Critical Performance Parameters Research Articles

Microwave-aided sensitive and eco-friendly HPTLC method for estimation of mirabegron using NBD-Cl as bio-sensing fluorescent probe by Integration of principal component Analysis, response surface modelling and white analytical chemistry approach

A large number of analytical methods have been used in in-vivo pharmacokinetic investigations and assays of mirabegron, which is used to treat overactive bladder. Because mirabegron does not give fluorescence, there is no known chromatographic method that uses this property in conjunction with environmentally safe solvents. In order to develop and assess analytical procedures that are precise, sensitive, environmentally friendly, economical, and easy to use, white analytical chemistry was recently presented. Hence, a microwave-aided HPTLC method with fluorescence detection was developed and applied for mirabegron quantification; this method is both sensitive and environmentally acceptable, and it makes use of NBD-Cl, a bio-sensing fluorescent-probe. The analytical-QbD approach, which includes chemometric and response-surface modeling concepts, was used to design this targeted chromatographic method. Critical method variables and performance parameters were identified and optimized using principal component analysis and Box-Behnken design. The targeted method was used to estimate mirabegron through the development of method operable design range navigation and control strategy. A mobile phase consisting of ethyl-acetate and ethanol (8.0 + 2.0, v/v) was used to develop the chromatogram of NBD-Cl derivatised-mirabegron into 8.0 mm bands on a TLC-plate that was supported with aluminum and precoated with silica-gel G60 F254. In a mirabegron concentration range of 50–250 ng/band, the suggested method exhibited linearity. Results showed that the suggested method has a recovery rate of 98 % to 102 %. When estimating mirabegron, the %RSD for the precision and robustness study was determined to be less than 2.0. The mirabegron %assay in the commercially available formulations was determined to be between 98 % and 102 % of the labelled claimed value. Mirabegron had a limit of detection of 10 pg/band and a limit of quantification of 50 pg/band. Marketed tablet formulations of mirabegron had a maximum plasma concentration of 71.58 ng/mL in rats. Using metrics from green analytical chemistry and the RGB model scoring system, we compared the whiteness and greenness profiles of the published and suggested methods. The suggested method was found to be sensitive, eco-friendly, quick, cost-effective, and easy to use for estimation of mirabegron.

Multi-objective optimization of a novel hybrid battery thermal management system using response surface method

This study evaluates the thermal performance of a Z-type battery thermal management system (BTMS) designed for nine lithium-ion batteries discharged at a high rate of 5C, using Computational Fluid Dynamics (CFD) simulations. The investigation employs Response Surface Methodology (RSM) to optimize two critical thermal performance parameters: the maximum battery temperature (Tmax) and the maximum temperature difference between cells (ΔTmax). Various cooling strategies are explored to comprehensively assess the BTMS, including natural convection, forced convection, cooling fins, phase change material (PCM), and composite PCM. These methods are analyzed to determine their effectiveness in controlling the thermal behavior of the battery pack. The simulation results indicate that integrating different cooling techniques can significantly lower Tmax from 352.38 K to 309.14 K and reduce ΔTmax from 14.6 K to 3.31 K, depending on the method used. Under critical conditions, such as the failure of the active cooling system, the BTMS still maintained a Tmax of 310.64 K and a ΔTmax of 0.95 K, demonstrating its robustness and reliability. Further optimization identified the ideal configuration for the system, including an inlet air speed of 1.2 m/s, an inlet temperature of 297.15 K, and a PCM thickness of 3.8 mm, achieving optimal thermal performance with a Tmax of 303.97 K and ΔTmax of 3.17 K. This study offers valuable insights into the design and optimization of effective BTMS for enhanced battery safety and longevity.

Advanced HPLC Method with Diode Array Detection Using a Phenyl-Bonded Column for Simultaneous Quantitation of Three Sunscreen Filters in a Moisturizing Sunscreen Cream for Acne-Prone Skin

