Engineering

Abstract: Aircraft electrification requires high performance thermal management systems able to cool-down power-plants with increasing power densities in electric aircraft motors. The demanding mission profiles and the request for compact electric components, in fact, induce high temperatures in power-plant system that must be cooled by proper thermal management systems, to assure systems efficiency and reliability. This paper investigates and compares two promising approaches for the cooling of megawatt order electric motor for aviation applications: nanofluid based liquid cooling and radial tube systems. Nanofluids are an innovative approach to system cooling leveraging on the physic properties of the coolant; radial tubes, conversely, represent a structural solution aimed at improving the heat removal. In particular, nanofluids are composed by colloidal suspensions of nanoparticles in a base fluid, enabling enhanced thermal conductivity and convective heat transfer coefficients compared to conventional coolants. Radial tubes improve heat removal through optimized conduction paths and increased surface to volume ratios without altering the working fluid. Through numerical analysis carried out by using state of the art Computational Fluid Dynamic (CFD) tools, results highlight the main advantages of the two systems: nanofluids provide a significant average heat transfer enhancement on the tooth, while the radial tubes involve a strong increase in the global heat exchange despite a larger oil flow rate.

Abstract: The increasing integration of renewable energy sources, distributed generation, and smart grid technologies has significantly increased the dynamic complexity of modern electric power systems. Under such operating conditions, synchronous generators are required to maintain stable voltage, enhance transient performance, and suppress electromechanical oscillations despite continuous variations in load demand and network disturbances. Conventional excitation control systems based on proportional–integral (PI) and proportional–integral–derivative (PID) regulators exhibit limited adaptability to nonlinear operating conditions because of their fixed controller parameters and simplified control structures. Consequently, developing advanced excitation control strategies capable of improving the dynamic stability and operational reliability of synchronous generators has become an important research challenge. This paper proposes an Adaptive Hybrid Excitation Control (AHEC) strategy for longitudinal–transverse excited synchronous generators to improve dynamic stability under variable operating conditions. The proposed approach integrates coordinated longitudinal and transverse excitation control with adaptive parameter tuning and nonlinear feedback compensation into a unified control framework. A comprehensive nonlinear mathematical model of the generator is developed in the synchronous dq-reference frame by considering stator electrical dynamics, dual excitation winding dynamics, electromagnetic cross-coupling, magnetic saturation, and rotor mechanical motion. Based on the developed model, an adaptive hybrid excitation controller is synthesized to coordinate excitation currents in real time, ensuring optimal magnetic flux distribution, enhanced damping characteristics, and improved transient performance. The effectiveness of the proposed control strategy is evaluated through detailed MATLAB/Simulink simulations under various operating scenarios, including sudden load changes, voltage sags, reactive power fluctuations, parameter uncertainties, and three-phase short-circuit faults. The obtained results are compared with those of conventional Automatic Voltage Regulator (AVR), PI, PID, and Adaptive PID excitation controllers using key dynamic performance indicators such as voltage overshoot, settling time, steady-state error, damping ratio, rotor-angle deviation, and transient stability margin. Simulation results demonstrate that the proposed Adaptive Hybrid Excitation Control strategy substantially improves voltage regulation accuracy, suppresses electromechanical oscillations, accelerates transient recovery, and enhances the overall dynamic stability of the longitudinal–transverse excited synchronous generator. Compared with conventional excitation control methods, the proposed controller provides superior robustness against nonlinear disturbances and parameter variations while maintaining stable operation over a wide range of operating conditions. The proposed methodology offers an effective solution for next-generation synchronous generators employed in renewable energy systems, autonomous micro grids, and intelligent power networks.

