Economical Solution Research Articles

Curcuma Longa Derived Heteroatom‐Self‐Doped Porous Carbon for Cost‐Effective Solid‐State Supercapacitors

The cost‐effective synthesis of supercapacitors is a significant challenge in energy storage research. This study introduces a sustainable and cost‐effective method for synthesizing biomass‐derived carbon for solid‐state supercapacitor fabrication. Turmeric (Curcuma longa) plant waste is carbonized at three distinct temperatures (500, 600, and 700 °C for 3 hours), and the resulting carbon is characterized to determine the optimal carbonization conditions. Physicochemical characterization revealed the presence of multiple heteroatoms, which may contribute to enhanced capacitance. Electrochemical studies showed that the carbonized material at 600 °C achieved the highest specific capacitance of 110.04 F/g at 0.1 A/g current density. After activation, the specific capacitance increased to 188 F/g at 0.1 A/g current density. A solid‐state supercapacitor was assembled using the synthesized activated carbon and PVA/H2SO4 gel‐type electrolyte. The resulting device exhibited an impressive specific capacitance of 92.33 F/g at 0.1 A/g, a power density of 4295.28 W/kg, and a cycling stability of 97.42%. This supercapacitor shows promising potential as an economical and sustainable energy storage solution for portable electronics.

A Study on Development in Engineering Properties of Dense Grade Bituminous Mixes with Coal Ash by Using Natural Fiber

Coal-based thermal power plants in India produce significant waste, primarily fly ash and bottom ash, which poses environmental and health risks when disposed of in large quantities. This research explores the effective use of these waste products in bituminous paving mixes, substituting natural aggregates to conserve resources. Dense-graded bituminous macadam (DBM) specimens were prepared using natural coarse aggregates, bottom ash as fine aggregate, and fly ash as filler, with sisal fiber as a reinforcing additive. Varying percentages (0–1%) and lengths (5–20 mm) of SS1 emulsion- coated sisal fiber were tested to optimize the mix’s engineering properties. VG30 bitumen, chosen for its superior Marshall characteristics, resulted in an optimum binder content of 5.57%, fiber content of 0.5%, and fiber length of 10 mm, achieving a Marshall stability of 15 kN. Additional performance tests, including moisture susceptibility, indirect tensile strength (ITS), creep, and tensile strength ratio assessments, confirmed that the coal ash and sisal fiber mix significantly enhances pavement durability. This approach offers an eco-friendly and economical solution for bituminous pavement construction, addressing coal ash disposal challenges and conserving natural aggregates. Keywords: Bottom ash, Fly ash, Sisal fiber, Emulsion, Indirect tensile strength, Static creep test, Tensile strength ratio.

Leveraging Social Determinants of Health to Enhance Recruitment of Underrepresented Populations in Clinical Trials.

Historically marginalized communities are disproportionately affected by cardiometabolic diseases yet are underrepresented in clinical trials that investigate needed interventions. This review investigates the barriers to equitable inclusion in clinical trials, identifying opportunities for improvement at the institutional, trial, community, and individual level. It proposes a social determinants-based approach that serves as a toolkit to target these barriers using structural, economic, community, healthcare access, and technology solutions, supporting constructive improvement in the clinical trial recruitment process.

Visible light photocatalytic reduction of Toxic Chemical Organophosphate Monocrotophos using reduced Graphene Oxide derived from bamboo leaves

In this study, graphene oxide (GO) was synthesized from waste bamboo leaves using a pyrolysis and ultra-sonication technique. UV-visible spectroscopy revealed a prominent absorption peak at 230nm, while Raman spectroscopy confirmed the presence of characteristic D-band (1340cm⁻¹) and G-band (1596cm⁻¹). XRD analysis showed a peak at 11.5°, corresponding to a lattice spacing of 3nm, and SEM/TEM imaging demonstrated the formation of multi-layered graphene sheets. The synthesized GO was evaluated for the photocatalytic degradation of the organophosphate pesticide monocrotophos under visible light. At a concentration of 25mg/L, graphene exhibited a removal efficiency of 98% with a degradation rate of 0.036 ppm/min, following a Langmuir isotherm and pseudo-first-order kinetic model. The significance of this study lies in the potential environmental application, offering an economical and sustainable solution for the decontamination of pesticide-contaminated water sources. The method could contribute significantly for reducing environmental pollution and addressing global water safety challenges.