This study introduces a novel, robust, and efficient method for the simultaneous quantitative determination of three sunscreen filters, namely, 4-methylbenzylidene camphor, octyl methoxycinnamate, and avobenzone, in a moisturizing sunscreen cream specifically designed for acne-prone skin. The method employs high-performance liquid chromatography with photodiode-array detection, providing a reliable separation of the analytes. Chromatographic separation was achieved using a Fortis Phenyl analytical column (150.0 × 2.1 mm, 5 μm), with isocratic elution at a flow rate of 0.4 mL/min. The mobile phase was composed of a 57/43 (v/v) mixture of acetonitrile/45 mM aqueous ammonium formate solution, ensuring sufficient resolution and peak symmetry for the target compounds. The method was validated comprehensively for critical performance parameters, including linearity, precision, accuracy, and robustness. Linearity was established across a suitable range for all three analytes, with high correlation coefficients. Precision was confirmed with intra-run and total precision coefficients of variation of ≤4.6%, while accuracy assessments yielded a percent recovery between 98.6 and 100.4, for all quality control levels. Additionally, the method was able to effectively separate the sunscreen filters from other cosmetic ingredients, such as [β-(1.3), (1.6)-D-glucan], low molecular weight (LMW) hyaluronic acid and plant extracts ensuring specificity in complex formulations. This straightforward and time efficient sample preparation process, involving methanol extraction followed by serial dilution, makes the method suitable for routine quality control in cosmetic laboratories. The method was successfully applied to the analysis of two different lots of a commercial sunscreen cream, achieving excellent recovery for all filters, ranging between 94.6% and 99.8%, thus demonstrating its reliability and applicability for the quality control of cosmetics.

Aspect ratio optimization of piezoelectric extensional mode resonators for quality factor and phase noise performance enhancement

Abstract This study focuses on optimizing the resonator geometry via the aspect ratio design of a width-extensional mode resonator to improve its quality factor (Q), which is one of the critical performance parameters for resonators in either sensing (Allan deviation) or frequency reference (phase noise) applications. The proposed approach uses finite element analysis to reduce the strain energy at anchor supports by altering the resonator geometric structure, thereby reducing energy loss through anchors. Moreover, process limitations on feature sizes are used as constraints to find aspect ratios that can not only increase the Q but also reduce spurious modes near the targeted frequency. The devices were fabricated using AlN thin film piezoelectric on a substrate (TPoS) process. The simulated energy dissipation trends for specific length-to-width (L/W) ratios closely match the measured changes in the resonator Q values in vacuum. In vacuum, the highest Q-factor achieved by the device is close to 8816, with a motional resistance of a few tens of ohms. Additionally, a board-level oscillator realized using a commercial low-noise amplifier exhibits phase noise performance of −141.21 dBc Hz−1 and −164.25 dBc Hz−1 at 1 kHz and 1 MHz frequency offsets, respectively. The calculated figures of merit for these offsets are 204 and 168, respectively.

Experimental and machine learning study on the influence of nanoparticle size and pulsating flow on heat transfer performance in nanofluid-jet impingement cooling

Maximizing heat transfer efficiency is crucial for enhancing performance and durability in diverse engineering applications, including fuel cells, EV batteries, and solar PV/T systems, thereby advancing sustainable energy innovation. This study investigates thermal dissipation from a simulated heat sink aligned with a PV cell’s back plate via jet impingement cooling. Specifically, it examines the impacts of pulsatile cooling and nanoparticle size in hybrid nanofluids, comprising combinations of Al2O3 and MWCNT in water, with varied nanofluid volume fraction (0.05 vol% ≤ ɸ ≤ 0.3 vol%) and flow Reynolds number (15000 < Re < 40000). Key findings reveal significant influences of nanoparticle size, nanofluid concentration, and pulsating flow on heat transfer performance. Notably, sample D demonstrated the highest heat transfer enhancement, achieving approximately 52.94 % and 79.06 % improvement in continuous and pulsating jet cooling compared to de-ionized water under continuous jet cooling. Machine learning classifiers were employed to identify critical thermal performance parameters, with Reynolds number identified as the most significant factor influencing heat transfer. Random Forest and Gradient Boosting classifiers showed notable accuracy in predicting Nu, emphasizing the role of machine learning techniques in optimizing thermal management strategies for improved heat dissipation from solar PV cell backplates.

Experimental Performance Evaluation of a Lightweight Additively Manufactured Hydrodynamic Thrust Bearing

In this paper, a lightweight additively manufactured (AM) fixed geometry hydrodynamic thrust bearing fabricated via laser powder bed fusion (LPBF) is experimentally compared to a traditionally manufactured cast aluminum alloy thrust bearing of similar design. The purpose of this study is to evaluate how weight-saving design features in the AM bearing affect active critical hydrodynamic performance parameters to better understand in-service viability. Under various static operating conditions, performance parameters such as hydrodynamic pressure distribution, minimum oil film thickness (MOFT), bearing temperature and increase in oil temperature are measured. Compared to the traditionally manufactured bearing, the AM bearing showed an average increase in minimum oil film thickness of 53%, an average increase in trailing edge hydrodynamic pressure of 116%, while exhibiting an average decrease in bearing temperature of 1%. Experimental results are compared to numerical simulation showing reasonably good agreement.