Abstract: Equipment platforms and large-span floors in buildings are often modeled as sandwich plates carrying discrete supported masses. If the in-plane support forces fluctuate periodically, bending stiffness is modulated in time and parametric instability can develop without direct transverse forcing, undermining vibration isolation. Prior work on viscoelastic sandwich plates has treated uniform layouts or isolated periodic design variables; how simultaneous spatial tailoring of face thickness, core moduli, and mass distribution interacts with biaxial longitudinal excitation is still open. Here we develop a coupled biaxial parametric stability formulation for periodically controllable viscoelastic sandwich plates with Kelvin–Voigt magnetorheological cores, extending the modeling framework in [1–3]. First-order shear deformation theory and Galerkin reduction yield a multi-degree-of-freedom system with periodic coefficients. Instability boundaries are obtained in one step through a direct eigenvalue procedure that couples Floquet theory, harmonic balance, and matrix eigenvalue analysis, avoiding branch-by-branch tracking. Finite element comparisons indicate that principal tongues appear near η ≈ 2ω_n, that matched periodicity in thickness and core modulus markedly increases stability margins, and that plate width, mass placement, and excitation waveform strongly reshape the safe operating region. The findings offer quantitative guidance for designing building-related sandwich foundations against parametric instability.

Abstract: Electric motorcycles are central to SubSaharan Africa’s transition to electric mobility, yet manufacturers in the region typically rely on costly and time-consuming physical prototyping to optimise powertrains built from imported components. This paper presents a validated full-powertrain digital twin of the Roam Air, an electric motorcycle assembled in Nairobi, Kenya, developed in MATLAB/Simulink as four interconnected subsystems: the battery, the controller, the motor, and the vehicle dynamics. The battery is modelled as a Thévenin equivalent circuit whose parameters were experimentally derived at the pack level through Hybrid Pulse Power Characterisation tests, and the controller replicates the motorcycle’s field-oriented control with a maximum torque per ampere strategy, including its battery current and voltage limiting behaviour. The motorcycle’s regenerative braking characteristics, drag coefficient, and rolling resistance coefficient were experimentally obtained through braking, coasting, and coast-down tests. The digital twin ingests rider inputs and GPS information, and predicts the motor’s speed and the battery’s power. Validation against six measured drive cycles in Stellenbosch, South Africa, demonstrates strong agreement between predicted and measured profiles, with Pearson’s r values of 0.905 0.981 for battery power and 0.912–0.996 for motor speed, and energy consumption predicted to within 2.71% for five of the six trips. The presented modelling and characterisation framework offers manufacturers a transferable, computationally efficient alternative to iterative physical prototyping for powertrain optimisation under Sub-Saharan African operating conditions.

Abstract: This paper proposes a novel neuro-symbolic framework that augments Vision-Language-Action (VLA) models to assist high-precision operations in industrial assembly. While Large Language Models (LLMs) demonstrate expertise in interpreting natural language instructions, they often struggle with processing structured manufacturing data and lack long-horizon planning capabilities and deterministic physical awareness required for complex robotic execution. Furthermore, their susceptibility to generating physically unfeasible sequences and their lack of robust error-recovery mechanisms limit their autonomous deployment. To address these limitations, the proposed pipeline decomposes the assembly process into sequential stages of planning, technological grounding, adaptive execution, and recovery. Initially, the framework leverages LLMs to synthesize multimodal inputs—ranging from natural language directives to formalized product and process specifications—into a formal Hierarchical Task Network (HTN). To ensure physical feasibility, an external symbolic planner rigorously validates the HTN’s causal dependencies against established spatial and operational constraints. This verified logical sequence is subsequently compiled into a reactive Behavior Tree (BT) to guide the VLA model. During execution, a real-time object detection system and an atomic skill library dynamically translate the BT into grounded actions. Designed for human-robot collaborative environments, the architecture autonomously allocates standard operations to the robotic agent while safely deferring tasks requiring high dexterity to human operators. By combining the semantic adaptability of LLMs with the rigorous state validation of symbolic planners, this methodology ensures deterministic, safe, and efficient manufacturing workflows. The proposed architecture is evaluated against both physical and logical criteria using task-specific evaluation metrics.

