This article investigates the characteristics of the marginal lateritic soil stabilized with cement and wood bottom ash (WBA) as subbase materials. A comprehensive methodology involved modified Proctor compaction and unconfined compression tests, supplemented by microstructural analyses, as well as pilot-scale field construction and leachate monitoring. Results revealed that compressive strength increased with WBA content up to 60%, and was further enhanced by higher cement dosages. Compressive strength was inversely correlated with porosity and voids-to-cement ratio. Field monitoring showed that Cr was the only metal consistently detected in leachate, decreasing over time but remaining above permissible limits, indicating insufficient immobilization due to weaker incorporation into cementitious phases. This study highlights the synergistic effects of WBA and cement in improving soil subbase performance while supporting sustainable reuse of industrial by-products.
The adsorption behaviour of anionic and cationic dyes, namely methyl orange (MO) and methylene blue (MB), respectively, has been studied on carbon-silica composites. These composites were produced through the valorisation of waste vinasse from industrial sugar production as the carbon feedstock, while the silica production was achieved via a simple sol-gel method using low-cost sodium and potassium silicate solutions (Na2SiO3 and K2SiO3) or higher-cost tetraethyl orthosilicate (TEOS). Use of Na2SiO3, K2SiO3 and TEOS, led to composites with specific surface areas of 375, 343 and 656 m2/g and % mesoporosity of 79.45, 94.35 and 82.93%, respectively. Adsorption of MO is reduced at higher pH, but MB removal was enhanced under basic pH. Kinetic results demonstrated a strong correlation with the pseudo-second order model, and it was noted that intraparticle diffusion had a key role to play in the mechanism of mass transport. Adsorption data fitted the Sips isotherm model, with excellent adsorption capacity of MO (844.63 mg/g for Na2SiO3, 979.06 mg/g for K2SiO3, 1161.65 mg/g for TEOS) and MB (514.30 mg/g for Na2SiO3, 385.34 mg/g for K2SiO3, 294.88 mg/g for TEOS). Elevated temperatures led to preferential removal of the acidic and basic dyes, suggesting an endothermic adsorption process. Carbon-silica composites derived from vinasse are promising materials for the remediation of MB and MO from aqueous systems, exhibiting comparable or higher surface area and adsorption capacities for both dyes relative to other reported materials, while being synthesized through simple preparation processes from low-cost agricultural by-products.
This study presented the comprehensive analysis and management to reduce carbon emissions in Thailand's reinforced concrete building construction projects using Structural Equation Modelling (SEM). Data were collected via questionnaires from experts certified by the Thailand Green Building Institute. The SEM analysis, which demonstrated good model fit, identified three main components: Material Manufacturing, Transportation, and Construction Processes. Structural work exhibited the highest factor loading, whilst electricity uses in construction showed the lowest. Notably, the study revealed the negative relationship between material manufacturing and transportation processes, challenging conventional assumptions. These findings provide crucial insights for designers and industry professionals in developing targeted strategies to reduce carbon emissions. The study's limitations include its focus on the Thai context and a specific set of variables. Future research should explore additional factors, such as renewable energy integration, and conduct longitudinal studies to refine our understanding of emission drivers in the construction sector.
Banana peels (Musa ABB, Namwa cultivar) were utilized as sustainable precursors for the production of carbonaceous materials for supercapacitor electrodes. Carbonization was performed at temperatures ranging from 500 to 1000 °C. During carbonization, CO2 and H2O were released, promoting physical self-activation and pore development. X-ray diffraction confirmed the presence of KCl and K2CO3 phases, which were removed after washing with HCl and deionized water, leaving only amorphous carbon. Iodine adsorption analysis revealed that increasing carbonization temperature enhanced porosity, with iodine adsorption values of -10.42, 194.20, 150.04, 485.80, 471.44, and 325.09 mg·g-1 at 500, 600, 700, 800, 900, and 1000 °C, respectively. This trend is in agreement with the change in surface area with carbonization temperature, which increased from 500 to 800 °C (382.403–800.846 m2·g-1) and decreased at 900 and 1000 °C (684.595 and 156.881 m2·g-1, respectively). The carbon obtained at 800 °C (CC800) delivered the highest specific capacitance, 169 F·g-1 at 0.5 A·g-1 in a 1 M H2SO4 electrolyte, owing to its highest specific surface area (800.846 m2·g-1). Additionally, the redox peaks associated with surface functional groups formed during carbonization would further enhance the capacitance of all carbons. These results demonstrate that banana peel-derived carbon, synthesized via carbonization, exhibits a high surface area and excellent charge storage capability.