Hydrodynamic and energy-harvesting performances of a compact-array OWC device: An experimental study

This study proposed and evaluated a compact-array oscillating water column device called “Rainbow”, which is expected to adapt to the nearshore wave climate characteristics in China, and satisfy the quick response requirements to coastal and offshore electricity. The proposed device employs a bent-duct buoy concept, with an open mouth in two opposite directions, and embraced a moon pool in a ring shape. Moreover, models under a scale ratio of 1:10 are designed and manufactured, which are fixed on the bottom and tested in regular and irregular wave scenarios in a wave tank. The time histories of critical performance parameters, hydrodynamic and aerodynamic performances, and capture width ratios in different modules were recorded and analyzed to reveal the operating mechanisms of the model. The various module arrangement topologies were found to cause significant differences in the value distribution characteristics of the capture width ratio in different modules, peaking at 0.26. The experimental results can be employed as critical benchmark cases for further designs and optimizations.

Performance prediction of sludge volume index of oxygenic photogranule based wastewater treatment system using machine learning algorithms

Oxygenic Photogranulation is a novel biotechnology that treats wastewater without external aeration and produces biomass in dense photogranules with high settling velocities. Oxygenic photogranules (OPG) based wastewater treatment (WWT) faces challenges during scaleup due to its dynamic and complex system variables, making troubleshooting costly. The Machine Learning (ML) approach can address this issue by creating a WWT process simulation. Moreover, traditional mechanistic models do not capture the interaction between input and output features due to their high dimensionality and the non-linear relationship, making them computationally expensive. In this study, the two-stage feature selection (FS) method is studied to enhance the prediction performance of SVI30, a critical operational parameter, to ensure optimal settleability and minimal loss of active photogranules. The optimal feature subsets generated by the two-stage selection method were evaluated using four regression models: decision tree, random forest, gradient boosting, and extreme gradient boosting. Results indicate that, among all regression models, the decision tree performs well having a prediction efficiency of 85 % with the subset of features obtained after Recursive Feature Elimination (RFE) of decision tree features in the second stage. This indicates the effectiveness of the two-stage FS method in identifying the most relevant features for predicting SVI30. The structured approach of FS and model evaluation highlights the potential of ML in addressing complex operational challenges in OPG WWT operations.

One dimensional multilayer structure for kerosene adulteration detection in petrol and diesel

Abstract We aim to design a multilayer periodic structure to detect the kerosene adulteration in the petrol and diesel fuels on a single processing setup. We built the structure by arranging TiO2 and SiO2 layers periodically in alternate manner with empty space at the middle. The optical response of the designed structure is then theoretically studied using transfer matrix method. Empty space of the design is filled with different kerosene adulterated petrol and diesel samples for inspecting structure response to incident electromagnetic waves. Theoretical calculations affirm that the proposed structure is capable to detect the fuel adulteration efficiently. Critical performance parameters like sensitivity, quality factor, figure of merit and limit of detection are calculated to check the effectiveness of the sensor. We also optimized the sensitivity of sensors design through modifying the defect thickness as well as angle of incidence of electromagnetic waves, and provides poly fit formula. It is observed that the sensitivity of sensor to detect kerosene adulteration in fuels highly depends on defect thickness and light incidence angle. An efficient sensor possessing high quality factor and figure of merit is realized from the designed structure that is capable of detecting 10 − 5 RIU.

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. The Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93 % and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

Exergy, exergoeconomic optimization and exergoenvironmental analysis of a hybrid solar, wind, and marine energy power system: A strategy for carbon-free electrical production