Abstract: This article discusses a study on the intelligent analysis of multisensory biogas data obtained from an experimental dataset generated by a Lab-on-Chip platform. The relevance of this work stems from the need for real-time monitoring of biogas quality and biomass condition under anaerobic digestion conditions, where changes in the concentrations of methane, carbon dioxide, hydrogen sulfide, oxygen, and temperature directly affect the stability of the technological process and the energy efficiency of the plant. This study utilizes a multisensor Lab-on-Chip/biosensor platform designed for rapid analysis of small samples of biogas, biomass, and biomix. The platform integrates gas, liquid, and optical sensor channels, as well as a module for transmitting data to the cloud. The experimental data obtained are processed using intelligent data analysis methods, including statistical analysis, correlation analysis, anomaly detection, and assessment of the relationships between monitored parameters. The scientific significance of this work lies in the application of an integrated approach to the analysis of multichannel experimental data obtained from the Lab-on-Chip platform, which enables a more accurate and timely assessment of the state of the biogas process. The practical significance lies in the ability to use the proposed approach for remote monitoring, early detection of anomalies, and improving the efficiency of biogas plant management.

Abstract: Single metal droplets are widely used in electronic packaging and flexible circuit printing. However, the high density and interfacial tension of liquid metals make it difficult for the jet to stably generate single metal droplets, which greatly restricts the development of pneumatic drop on demand metal droplet ejection technology. In this paper, through experiments and numerical simulations, the jet motion states and internal fluid flow transition mechanisms during metal droplet generation are systematically investigated, and a dimensionless limiting jet length prediction model for stable single metal droplet generation is established. The results show that the dynamic competition and dominance transition between inertial force and interfacial capillary force fundamentally determine the jet motion states and internal fluid transition mechanisms. The pulse width is responsible for “activating” and “sustaining” the jet behavior, while the limiting jet length is primarily governed by the velocity provided by the supply pressure. The predictions of the dimensionless limiting jet length model are consistent with the experimental results, indicating that the model can effectively predict the limiting jet length for generating single metal droplets. The findings of this study are of significant importance for revealing the evolution mechanism of the jet during pneumatic drop on demand metal droplet generation and for achieving stable generation of single metal droplets, providing a theoretical basis for the further development of pneumatic drop on demand metal droplet additive manufacturing technology.

Abstract: Polarization control is essential for modern optical systems. Recently, two-dimensional (2D) materials featuring highly anisotropic absorption across broad wavelength regions have emerged as a promising solution for realizing optical polarizers with operation bandwidths far exceeding those achievable using bulk materials. Despite this, existing 2D-material-based optical polarizers face critical limitations in simultaneously delivering high polarizer figures of merit (FOMs), robust power endurance, and scalable fabrication, hindering their practical deployment beyond laboratory. Here, we overcome this bottleneck by demonstrating optical polarizers through integrating 2D MXene films onto silicon photonic devices and subsequently converting them in-situ into 2D titanium dioxide (TiO₂). The 2D TiO₂ exhibits significantly improved anisotropic absorption and thermal / chemical stability compared with MXene, enabling the hybrid polarizers to achieve a record high polarizer FOM of ~17.5 (among 2D-material-coated devices) and excellent power tolerance (stable under >2 W coupled average power), together with a broad operation bandwidth (>140 nm) and a high fabrication yield (~98%). We also demonstrate microring resonator polarizers achieving high polarization extinction ratios up to ~12 dB. With outstanding overall performance and strong capability for large-scale manufacturing, these polarizers represent a solid step towards industry-ready implementations of 2D-material-based optical polarizers.

Abstract: This paper presents an experimental study on extrudable cob-based and hemp-based materials, aiming to investigate their feasibility for 3D printing and their performance as sustainable construction materials. Cobcrete, a mixture of clay, sand, lime, straw, and water, and hempcrete, a composite material made from hemp hurd fiber and lime-based binders, have gained attention for their potential as a new construction material and possibly in 3D printing applications. The study focuses on assessing the workability, mechanical properties, and environmental sustainability aspects of these materials. A series of laboratory experiments are conducted to evaluate the printability, compressive strength, flexural strength, and water absorption characteristics of the extruded cobcrete and hempcrete specimens. Additionally, the impact of different mix ratios and curing conditions on the material performance is examined. The findings reveal that both cobcrete and hempcrete exhibit favorable printability potential and demonstrate promising mechanical properties suitable for construction applications. Moreover, these materials showcase sustainability advantages, including low embodied energy and potential carbon sequestration for hempcrete. The study contributes to the understanding of the feasibility and performance of 3D printable cobcrete and hempcrete, providing insights into their potential as eco-friendly alternatives in the construction industry. The results underscore the need for further research and development to optimize these materials for broader adoption and to address challenges such as standardization, regulatory compliance, and market acceptance. Ultimately, this study paves the way for utilizing 3D printable cobcrete and hempcrete in sustainable construction practices, contributing to the development of greener and more environmentally conscious building technologies.