This study presents the development of a cost effective and an innovative household water filtration system utilizing activated carbon derived from locally sourced rubber tree branches. Aimed at improving tap water quality in the Ban Kut Saeng Community, Sakon Nakhon Province, Thailand, the research promotes sustainable material use and simple technology to enhance the availability of clean water in underdeveloped and developing regions. Activated carbon was produced through a dry activation process using NaOH, yielding high adsorption capacity 687.24 mg I₂/g for iodine and complete (100%) methylene blue removal at 100 ppm. BET surface area analysis indicated a value of 833.06 m²/g, with the pore structure exhibiting both microporous and mesoporous characteristics. The filtration performance was evaluated by comparing raw and treated water quality based on the standards of the American Public Health Association (APHA), American Water Works Association (AWWA), and Water Environment Federation (WEF), covering physical, chemical, heavy metal, and microbiological parameters. The raw water has shown high turbidity and bacterial contamination. Following two-stage filtration, Sample D exhibited the tap water quality standards, with turbidity of 9 NTU, apparent color of 2.1 Pt-Co, total dissolved solids (TDS) of 151 ppm, pH of 6.8, total hardness of 30 ppm as CaCO₃, and chloride ion concentration of 9.8 ppm. Total coliforms and E. coli counts were observed below the detectable limits (<1.1 CFU/100 mL), and heavy metals or other harmful ions found to be absent. The results have confirmed the system’s high efficiency in contaminant removal and water quality improvement, underscoring its potential as a low-cost, community-driven solution for household water treatment in the resource-limited areas.
This paper reviews presents a review of the current status and future prospects of plasmonic-effect-based No-Core Fiber (NCF) sensor technology. The discussion covers the basic principles of plasmonic effects, namely Surface Plasmon Resonance (SPR) and Localized Surface Plasmon Resonance (LSPR), the sensing mechanism of NCF, and the current status of plasmonic-based NCF sensor development. Plasmon-based NCF sensors have been widely applied in various measurement applications, with the majority of research focusing on refractive index sensors. Most studies still utilize SPR rather than LSPR, considering the technical challenges in controlling the size and shape of metal nanoparticles in LSPR. Nevertheless, LSPR provides design flexibility that enables precise tuning of nanostructure parameters to improve sensor sensitivity. Some potential development directions include optimizing sensor design to improve sensitivity and selectivity, developing more controllable and reproducible fabrication methods, and exploring new plasmonic materials. With the advancement of fabrication technology and characterization methods, it is expected that the technical challenges in developing plasmonic-based NCF sensors can be overcome. This advancement will facilitate broader applications across diverse fields, including chemistry, biosensing, and environmental monitoring.
The fast growth of Internet of Things (IoTs) devices calls for the demand to accomplish an efficient and lightweight image encryption. Also, protecting the privacy of sensitive visual information on devices with limited resources presents significant challenges. Existing techniques and methods are not able to provide both powerful security and low computational requirements. Therefore, we propose an innovative encrypting scheme that combines three methods (Latin squares, dynamic DNA sequencing, and chaotic systems). This architecture is designed for the IoT environments where it is important that transactions are made both efficient and secure. Additionally, our approach offers strong security assurances and suitable for the constraints of edge devices. The performance evaluations of the proposed technique against modern encryption algorithms display a promising result. Our proposal demonstrates almost perfect randomness. It achieved an image entropy of (7.9994) and a minimal correlation of (-0.00472). The proposed system also demonstrated its high ability to resilience the differential attacks, with NPCR and UACI of (98.671) and (34.098), respectively. Also, the keyspace of the proposed method is ( ), that providing a robust defense against brute-force attacks. Despite this high security level, the technique maintains practical performance, encrypting a (512×512) image in (1.4026) seconds. These outcomes confirm the algorithm's effectiveness for protecting sensitive images in IoT applications, offering both security and suitable efficiency for real-world use.