In this research, aligned with global policies aimed at reducing CO2 emissions from traditional power plants, we developed a holistic energy system utilizing solar, wind, and ocean thermal energy sources, tailored to regions optimal for ocean thermal energy conversion (OTEC). The selected site, characterized by favorable wind and solar conditions close to areas with high OTEC potential, is designed to meet the electricity needs of a coastal community. The system's core components include an Organic Rankine Cycle, turbines, thermoelectric elements, pumps, a heat exchanger, a wind turbine, and a solar collector. A detailed system analysis and thermodynamic evaluation based on thermodynamic principles were carried out using the Engineering Equation Solver (EES) software. Key factors such as wind speed, solar radiation, and collector area were critical in determining system performance. To enhance the system's effectiveness, we conducted a comprehensive comparison of optimization algorithms, incorporating the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and utilizing a Pareto front for value optimization. This approach significantly outperformed other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of system efficiency and cost-effectiveness. The developed system achieved an exergy efficiency of 14.46 % and a cost rate of $74.98 per hour, demonstrating its suitability for its intended functions. Moreover, exergoenvironmental evaluation was conducted for the proposed plant. The findings revealed that key component HEX has a high exergoenvironmental factor due to their use of hot water, which has zero unit exergoenvironmental impact. Additionally, pumps demonstrated a zero exergoenvironmental impact factor, indicating negligible component-related environmental impacts. Sensitivity analysis further evaluated critical performance parameters, revealing that increases in solar irradiation lead to decreased total system cost rates, while higher turbine temperatures resulted in a remarkable 14.08 % reduction in the system's cost rate. These results underscore the economic viability of operating the system at higher temperatures and strengthen the argument for its adoption from a financial perspective.

A study of kapton as a flexible substrate for perovskite solar cells; advantages and disadvantages

The increasing usage of perovskite solar cells is being driven by their high efficiency and cost-effective manufacture. Polyethylene Terephthalate (PET) and Polyethylene Naphthalate (PEN) are flexible, transparent, and cost-effective. However, issues have been raised about their long-term stability, especially to the sensitivity of perovskite materials to environmental conditions like as moisture and heat, as well as the challenge of oxygen and moisture transmission across these substrates. Researchers are investigating Polyimide (PI), particularly colorless PI, for enhanced stability, but the techniques are costly. Kapton, a popular colored PI, provides excellent thermal and electrical insulation, but its reduced transparency provides optical issues. In this study, finite-difference-time-domain (FDTD) modeling and experimental analysis were utilized to investigate the optical properties and efficiency of perovskite solar cells on Kapton, PET, and glass substrates. According to the modeling, using Kapton instead of traditional substrates leads to a decrease of 16.6 % in the solar cells short circuit current (JSC). In laboratory experiments, by adding lithium (Li) as the dopant in the electron transport layer (ETL), the sheet resistance of the ETL was reduced from 214 to 2.4 kΩ/□. According to modeling, this enhancement resulted in a 3 % increase in the power conversion efficiency (PCE) of the solar cell, increasing to 17.4 % compared to 16.8 % on a PET substrate. Furthermore, using a Kapton substrate improves critical performance parameters like open circuit voltage (VOC) and fill factor (FF).

Sensitivity enhanced flexible capacitive pressure sensor microstructure optimization for biomedical applications

This paper explores the design and optimization of Flexible Capacitive Pressure Sensors (FCPS) using microfabrication technology for applications in the emerging field of flexible electronics, with a particular focus on measuring bio-signals characterized by lower pressure ranges. Sensitivity, a critical parameter for effective FCPS performance, is investigated through a comprehensive series of simulation analyses employing finite element modeling. The study involves varying geometrical and mechanical parameters that influence FCPS performance, individually adjusting each parameter while keeping others constant. Microstructures such as cuboids, truncated pyramids with an aspect ratio of 0.5, cylinders, pyramids, and cones are modeled on the dielectric material surface. The parameters considered include inter-space, base length, height, and elastic modulus, to enhance FCPS sensitivity and linearity. Among the different shapes modeled, the cone exhibits the highest sensitivity, followed by the pyramid structure. Comparative analysis indicates that the cone and pyramid shapes demonstrate 15- and 10-times higher sensitivity, respectively, compared to the cuboid structure under an applied pressure of 10 Pa. Simulation results suggest that sensitivity can be finely tuned, with higher inter-space and microstructure height, as well as lower base length and Young’s modulus of the dielectric material, contributing to increased sensitivity. However, it is noted that these conditions may lead to decreased capacitance in the absence of applied pressure due to air occupation relative to the dielectric material. The findings are further compared with existing literature, and the FCPS response analysis provides valuable insights for the future design of FCPS, particularly in the context of biomedical applications requiring precise low-pressure signal measurements. This research contributes to advancing the understanding of FCPS performance optimization and lays the groundwork for the development of sensors with enhanced sensitivity for bio-medical applications.