Abstract: This study presents a synergetic control approach for coordinating the motion of a UAV swarm based on self-organization principles and energy-potential interactions. The proposed method effectively addresses the known limitations of traditional potential field techniques. A procedure for synthesizing synergetic control of swarm motion is developed using a virtual force field with symmetric attraction and repulsion functions, enabling natural collision avoidance and stable formation maintenance. Damping terms are incorporated into the equations of motion to eliminate self-oscillations and ensure asymptotic stability. A synergetic controller based on virtual-pendulum meters is introduced to prevent stagnation. The curse of dimensionality is mitigated by a specialized mathematical framework, as confirmed by simulations. The swarm-formation time increases sublinearly with the number of UAVs, highlighting the scalability of the approach. A modeling study of swarm self-organization reveals three characteristic phases: chaotic motions, transitional reconfiguration, and ordered swarm behavior. The formation process corresponds to a gradual minimization of inter-UAV interaction energy, while stable collective motion is achieved by maintaining a constant polarization of vehicle heading angles. Simulations also identify parameter ranges that provide optimal synergetic properties during swarm formation and coordinated movement. The findings are further validated through real-world experimental tests of swarm control.

Abstract: To address the challenge of uneven coating deposition during air-assisted robotic spraying on horn-shaped surfaces, a gas–liquid coupled numerical model based on the Euler-Euler multiphase flow approach was developed and experimentally validated. Two dynamic spray trajectories (axial spraying and circumferential spraying) were designed to investigate the effects of spraying strategy, horn angle (45°–135°), spray distance, and spraying speed on film-forming characteristics. Results indicate that horn features alter the near-wall flow field through wall confinement effects, causing spray splitting and kinetic energy attenuation with significant anisotropy—stronger geometric constraints along the minor axis and weaker along the major axis. Axial spraying requires approximately 60% less time than circumferential spraying and yields more uniform coatings, making it the preferred strategy. During axial spraying, the central film thickness increases monotonically with horn angle (up to 147.7% over 45°–135°), while circumferential spraying achieves optimal uniformity at 67.5°–90° governed by competing geometric and temporal effects. The film thickness approximately follows a power-law decay with spraying speed (h ∝ 1/v). Pearson correlation analysis identifies horn angle as the primary variable controlling film thickness (r = 0.466), followed by spraying speed (r = −0.347) and spray distance (r = −0.325), with an optimal spray distance of 190–210 mm recommended. This study reveals the anisotropic regulation mechanism of horn features on the spray flow field and provides quantitative guidance for precise robotic spraying of complex-curved components.

Abstract: Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accessible tools for early risk assessment. This study investigates the use of established machine learning (ML) techniques to predict heart disease risk from self-reported information that can be collected through telephone or online health questionnaires. The analysis is based on the publicly available Heart-2020 dataset, derived from the U.S. Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). Four widely used ML models were trained and evaluated: Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Bagged Trees (BT), and Extreme Gradient Boosting (XGBoost). Model performance was assessed using precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC). To further improve predictive performance and robustness, an ensemble architecture based on a second-stage MLP was implemented to combine the outputs of the individual classifiers. In addition, several feature engineering techniques, including Pearson correlation analysis, Analysis of Variance (ANOVA), and Principal Component Analysis (PCA), were investigated. The proposed approaches achieved F1-scores exceeding 0.80, demonstrating strong predictive capability using only non-clinical, self-reported information. These findings suggest that simple questionnaire-based data can support automated early-warning systems capable of identifying individuals who may benefit from further medical evaluation. Beyond individual risk assessment, the proposed methodology could also facilitate large-scale population health monitoring, contributing to preventive healthcare strategies and informed public health policy development.