Non-adherence to medication regimens remains a significant concern in healthcare, negatively impacting patient outcomes and increasing costs. This pilot study investigated using Internet of Things (IoT) and microcontroller technologies to enhance adherence through a smartphone-app-driven smart pill box. The prototype integrated an IoT-enabled microcontroller with a custom app designed to send real-time medication reminders and log pill box usage. Ten participants with chronic conditions requiring daily medication evaluated the device over four weeks. Adherence rates, self-reported before the trial, were compared to those monitored during the trial. Results indicated a marked improvement in adherence, increasing from 78% at baseline to 92% during the study. Additionally, 90% of participants rated the system as user-friendly and expressed willingness to continue its use. The study highlights the potential of this IoT-enabled smart pill box to address medication non-adherence effectively. However, further research with larger sample sizes is necessary to confirm its broader applicability across diverse populations and medical conditions. These findings suggest that integrating IoT and microcontroller technologies into adherence interventions could significantly benefit chronic disease management and healthcare outcomes. This innovative approach holds promise for addressing the widespread challenge of medication non-adherence, but larger and more demographically diverse studies are needed to validate its long-term effectiveness and generalizability.
Critical infrastructure such as police stations, military bases, and security checkpoints is increasingly exposed to armed threats, while most existing structures lack adequate ballistic protection. Conventional construction materials are generally ineffective against high-velocity projectile impacts, highlighting the need for practical, retrofit-compatible protective systems. This study aims to develop and optimize composite bulletproof panels that meet NIJ 0108.01 Level III requirements for resistance against 7.62×51 mm NATO M80 ammunition, with emphasis on structural feasibility and cost-effectiveness. An experimental program was conducted using three materials: standard structural steel (SS400), high-hardness steel (HS450), and asphalt cement (AC). These materials were configured as single-layer, double-layer, and sandwich panels. Ballistic tests were performed using 7.62×51 mm NATO projectiles at an average velocity of 847±9.1 m/s. Projectile velocities before and after impact were recorded to evaluate energy absorbed. Finite element simulations using ABAQUS were employed to validate experimental results and analyze stress-wave propagation and failure mechanisms. The results indicate that material hardness plays a more significant role than thickness in enhancing ballistic resistance. While 6 mm SS400 absorbed 35.81% of impact energy, a thinner 3 mm HS450 layer achieved 28.95%, demonstrating the efficiency of high-hardness materials. Layered configurations significantly improved performance, with SS400-6/AC25 and HS450-3/AC25 absorbing 62.39% and 78.35% of impact energy, respectively. Notably, sandwich configurations (SS400-6/AC25/SS400-6 and HS450-3/AC25/SS400-6) achieved complete projectile arrest. Numerical results confirm that a high-hardness strike face combined with a viscoelastic backing layer maximizes energy dissipation. The optimized HS450-3/AC25/SS400-6 panel provides full ballistic protection with reduced weight compared to conventional steel armor. Field validation demonstrates its applicability for retrofitting existing infrastructure. This study contributes practical design guidelines for developing cost-effective ballistic-resistant building systems in high-risk environments.
This study presents a comprehensive numerical investigation into the hydrodynamic effects of commonly adopted hull appendages, which are the bulbous bow, skeg, and sonar dome, on a displacement ship operating across a wide Froude number range (Fr = 0.16 to 0.41). Simulations were conducted using Reynolds-Averaged Navier–Stokes (RANS) equations in combination with the Volume of Fluid (VOF) method to capture free-surface effects. A four-stage stepwise configuration scheme was employed: bare hull, hull with bulbous bow, with bulbous bow and skeg, and finally the fully appended hull including the sonar dome. Results indicate that the bulbous bow contributed the most significant reduction in total resistance, which is up to 8.62% at Fr = 0.26, through wave interference and improved pressure distribution. The skeg aided aft-body flow alignment while the sonar dome showed negligible effect on resistance within typical operating conditions. All appended configurations exhibited increased frictional resistance due to surface area growth, with the full configuration showing an average increase of 6.97%. However, these increases were offset by considerable reductions in pressure resistance, particularly in transitional speeds, with a maximum pressure drag reduction of 29.03% at Fr = 0.24. The novelty of this study lies in its systematic, configuration-based evaluation of multiple appendages under consistent CFD conditions, enabling clear quantification of trade-offs between frictional and pressure resistance. The findings offer practical insights into appendage integration strategies for performance optimization in displacement vessels and guidelines for naval architects in selecting and integrating appendages to optimize resistance characteristics during the early design stages of displacement vessels.
