness. In the modern industry, multi-criteria decision-making (MCDM) methods are increasingly used to evaluate, optimize and solve various manufacturing challenges. In this review article, existing methodologies and patents related to optimization and decision making are investigated. The main characteristics and applications of the methods are outlined. The purpose of this article is to provide a systematic review and evaluation of the main MCDM methods used in industrial practice, including through an analysis of relevant methodologies and patents. The methodology involves a structured literature and patent review, focusing on applications of widely used MCDM techniques such as the AHP (analytic hierarchy process), ANP (analytic network process), FUCOM (full consistency method), TOPSIS (technique for order preference by similarity to ideal solution), and VIKOR (višekriterijumsko kompromisno rangiranje). The analysis outlines each method’s strengths, limitations and areas of applicability. Special attention is given to the potential of the FUCOM for process evaluation in manufacturing. The findings are intended to guide researchers and practitioners in selecting appropriate decision-making tools based on specific industrial contexts and objectives. In conclusion, from the comparative analysis made, the methodologies reveal their advantages and disadvantages as well as limitations that arise in their application. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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Other

Jump to: Research, Review

39 pages, 4130 KB

Open AccessSystematic Review

Predictive Models of Soil Electrical Resistivity Based on Environmental Parameters: A Systematic Review of Modeling Approaches, Influencing Factors and Applications

by Cesar Augusto Navarro Rubio, Hugo Martínez Ángeles, Mario Trejo Perea, Roberto Valentín Carrillo-Serrano, Saúl Obregón-Biosca, Mariano Garduño Aparicio, José Luis Reyes Araiza and José Gabriel Ríos Moreno

Technologies 2026, 14(5), 245; https://doi.org/10.3390/technologies14050245 - 22 Apr 2026

Viewed by 850

Abstract

Soil electrical resistivity (SER) is widely used as an indirect indicator of soil physical, chemical, and hydrological properties and plays an important role in applications such as grounding system design, geotechnical site characterization, agricultural soil monitoring, and environmental contamination assessment. However, SER is [...] Read more.

Soil electrical resistivity (SER) is widely used as an indirect indicator of soil physical, chemical, and hydrological properties and plays an important role in applications such as grounding system design, geotechnical site characterization, agricultural soil monitoring, and environmental contamination assessment. However, SER is strongly influenced by environmental variables including soil moisture content, temperature, salinity, and soil texture, which makes accurate prediction challenging under heterogeneous field conditions. A systematic review was conducted following the PRISMA 2020 protocol using the Scopus database to identify peer-reviewed studies published between 2018 and 2026 related to predictive models of soil electrical resistivity based on environmental parameters. After applying defined inclusion and exclusion criteria, a set of relevant studies was selected for qualitative and comparative analysis. The reviewed studies consistently identify soil moisture content as the most frequently reported influential factor affecting SER, followed by temperature, salinity, and soil texture. This observation reflects the predominant focus of the analyzed literature within the selected time frame rather than a definitive representation of all controlling physical processes. Similarly, the reviewed literature suggests that empirical and statistical models remain valuable due to their simplicity and interpretability, whereas machine learning approaches such as artificial neural networks, support vector regression, and ensemble methods are often reported to achieve higher predictive accuracy in complex soil environments. The predictive SER modeling represents a rapidly evolving research field, and future work should focus on hybrid physics-informed machine learning models, the development of standardized datasets, and the integration of predictive algorithms with emerging sensing technologies and IoT-based monitoring systems. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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39 pages, 1059 KB

Open AccessSystematic Review

Ground Enhancement Materials for Grounding Systems: A Systematic Review of Factors, Technologies and Advances

by Hugo Martínez Ángeles, Cesar Augusto Navarro Rubio, Luis Angel Iturralde Carrera, Leonel Díaz-Tato, José Gabriel Ríos Moreno, Mario Trejo Perea, Roberto Valentín Carrillo-Serrano and Juvenal Rodríguez-Reséndiz

Technologies 2026, 14(1), 49; https://doi.org/10.3390/technologies14010049 - 8 Jan 2026

Cited by 3 | Viewed by 2427

Abstract

Grounding Systems (GS) play a critical role in electrical safety, lightning protection, and the reliable operation of power and renewable energy infrastructures, particularly in high-resistivity soils. In this context, Ground Enhancement Materials (GEM) are widely used to reduce soil resistivity and improve grounding [...] Read more.

Grounding Systems (GS) play a critical role in electrical safety, lightning protection, and the reliable operation of power and renewable energy infrastructures, particularly in high-resistivity soils. In this context, Ground Enhancement Materials (GEM) are widely used to reduce soil resistivity and improve grounding performance. This systematic review analyzes and synthesizes recent advances (2018–2025) in GEM applied to GS, with emphasis on their electrical performance, durability, and environmental sustainability. The review covers conventional GEM, industrial waste-derived materials, and hybrid formulations, evaluating their effectiveness under different soil types and moisture conditions. Comparative analysis of the literature indicates that GEM derived from industrial byproducts and hybrid composites often exhibit superior long-term resistivity reduction due to enhanced moisture retention and material-soil interactions, especially in clay-rich and heterogeneous soils. Sustainability considerations such as environmental impact, material availability, and long-term stability are increasingly influencing GEM selection and design. Overall, this review provides a structured framework for understanding the factors governing GEM performance while highlighting current trends, challenges, and future research directions in the development of sustainable grounding solutions. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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