Abstract: This paper investigates stable and compact diagnostic feature signatures for faults related to demagnetization in BLDC/PMSM drives. Discovery analysis on the public DUDU-BLDC benchmark compares current, speed, and combined representations under explicit top-k budgets using ReliefF, mRMR, LASSO, and Bayesian automatic relevance determination logistic ranking. A second March 2026 experimental campaign, released as DUDU-BLDC 1.5, comprises 50 recordings from five physical motors under altered acquisition conditions. On this second campaign, five-fold recording-grouped validation with three deterministic repeats reached balanced accuracies of 0.806 and 0.779 for the two confirmatory within-motor tasks. Motor-held-out balanced accuracies fell to 0.512 and 0.604, with most physical-motor estimates near chance, exposing substantial between-motor heterogeneity. A paired experiment that quantised the DUDU-BLDC raw current signals to the approximately 0.08 A resolution of DUDU-BLDC 1.5 produced a mean absolute balanced-accuracy change of 0.0046, although the maximum change was 0.0725 and individual ranking-stability changes were larger. The results support compact signatures for repeated monitoring within an established or calibrated motor population and show aggregate robustness to reduced current resolution. They do not establish universal transfer to unseen motor instances; broader deployment requires motor-specific calibration or more diverse multi-motor training data.

Abstract: This work systematically evaluates the capability of generative large language models (LLMs), specifically GPT-4o, to support the full design, simulation, and optimization workflow of low-dropout (LDO) linear voltage regulators. The study covers four core design phases: pre-design specification mapping, transistor-level circuit topology generation, SPICE simulation guidance, and post-simulation performance fine-tuning, with an extended investigation into the integration of magnetic inductive components within the LDO signal path. GPT-4o autonomously proposes a single-stage differential-pair error amplifier architecture with thin-oxide MOS transistors, provides sizing guidance for the PMOS pass element, and recommends passive compensation networks to secure closed-loop stability. The LDO testbench adheres to low-voltage portable electronics specifications: an input range of 0.8-1.2 V, tunable 0.7-1.1 V output, maximum 250 mA load current, and integrable output capacitance below 10 nF. Transient and small-signal AC SPICE simulations validate LLM-assisted circuit implementations, quantifying settling time reduction via compensation capacitors and verifying adequate phase margin across operating bandwidth. A key novel extension explores three distinct inductor placement schemes, input-side supply filtering, out-of-loop LC output filtering, and inductive loading embedded within the feedback divider, rooted in Maxwell's electrodynamic principles and MOSFET small-signal device physics. Comparative Bode and output impedance analysis reveals that inductors inserted inside the feedback path introduce resonant complex poles, severe gain peaking, and degraded phase margin, while inductors placed external to the feedback sensing tap preserve regulator stability while suppressing high-frequency electromagnetic interference. Despite robust performance in topology drafting and simulation instruction, GPT-4o exhibits notable limitations: it occasionally omits critical passive components matching design constraints, generates syntactically flawed SPICE netlists for custom transistor subcircuits, and fails self-correction for unconventional magnetic load configurations. Overall, this work demonstrates that LLMs function as powerful auxiliary engineering assistants to accelerate iterative analog design, though rigorous human cross-verification of component sizing, loop stability, and passive network topology remains mandatory for reliable LDO implementation, particularly when integrating inductive magnetic elements.