This study focused on developing a novel adsorbent material of green nano zero-valent iron supported by nanocellulose (G-NZVI/NCC) from agricultural waste for the effective removal of arsenic. The adsorption isotherm for arsenite (As3+) and arsenate (As5+) on G-NZVI/NCC showed maximum capacities of 4.2123 and 4.9579 mg·g‒1, respectively. The model indicated that As3+ forms a multilayer on a heterogeneous surface, while As5+ forms a monolayer on a homogeneous surface. The Gibbs free energy for the adsorption of As3+ and As5+ on G-NZVI/NCC from water demonstrates a non-spontaneous process at higher temperatures for As3+, but it occurs spontaneously for As5+. Furthermore, most previous studies have largely overlooked the influence of co-existing ions in water, or have investigated them individually. Consequently, this study aimed to simulate real-world conditions by examining the simultaneous effects of all relevant ions. This comprehensive approach is critical for the practical implementation of the system, and statistical principles are employed for data analysis and interpretation. However, surface water contains competing ions such as calcium (Ca2+), phosphate (PO43‒), bicarbonate (HCO3‒), chloride (Cl‒), and sulfate (SO42‒) that can interfere with arsenic adsorption. A 25‒2 fractional factorial design was used to systematically evaluate individual factors, significant parameters, and their interactions on arsenic removal efficiency. The results revealed that Ca2+, PO43‒, and HCO3‒ play crucial roles in arsenic removal, with Ca2+ and PO43‒ enhancing removal efficiency, while HCO3‒ exhibits inhibitory effects. Notably, high PO43‒ concentrations unexpectedly enhanced arsenic removal compared to previous studies, attributed to the synergistic physical and chemical adsorption properties of iron oxide and hydroxide phases. Mechanistic analysis revealed that As5+ can substitute PO43‒ in vivianite formation, creating a vivianite-symplesite solid solution (Fe3(PO4)2.7(AsO4)0.3·8H2O), providing new insights into arsenic immobilization mechanisms under realistic water chemistry conditions.
In the modern digital era, e-commerce remains a fundamental pillar of the global economy. The rapid growth of online ventures has fundamentally reshaped how products are bought and sold. However, as the number of products on e-commerce platforms in Indonesia increases, the main challenge is accurately matching products to consumer preferences. While previous studies have primarily used unimodal approaches to product matching, relying on a single type of information, little research has examined how to recognize new products on e-commerce platforms. Utilizing a multimodal approach that integrates information from diverse data types, such as text and images, is increasingly appealing for improving product-matching quality. This study's main contribution is to create vector representations of image and text features using deep learning techniques, including Convolutional Neural Networks and Doc2Vec. It also involves merging features through a cross-modality approach using FeedForward Neural Networks and determining the appropriate parameters for clustering new products into existing clusters using Hierarchical Clustering. The research demonstrates that employing a multimodal approach that leverages text and image information can enhance product matching quality on e-commerce platforms in Indonesia, achieving a Normalized Mutual Information of 0.96.
This study proposes to create packaging films using a blend of polyvinyl alcohol (PVA), chitosan (CS), and ethylenediaminetetraacetic acid (EDTA) in order to inhibit bacterial growth on tilapia fillets during storage. The films were prepared in three formulations with varying weight ratios: PVA6/CS10, PVA6/CS10/EDTA2, and PVA6/CS10/EDTA4. The results showed that as the concentration of EDTA increased, the ∆E* value, thickness, moisture content, and water vapor transmission rate of the films also increased. The morphology of the cross-sectional microstructure revealed a small spot in the PVA6/CS10/EDTA4 film, while the other formulations exhibited a homogeneous structure. FT-IR analysis indicated that the hydroxyl and carboxyl group peaks in the blended films (PVA, CS, and EDTA) were more prominent than in the individual components. Further, the tilapia fillets wrapped in the PVA6/CS10/EDTA4 film showed the greatest total color change but experienced less weight loss than the other films after storage at 4-7 °C for 7 days. The increased EDTA concentration in the wrapped film enhanced the inhibition of aerobic bacteria on the tilapia fillets. Therefore, the combination of PVA, CS, and EDTA in the film effectively inhibited bacterial growth and extended the shelf life of the tilapia fillets.