Real-time Finite Element Analysis based on surrogate model

In the field of engineering, Finite Element Analysis (FEA) serves as a pivotal numerical simulation tool. Nevertheless, its inherent offline nature and dependency on real-time data pose limitations on its versatility. To confront this challenge, a real-time FEA approach centered on surrogate modeling has been developed. This method enables the instantaneous prediction of critical performance parameters, thereby providing crucial support for real-time decision-making and process monitoring. In the context of this study, the focus is on the glass compression molding process, a process that is not easily observable. The research commences with preliminary thermal conduction simulations conducted using ABAQUS software. Subsequently, a surrogate model is constructed to facilitate real-time FEA for the temperature field of the glass preform. Finally, Unity 3D software is harnessed for visualization, offering an effective tool for the real-time monitoring and control of the glass-forming process.

Blocker tolerant cascode LNA for Wifi &amp; IoT applications

SummaryWithin the domain of contemporary wireless communication systems, crafting Low‐Noise Amplifiers (LNA) holds a pivotal significance in achieving heightened signal sensitivity and comprehensive system efficacy and suppresses noise contributions from subsequent stages. Additionally, the low noise figure (NF) and high gain are critical LNA performance parameters in portable applications. Normally, LNA contains issues in noise performance, amplification, bandwidth, gain, and stability. To overcome these challenges, there must be a proper selection of transistors, harmonious impedance calibration, bias optimization, and circuit fine‐tuning. Moreover, metal oxide semiconductor field effect transistors (MOSFETs) are a popular choice in LNA design because of their excellent noise performance, high input impedance, complementary metal‐oxide semiconductor (CMOS) integration capabilities, low power operation, and suitability for a wide range of frequency applications. Through simulation and iterative enhancement, the LNA showcases remarkable Noise Figure, amplification, and linearity over a designated frequency span. This creation contributes to the advancement of radio frequency (RF) circuitry by designing an approach for LNA crafting that harmonizes conflicting prerequisites and unveils effective noise mitigation tactics. The outcomes emphasize the importance of a deliberated circuit architecture and parameter adjustment in accomplishing superlative LNA capabilities, rendering it fit for assimilation into diverse communication systems that demand minimal noise and heightened sensitivity.

Double Passivation Using Ultra‐Thin Insulators for Improved Stability and Efficiency of Quasi‐2D Perovskite Light Emitting Diodes

AbstractInterface engineering is a critical parameter for optimal optoelectronic device performance. In quasi‐2D perovskite light emitting diodes (PeLEDs), traditional poly (3,4‐ethylene dioxythiophene) polystyrene sulfonate (PEDOT:PSS) hole injection layer (HIL) is replaced by self‐assembled monolayer (SAM) [2‐(3,6‐dibromo‐9H‐carbazol‐9‐yl) ethyl] phosphonic acid (Br‐2PACz) HIL. The deep highest occupied molecular orbital (HOMO @ −5.73 eV) of Br‐2PACz facilitates the efficient hole‐injection along with a reduction in leakage current by 4–5 orders of magnitude as compared to PEDOT:PSS (HOMO @ −5.00 eV) HIL based optimized PeLEDs. Ultra‐thin SAM‐based Br‐2PACz HIL presents two challenges: i) device reproducibility is hindered by spatial inhomogeneity, and ii) it fails to regulate nonradiative recombination at the perovskite/HIL interface. To address these challenges, the ultra‐thin interlayer of atomic layer deposited aluminum oxide (Al2O3) is utilized between Br‐2PACz, and perovskite which aids in passivating the interfacial states at the HIL/perovskite interface. With the optimized thickness of Al2O3 interlayer (3 nm), the device's external quantum efficiency (EQE) improved from 9.47% to 13.14%, accompanied by luminous efficiency of 42.8 cd A−1. An ultra‐thin interlayer of lithium fluoride (LiF) (2 nm) is implemented on the top side of the perovskite, which can provide 1.5 times increase in the device's LT50 stability.