Novel Poly(N‐(1‐cyclohexylethyl)Acrylamide‐co‐Styrene‐co‐Ethyleneglycol Dimethacrylate) Synthesis for Efficient Methylene Blue Adsorption from Aqueous Solutions Synthesis

AbstractSynthesis and evaluation of a new polymeric adsorbent for adsorbing methylene blue (MB) from these economical aqueous solutions were investigated. In the first step of the study, N‐(1‐cyclohexylethyl)acrylamide (CEA) monomer was synthesized using S‐(+)‐1‐cyclohexylethylamine and acryloyl and the obtained CEA was analyzed by FTIR, 1H‐NMR, and 13C‐NMR. Then, a poly(CEA‐co‐Styrene‐co‐EGDMA) copolymer was obtained by cross‐linking CEA monomer, ethylene glycol dimethacrylate (EGDMA) and styrene monomers by subsequent radical polymerization episodes. FTIR, solid‐NMR, and elemental analysis methods were used for the characterization of this copolymer. In the last step of the study, the obtained poly(CEA‐co‐Styrene‐co‐EGDMA) copolymer was used as an adsorbent to adsorb MB from aqueous solutions. In the study, various factors such as adsorption properties, adsorbent dose, contact time, initial concentration, and solution pH were investigated. In the study, the most suitable adsorbent dose was determined as 0.1 g, initial solution concentration as 500 mg/L, contact time as 70 min, and pH as 7. Adsorption capacity was determined as 256.4 mg/g at 25 °C. As a result of the analysis of kinetic data, it was found that the model with the highest regression coefficient and the best fit with the experimental data was the pseudo‐second‐order kinetic model.

Introducing the mineral powder to strengthen polyurethane grouting materials for crack repair of asphalt pavements

Cracking in asphalt pavement may lead to structural distress while existing crack repair materials have significant shortcomings in terms of cost and construction efficiency. This study incorporates mineral powder into the polyurethane grouting materials to provide a more economical and durable solution for repairing cracks in asphalt pavement. The results of bonding and tensile tests show that excessive mineral powder may deteriorate the mechanical properties of polyurethane grouting materials, which is caused by the agglomeration of mineral powder observed by the microscale characterization. Characterization tests indicate that ultraviolet exposure results in the evolution of the chemical components and thermal stability of polyurethane grouting materials, subsequently causing the degeneration in the mechanical properties. The addition of mineral powders was found to increase the tensile strength of polyurethane grouting materials after Ultraviolet aging by 12 %, while the elongation at break measured in tensile tests remained essentially unchanged. The results of the splitting test, bending test, and immersion Marshall test prove the enhanced low-temperature property and moisture resistance of mineral powder-modified polyurethane grouting materials as compared to the styrene-butadiene-styrene emulsified asphalt. The economic analysis shows that this study presents a practical approach to cost-effective asphalt pavement crack repair, offering insights for enhancing the durability of asphalt pavements.

Gamma radiation induced optimized synthesis of amine functionalised poly-glycidyl methacrylate grafted poly-propylene non-woven fabric and its adsorption behaviour towards aqueous Pb(II)

Comprehensive optimization of the functionalization of polypropylene (PP) non-woven fabric through gamma irradiation-induced graft polymerization of glycidyl methacrylate (GMA), results in a material having high grafting yield. The technique, utilizing a mutual irradiation method, produced poly-GMA (PGMA) grafted PP fabric with a remarkable grafting yield of 125 % in a 1:1 acetone–water mixture, subjected to 25 kGy of total dose at a dose rate of 5 kGyh−1. The enhancement of the amination process utilizing PGMA-grafted PP was investigated with polyamines. Under the optimized conditions, the diethylene triamine (DETA) functionalized variant was identified as an effective adsorbent for aqueous Pb2+ ions. The optimized adsorbent exhibited a high saturation capacity of approximately 230 mgg−1 for Pb2+ ions, demonstrating rapid kinetics at near-neutral pH and 25 °C. This low cost innovative material holds significant promise for effective lead removal from drinking water and industrial wastewater, offering a sustainable economical solution for environmental remediation.

Investigating enhanced electrical conductivity for antenna applications through dual metallization on 3D printed SLA substrates

The advancement of 3D printing (additive manufacturing) has gained interest in variety of applications, especially for antenna fabrication. The demand for cheap, reliable, and high-performance antenna fabrication methods is essential in order to cope with the demand of wireless communications industry. In this work, the focus was emphasized on enhancing the electrical conductivity of the 3D printed substrates fabricated by stereolithography (SLA) 3D printer. The 3D printed substrate went through a dual metallization approach, involving sputtering and electrodeposition techniques for the fabrication of conductive metal layers. The parameters for the sputtering were fixed for all the substrate while current density during electrodeposition process was varied at 25 %, 50 %, 75 % and 100 % from recommended value of current. The results showed that the current density during the electrodeposition determined the conductivity performance of the metal layers and established a significant correlation with the surface morphological. Three different frequencies comprised of 2.4 GHz, 3.5 GHz and 5.0 GHz were selected to simulate and fabricate a microstrip patch antenna using the optimized current density which was valued optimal at 75 % (52.5 mA). The percentage of accuracy between simulated and measured frequencies of antenna, portrayed that the measured values were slightly higher than the simulated approximately about 5.14 to 6.67 %. Therefore, the integration of additive manufacturing or 3D printing techniques for antenna could address the critical necessity for fast and economical solution in wireless systems.