This study investigates the free vibrations of a glass/polyester composite cantilever beam through an integrated analytical, numerical, and experimental approach. An analytical formulation, based on the Timoshenko beam model and incorporating bending-torsion coupling, was developed to compute natural frequencies and mode shapes. Numerical modeling, using finite element methods, simulated the vibrations while accounting for transverse shear and rotary inertia effects. Concurrently, an experimental modal analysis was performed by exciting the beam at multiple points and measuring natural frequencies via frequency response functions. The findings reveal strong agreement between the analytical and numerical approaches, with a relative error below 0.15%, but notable discrepancies with experimental data, exceeding 15%. These discrepancies are attributed to two main physical factors neglected in idealized models: the inherent material damping and the imperfect stiffness of the experimental support system. Adjusting the numerical model reduced these discrepancies, enhancing the method’s reliability. This approach provides a robust framework for designing composite structures, while highlighting the need to incorporate quantified damping and accurately defined material and boundary characteristics in future research for improved predictive accuracy.
This study investigates the effect of combining cassava starch gelatin and cassava peel gelatin and evaluates the influence of two different fillers, cellulose and bentonite clay, on the mechanical properties of bioplastics. It aims to obtain bioplastics with tensile strength and elongation properties comparable to low-density polyethylene (LDPE). The study employs different mass ratios of starch to gelatin (9:1, 8:2, and 7:3 g/g) and incorporates bentonite clay and cellulose fillers (corn stalks and husks), each at 0.4 g. The materials were processed with a 200-mesh sieve. Bioplastic synthesis involved a glycerol concentration of 25% (w/w), stirring at 375 rpm for 35 minutes at 90°C. Mechanical properties (tensile strength, elongation, and Young's modulus) were analyzed, along with SEM and FTIR characterization. The results indicate that the bioplastic formulation with a starch-to-gelatin ratio of 8:2 and bentonite clay as a filler exhibits the most promising mechanical properties, with a tensile strength of 1.837 MPa, elongation of 33.584%, and a Young's modulus of 5.47 MPa. Comparatively, bentonite clay enhanced tensile strength and rigidity, while cellulose fillers provided higher elongation and flexibility but lower reinforcement due to particle agglomeration. FTIR analysis identified key functional groups, including O-H, CH2, C=O, N-H, and C-O. The combination of cassava starch, cassava peels, corn stalk-derived cellulose, fillers, and plasticizers significantly impacts the quality and mechanical properties of bioplastics.
The transformation of traditional into smart logistics parks (SLPs) requires a structured framework for assessing maturity and guiding development. Existing maturity models, often adapted from smart city or industrial park frameworks, lack specificity and empirical validation for the logistics park context. To address this gap, this study constructs and validates a data-driven maturity model for SLPs by integrating a literature review, expert consultation, and case studies of two logistics parks in western China. The model comprises five dimensions—Smart Economy, Public Services and Smart Governance, Infrastructure and Intelligent Technology Application, Skilled Human Capital, and Environmental Sustainability—and defines 20 measurable factors. These factors are evaluated through a four-level maturity framework progressing from Initial to Optimization stages, corresponding to the conventional levels of Initial, Defined, Managed, and Leading. The application revealed that both parks were positioned between the Initial and Growth stages. Logistics Park 2 excelled in digital infrastructure and technology adoption, as well as workforce digital literacy, whereas Logistics Park 1 enjoyed advantages in operational ROI and stakeholder integration. While for both parks, Public Services and Smart Governance reached the Growth stage, Smart Economy and Environmental Sustainability lagged, reflecting reliance on volume-based growth and underdeveloped sustainability outcomes. The model differentiated between transitional and emerging SLPs, proving its value as a diagnostic and strategic tool. One limitation, however, is the model's initial validation within the two case studies in western China, and future research should expand the application to diverse contexts to enhance generalizability and robustness.
Drone-assisted chemical application in longan orchards is feasible but requires refined methods for efficient and uniform aerosol delivery. This research aimed to determine the optimal drone flight altitude and chemical-to-water mixing ratio for inducing inflorescence in longan trees. Three chemical mixtures were tested: the conventional farmer-used ratio (50 g/10 L), a higher concentration (50 g/5 L), and a reduced dosage (25 g/5 L). Droplet deposition at different flight altitudes above the longan canopy was evaluated using water-sensitive paper to identify the most effective spraying conditions. The results revealed that at a drone flight altitude of 2 m above the longan canopy, the droplet deposition percentage from the drone sprayer reached a maximum of 76.22% on water-sensitive paper. This flight altitude was selected for the flight test to determine the appropriate mixing ratio of inflorescence-inducing chemicals for spraying in longan orchards. The reduced dosage treatment (25 g/5 L) resulted in the greatest number of inflorescences and the highest yield. Although the conventional and reduced treatments share the same chemical concentration (5 g/L), the latter involved a lower total amount of chemical applied per tree due to the reduced spray volume during drone operation. This approach enables farmers to decrease total chemical usage and operational costs while enhancing inflorescence induction and yield in longan orchards. Additionally, it enables faster drone operation, effectively doubling the treatment area per flight.