Abstract: This study employs a multi-method, high-precision research approach to evaluate the thermal comfort performance of a window-based direct evaporative cooling (DEC) air conditioning system installed in a university dormitory building (Building 6, 50 rooms) in Beijing. To compensate for the insufficiency of single-day test data, the study was conducted continuously for 30 days from June 1st to 30th, 2026 (00:00–23:59 daily). The research integrates field physical measurement data, standardized subjective questionnaire surveys (200 questionnaires in total), and advanced computational thermophysiological modeling results based on the frameworks of ISO 7730–2021 and ASHRAE Standard 55–2023. Environmental parameters, including dry-bulb temperature (Ta), relative humidity (RH), air velocity (Va), and mean radiant temperature (Tr), were monitored at eight spatially distributed points with a 10 Hz sampling frequency and a one-hour median resolution. Simultaneously, through confirmatory pre- and post-questionnaires compliant with ISO 10551 and the Appendix B requirements of ANSI/ASHRAE Standard 55, data on clothing ensembles, activity levels, and subjective thermal sensation votes (TSV) were collected. The acquired data were input into a customized simulation platform developed in the Fortran language, which employs the Fanger two-node thermoregulation model to accurately calculate and predict the predicted mean vote (PMV), predicted percentage of dissatisfied (PPD), new effective temperature (ET*), and standard effective temperature (SET*). The results indicate that the DEC unit achieved a stable outlet temperature reduction of t =3.87°C (inlet temperature 31.72°C, outlet temperature 27.85°C), with a stable average wet-bulb air temperature was 18.66°C while maintaining a relative humidity of 42.07%—a result particularly crucial for Beijing's high-humidity summer environment.

Abstract: This study presents a spatiotemporal analysis and predictive modeling approach for assessing distributed photovoltaic potential in La Maná, Ecuador, using hourly NASA POWER satellite data from 2014 to 2025. A total of 105,168 hourly records of global horizontal irradiance, air temperature, relative humidity, and wind speed were processed. The methodology included data cleaning, temporal feature generation, hourly, monthly, and interannual solar resource analysis, and the comparison of machine learning models for predicting global horizontal irradiance as an indicator of photovoltaic potential. The results showed a relatively stable solar resource throughout the study period, with hourly maximum values occurring around midday and low season-al variability. Among the evaluated models, Gradient Boosting achieved the best predictive performance, with an MAE of 35.14 W/m², an RMSE of 68.44 W/m², and a coefficient of determination of 0.8875. The feature importance analysis revealed that the hourly component dominated solar resource prediction; however, when this effect was excluded, air temperature emerged as the most influential meteorological variable. These findings demonstrate that integrating hourly satellite data with machine learning provides a robust alternative for evaluating photovoltaic potential in tropical regions with limited ground-based meteorological monitoring infrastructure.

Abstract: Electrical machines, including motors, generators, and transformers, are fundamental components of modern industrial systems, power networks, transportation infrastructure, and renewable energy installations. Their reliable operation is essential for maintaining system efficiency, operational safety, and economic performance. However, these machines are continuously exposed to electrical, mechanical, thermal, and environmental stresses that can lead to performance degradation and unexpected failures. Conventional condition monitoring and maintenance approaches, such as vibration analysis, Motor Current Signature Analysis (MCSA), infrared thermography, acoustic emission monitoring, partial discharge testing, and Dissolved Gas Analysis (DGA), have been widely used for machine health assessment. Despite their effectiveness, these methods often require expert interpretation, extensive manual analysis, and periodic inspections, limiting their ability to detect incipient faults and support predictive maintenance. Recent advances in sensing technologies, the Industrial Internet of Things (IIoT), edge computing, and cloud analytics have enabled the collection of large volumes of operational data, creating new opportunities for Artificial Intelligence (AI)-based health management systems. This review presents a comprehensive overview of AI applications in electrical machine health monitoring, fault diagnosis, and predictive maintenance. The paper discusses electrical machine classifications, common fault mechanisms, condition monitoring data sources, maintenance strategies, and the limitations of conventional diagnostic approaches. Furthermore, the review examines machine learning (ML), deep learning (DL), hybrid AI approaches, and Explainable Artificial Intelligence (XAI) techniques for motors, generators, and transformers. Finally, key challenges related to data quality, model interpretability, computational complexity, cybersecurity, and standardization are discussed, together with future research directions involving Digital Twins, multimodal data fusion, federated learning, and edge intelligence.