Techno-economic study for photovoltaic battery-free irrigation systems in an arid area for olive fields in Western West Al Minya, Egypt

Due to rain scarcity, artificial irrigation became an environmentally critical application for crop production. Proper irrigation is essential to maximize water use efficiency and plant biomass. Using clean energy sources is currently a trend that is sweeping the globe. To achieve this, we propose a solar-powered irrigation system. This study considers alternative irrigation systems using photovoltaic solar systems to pump water from deep wells for new land reclamation, whereas groundwater is the only source. The main objective is to evaluate various PV-powered pumping systems in Egypt's Western West Al Minya area. Two systems were nominated by considering the annual savings: the conventional irrigation Diesel system and a Photovoltaic (PV) battery-free irrigation system. The second system requires isolated pipes so solar radiation does not affect water temperature and avoids damage to plant roots. The 192 kW PV system was sized to operate a 60 KW water pumping system, with a required area of 1920 m2 for implementation. Direct irrigation using PV systems proved the best economical solution since it incurs the fewest costs of $0.015/m3 for 100–120 m well depth, compared with $0.073/m3 from the conventional system. As a result, the proposed PV-pumping system reduced the overall system cost by around 80% concerning the conventional system.

Key pavement condition indicators for assessing highway pavement maintenance quality index

The technical condition of road pavements requires regular monitoring to support accurate maintenance decisions. However, existing pavement maintenance quality index (PQI) assessment methods are often expensive and infrequent, which limits intelligent decision-making. This paper proposes a cost-effective and precise PQI assessment method using data from five highways in Beijing. Key PQI indicators were identified through a hybrid feature selection method, and a CatBoost model optimised by a genetic algorithm (GA-CatBoost) was developed. The model demonstrated superior accuracy, achieving an R2 of 0.938, an MSE of 0.838, and an MAE of 0.576. It achieved 98.46% accuracy compared to traditional methods, confirming its reliability in an on-site application. This approach offers an economical solution for high-frequency PQI assessment, enabling intelligent decision-making in road infrastructure maintenance and supporting the use of lightweight detection equipment.

Sustainable Water Management with Photocatalytic Janus Mesh: Efficient Fog Harvesting, Water Purification, and Microbial Disinfection.

Water scarcity is a critical global challenge, especially in arid and semiarid regions. Fog harvesting has emerged as a promising solution; however, concerns about air pollution and bacterial growth in humid environments have raised doubts about the safety and sustainability of such systems. This study introduces a Janus mesh with asymmetric wettability on its two faces, fabricated through a simple and scalable method. The unique design of the Janus mesh enables the transport of water droplets from the superhydrophobic side to the hydrophilic side in a unidirectional manner, enhancing its fog harvesting efficiency. The mesh's photocatalytic properties not only elevate the fog harvesting rate to 4.7 kg·m-2h-1 but also effectively purify the harvested water by removing organic contaminants (94%) and microbial impurities (99.98%). Additionally, its inherent bactericidal activity prevents biofouling, ensuring sustained efficiency in water collection. The mesh's self-cleaning abilities through photocatalysis maintain its surface integrity, promising long-term stability for fog harvesting applications. This technological advancement in fog harvesting offers a sustainable and economical solution to water scarcity concerns, addressing safety and sustainability issues associated with existing systems. By potentially transforming the livelihoods of communities struggling with water scarcity, this innovation paves the way for a more sustainable future.

MGScoliosis: Multi-grained scoliosis detection with joint ordinal regression from natural image

Scoliosis is among the most prevalent diseases affecting teenagers. However, traditional scoliosis screening methods often resort to physical examination or radiographic imaging. The two ways both rely on experts with high costs, which are not suitable for wide-range screening. Besides, estimating Cobb angle level only using natural images are challenging. To tackle these issues, we propose a multi-grained scoliosis detection framework by jointly estimating severity level and Cobb angle level of scoliosis from a natural image instead of a radiographic image, which has not been explored before. Specifically, we regard scoliosis estimation as an ordinal regression problem, and transform it into a series of binary classification sub-problems. Besides, we adopt the visual attention network with large kernel attention as the backbone for feature learning, which can model local and global correlations with efficient computations. The feature learning and the ordinal regression is put into an end-to-end framework, in which the two tasks of scoliosis severity level estimation and scoliosis angle level estimation are jointly learned and can contribute to each other. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economical solution to wide-range scoliosis screening. Particularly, our approach achieves accuracies of 94.90% and 79.62% in estimating severity level and Cobb angle level, improving large margins of 4.90% and 12.15% over existing natural image based scoliosis detection performance, respectively. The code is available at https://github.com/RuiChen-stack/MGScoliosis.