While stacking ensemble methods have been widely adopted for disaster risk classification, most traditional implementations are limited to shallow single-layer architectures, lacking generalization capacity and adaptive meta-feature design. This study proposes Multilayer Stacking Ensemble Learning (MLSEL), a novel approach that addresses these limitations through three key innovations: (i) a deep three-layer stacking architecture, (ii) Meta Feature Augmentation (MFA) to enrich inter-layer representations, and (iii) automated hyperparameter optimization using Optuna to enhance meta-learner performance. The model is evaluated on a multiclass flood risk dataset consisting of 50,000 structured records. Results reveal that the proposed MLSEL particularly the configuration using XGBoost + Optuna + MFA achieves superior accuracy of 98.69%, significantly outperforming both the best-performing baseline and conventional stacking models. This research demonstrates that combining deep-layered learning, meta-feature engineering, and adaptive optimization effectively overcomes overfitting, feature redundancy, and scalability issues inherent in traditional stacking. The MLSEL framework establishes a robust foundation for accurate and reliable disaster risk prediction systems.
Efficient sedimentation monitoring is vital for maintaining navigability and hydraulic performance in canal systems, yet conventional hydrographic surveys remain costly and logistically demanding. This study integrates a low-cost hydroacoustic surveying method using a recreational-grade single-beam echosounder to provide cost-effective and practical solution for canal sedimentation management. The approach was first validated in Songkhla Lake, representing mixed freshwater, brackish, and saline conditions, and subsequently applied to the Samrong Canal in southern Thailand as a real-world case study. Controlled experiments compared echosounder-derived depths with reference measurements to evaluate vertical accuracy under different water types following the International Hydrographic Organization (IHO) S-44 Special Order criteria. Results showed a standard deviation (SD) of 0.05 m and RMSE₉₅ = 0.14 m in controlled settings, with no statistically significant effect of water type on accuracy. Semi-controlled field surveys yielded SD values of ±0.06 m (fresh), ±0.05 m (brackish), and ±0.09 m (saline), all within the IHO tolerance limits. The validated setup was then applied for 0.5-m contour bathymetric mapping in Samrong Canal, successfully delineating shoaling zones and siltation hotspots critical for maintenance planning. The findings demonstrate that integrating low-cost echosounders into canal monitoring workflows can produce IHO compliant, high-utility bathymetric data, offering a scalable and cost-effective alternative for sedimentation assessment in shallow tropical waterways.
The construction industry consumes large quantities of cement and is therefore a major source carbon dioxide (CO2) emissions. As a more sustainable alternative, rice husk ash (RHA) can partially replace cement, taking advantage of its pozzolanic properties. The reactivity of RHA varies depending on the calcination temperature, affecting the strength and microstructure of the mortar (MM). This study evaluates how calcination temperature of RHA affects compressive strength and MM. RHA was heat treated at 600, 650, 700 and 750 °C to analyze the compressive strength of the mortar. Mortar specimens were prepared with RHA replacing cement at 5, 10, 15 and 20% to replace cement and compressive strength tests were performed at 7, 14, 21 and 28 days. In addition, the MM was characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Thermogravimetric Analysis (TGA) were used to evaluate the MM. The results indicate a maximum gain in compressive strength up to 55.87% when 15% of the cement was replaced with RHA calcined at 700 °C, whose amorphous silica content was 69.40%. Moreover, microstructural analyses evidenced the formation of C-S-H and C-A-S-H gels, which densified the mortar, improved the interfacial zone (ITZ), and reduced overall porosity. It is concluded that the calcination temperature significantly influences the pozzolanic reactivity of RHA, the mechanical strength, and MM; RHA calcined at 650 - 700 °C exhibited the best mechanical performance, attributable to higher amorphous silica content and greater microstructural densification. It is recommended for future works to investigate calcination temperatures above 750°C, analyze the relationship between RHA particle size and its amorphous silica content, and evaluate the mortar´s long-term durability. In addition, studies should assess the interaction of RHA with other alternative materials to identify synergistic effects and optimize the RHA dosage.