Abstract: Background: Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. Aim: To develop and evaluate a two-stage ensemble machine learning pipeline for breast cancer diagnosis from digital mammograms. Methods: The proposed framework combines image preprocessing, multiple convolutional neural networks trained under different conditions, and a second-stage classifier that integrates the CNN outputs. Several machine learning models and feature selection techniques were evaluated using publicly available mammography datasets. Results: The ensemble approach consistently outperformed the individual CNN models. The MLP classifier achieved the best overall balance between precision and recall, while the heuristic fusion method provided the highest sensitivity. Feature selection reduced model complexity while maintaining comparable performance, and cross-validation confirmed the robustness of the proposed methodology. Discussion: Combining complementary information from multiple CNNs with classical machine learning improves diagnostic performance and provides a robust framework for computer-aided breast cancer diagnosis. Conclusions: The proposed two-stage ensemble offers an effective and interpretable approach for mammographic breast cancer classification. A demonstration application incorporating Grad-CAM explainability further supports its potential use as a clinical decision-support tool.

Abstract: This study presents the development and comprehensive characterisation of a sustainable 3D-printable cementitious composition in which up to 40 wt.% of Portland cement was replaced by a ternary binder containing oil shale ash (OSA) and metakaolin (MK). Following laboratory optimisation, the developed formulations were successfully transferred to industrial production as pre-blended dry mixes at Sakret Latvia Ltd., demonstrating the feasibility of large-scale manufacturing of printable cementitious materials. Attention was devoted to the characterisation of the raw materials and dry mixtures using particle size distribution (PSD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and X-ray diffraction (XRD). Two compositions—a reference mixture (REF) and the ternary OSA mixture—were evaluated in terms of printability, mechanical performance, durability, and the anisotropic behaviour of 3D-printed elements. The ternary composition (due to the pozzolanic activity of MK and OSA) exhibited strength development resulting in compressive strength exceeding that of the reference mixture after 90 days of curing. The anisotropy study revealed a difference in the mechanical properties of printed samples compared with conventionally cast samples. Durability assessment, including capillary water absorption and surface freeze–thaw scaling tests performed using two standardised methods, demonstrated satisfactory frost resistance and confirmed the suitability of both mixtures for outdoor applications. The results further indicate that the layered manufacturing process governs moisture transport and mechanical anisotropy through interlayer interfaces. The developed pre-blended OSA–MK composite represents a promising low-carbon material for industrial 3D concrete printing, combining reduced cement consumption with reliable printability, mechanical performance, and durability.

Abstract: Deep saline aquifers serve as primary locations for storing large amounts of CO₂ but their injectivity faces a major threat due to salt accumulation in the reservoir pores. The research team conducts experiments to study how Brine chemistry affects salt precipitation, and they test different methods for removing these salts through laboratory experiments. The research team performed core-flooding tests on Bentheimer and Salt Wash North sandstone cores which they treated with mono- and divalent saturated (20 wt%) Brines (NaCl, KCl, CaCl₂, MgCl₂) before they injected supercritical CO₂ to create salt deposits and then tested low‑salinity Brine (0.5 wt%) and seawater (3.5 wt%) for their ability to remove these deposits. The research shows that salt deposits lead to lower porosity and permeability which causes restricted water movement and results in complete loss of injection ability. The process of salt buildup in divalent Brines generates more severe pore blocking than monovalent Brines which leads to faster CO₂ breakthrough. The research shows that low‑salinity Brine outperforms seawater for total permeability recovery because it produces recovery rates between 40.6 and 68.4 percent while seawater produces recovery rates between 7.4 and 17.2 percent under the same experimental conditions. The research establishes a direct connection between Brine chemical composition and salt formation which leads to decreased water flow through injection systems. The research methods followed identical patterns which demonstrated steady experimental results through multiple tests. The research findings received support from stable pressure values and flooding test repetition and SEM–EDX analysis which showed salt deposits forming in pores and disappearing during the treatment process. The research results demonstrate that salt buildup stands as the main factor which leads to reduced injection capacity. The study shows that low-salinity water serves as an effective treatment to restore permeability when compared to seawater.