Direct ink writing of viscous inks in variable gravity regimes using parabolic flights

Additive manufacturing (AM) has significant utility for off-planet fabrication where dedicated infrastructure is severely limited, weight reduction and in situ resource utilization is desirable, and demands for complex systems are high. Direct ink writing (DIW) is a useful AM technique since it enables the deposition of a broad set of materials and the co-printing of multiple materials simultaneously. This allows for the fabrication of complex functional devices and systems in addition to structural objects. To evaluate this technique for space applications, this study characterized DIW in low gravity environments. Parabolic flights were used to simulate Martian, Lunar, and Micro gravity, and the effects that these 3 gravity regimes have on two critical print performance parameters, drooping and slumping, was evaluated using viscous paste inks deposited with an auger-driven extrusion head. In the drooping case, bridging structures were printed across gaps without support material, and the deformation was monitored. In the slumping case, a wall was printed through sequential layer deposition, and the vertical displacement of each layer under reduced gravity was explored. As expected, we found that a reduction in apparent gravity led to a decrease in the droop of a printed line, and as apparent gravitational acceleration is decreased its impact on the magnitude of drooping becomes less significant. For wall structures printed in simulated Lunar or Martian gravity regimes, the total structure height was found to be similar to that of a structure printed under Earth's gravitational conditions. In contrast, for prints performed in microgravity, it was found that slumping was significantly reduced and structure height was larger. These results provide experimental data to enable the design and optimization of appropriate structures and tool paths for printing objects using DIW for off-planet manufacturing.

Development and Evaluation of a MQ-5 Sensor-Based Condition Monitoring System for In-Situ Pipeline Leak Detection

The condition monitoring system for an in-situ pipeline is an innovative concept that uses MQ-5 sensors to detect fuel leaks in pipelines and relay concentration data to a receiver station. The essence of the monitoring process is to ensure the safety and security of engineering properties and lives. The project addresses the crucial requirement for rapid detection of fuel leaks to avoid environmental problems, economic losses, and safety risks connected with pipeline circulation. The goal is to create a leak detection monitoring system. The project required the design and execution of four transmitter stations, each responsible for detecting fuel leaks at various places along a small-scale pipeline and transmitting concentration data to a central receiver station. The system's response time, a critical performance parameter for this project, was measured by timing how long it took for an alert to arrive at the receiving station after the transmitter detected a leak. This time was measured at three distances between the transmitter and receiver stations: 1m, 2m, and 3m. Multiple measurements were taken at each distance, and the average response time was computed. The results showed that as the distance between the stations increased, so did the reaction time. The average response time at 1m was 3.37 seconds, whereas, at 2m and 3m, it was 3.856 seconds and 4.198 seconds, respectively. A t-test inference was performed to check that there was a distinct difference between the response times for each distance, and a significant difference was detected. The built system effectively demonstrated its ability to detect fuel leaks, and the observed response times offered useful information about the system's performance at various distances. This technology demonstrates the potential to improve pipeline transportation safety and efficiency by enabling early identification of fuel leaks.

Impact of Physico-Chemical Characteristics on the Mechanical Strength and Pore Structure of Air Lime Mortars with Isparta Tuff and Banahmeta Additives

The physical and chemical interactions between the lime and pozzolans in conservation mortars are fundamental to sustainable building practices. Here, we report experimental investigations on pure air lime mortar, air lime-isparta tuff mortar, and air lime-banahmeta mortar. Isparta Tuff is formed from volcanic rocks found in the Southwest between Isparta and Burdur city centres in Anatolia, belonging to the Gölcük volcanism. The microstructural and physiochemical interactions of these mixed designs were investigated. Importantly, this study quantifies critical performance parameters of air lime mortars incorporating Isparta tuff as a pozzolan. It supports using local and natural volcanic tuffs in developing sustainable mortars for the conservation of historical assets in Turkey.

A gas-driven capillary based on the synergy of the catalytic and photothermal effect of PB@Au for Salmonella typhimurium detection

Rapid detection method for Salmonella typhimurium is vital to prevent the spread of food-borne diseases. In this work, a gas-driven capillary detection method was established to achieve sensitive and rapid detection of Salmonella typhimurium using the catalytic and photothermal synergy of Prussian blue-nanogold (PB@Au) nanomaterials. The immuno-PB@Au probe attached to the capillary by specific identification of target bacteria catalyzed the H2O2 under laser irradiation, driving the H2O2 liquid column to move (ΔL) by producing gas, and achieving the quantitative detection of Salmonella typhimurium. After detailed optimization of the critical performance parameters of the gas-driven capillary assay, the limit of detection (LOD) after laser irradiation and being catalyzed by PB@Au was calculated to be 37 CFU mL−1 through the determination of different concentrations of target bacteria. Furthermore, the detection performances of the gas-driven capillary method were evaluated in detail, and the recoveries ranging from 92.9 ± 4.7 % to 107.7 ± 4.1 % were achieved using the spiked actual samples with complex matrices, indicating that the established rapid assay can offer promising strategies for the monitoring and controlling of food-borne pathogenic bacteria.