IOT-Based Smart Door Selector For Double Security: Integration of Rfid and Blynk App for Economical Solution

This research aims to design and develop an Internet of Things (IoT)-based smart door lock system by utilizing Radio Frequency Identification (RFID) technology and the Blynk application. The system is designed to improve the level of home security by providing a variety of flexible access methods, including the use of RFID cards, smartphone apps, and emergency buttons. The prototype method is applied in system development, which involves creating a physical model to evaluate its performance and functionality. The test results showed that the system was operating properly, where RFID only responded to the registered card to unlock, thus increasing access security. Hardware design that is carried out with an economical approach and simple use of components makes this system more affordable compared to commercial smart door lock products on the market. This research shows great potential in increasing efficiency and comfort in managing home security systems and offers ease of implementation in various types of housing. Thus, this research makes a significant contribution to the development of innovative and practical solutions to improve home safety at a lower cost while providing a sense of security for its occupants.

Groundwater Contamination by Heavy Metals in Malaysia: Sources, Transport, and Remediation Strategies

Groundwater contamination by heavy metals is a pressing environmental concern, particularly in regions highly dependent on groundwater as a freshwater source. While Malaysia primarily relies on river water, certain states and islands depend on groundwater for their supply. Research on heavy metal contamination in Malaysia’s groundwater remains limited, making it crucial to study the distribution and mobility of contaminants to develop appropriate remediation strategies. In addition to natural sources, anthropogenic activities such as landfills, mining, and the use of fertilizers contribute significantly to heavy metal pollution in groundwater. Factors like rainfall, fluctuating groundwater levels, and low soil pH can exacerbate heavy metal leaching into aquifers. Various models and techniques, including 2D resistivity imaging and MODFLOW, are used to assess groundwater flow and contaminant transport. These models suggest that contaminant concentrations decrease with increased depth and radial distance from pollution sources such as landfills and mining areas. The health risks associated with heavy metal exposure through groundwater consumption are significant, necessitating effective remediation strategies. Phytoremediation is an economical solution for groundwater containing low concentrations of heavy metals, while permeable reactive barriers may be suitable for more complex cases, pending detailed site investigation. This review aims to examine the current state of knowledge on heavy metal contamination in Malaysia’s groundwater, focusing on sources, distribution patterns, and movement of pollutants. It also seeks to evaluate existing remediation methods, including phytoremediation and permeable reactive barriers, while identifying gaps in research, particularly concerning risk assessments and heavy metal speciation.

Improved chickpea growth, physiology, nutrient assimilation and rhizoremediation of hydrocarbons by bacterial consortia.

Soil pollution by petroleum hydrocarbons (PHCs) reduces yield by changing the physico-chemical properties of soil and plants due to PHCs' biotoxicity and persistence. Thus, removing PHCs from the soil is crucial for ecological sustainability. Microbes-assisted phytoremediation is an economical and eco-friendly solution. The current work aimed to develop and use bacterial consortia (BC) for PHCs degradation and plant growth enhancement in hydrocarbon-contaminated soil. Initially, the enriched microbial cultures (that were prepared from PHCs-contaminated soils from five distinct regions) were obtained via screening through microcosm experiments. Afterward, two best microbial cultures were tested for PHCs degradation under various temperature and pH ranges. After culture optimization, isolation and characterization of bacterial strains were done to construct two BC. These constructed BC were tested in a pot experiment for hydrocarbons degradation and chickpea growth in PHCs contaminated soil. Findings revealed that PHCs exerted significant phytotoxic effects on chickpea growth and physiology when cultivated in PHCs contaminated soil, reducing agronomic and physiological traits by 13-29% and 12-43%, respectively. However, in the presence of BC, the phytotoxic impacts of PHCs on chickpea plants were reduced, resulting in up to 24 - 35% improvement in agronomic and physiological characteristics as compared to un-inoculated contaminated controls. Furthermore, the bacterial consortia boosted chickpea's nutritional absorption and antioxidant mechanism. Most importantly, chickpea plants phytoremediated 52% of the initial PHCs concentration; however, adding BC1 and BC2 with chickpea plants further increased this removal and remediated 74% and 80% of the initial PHCs concentration, respectively. In general, BC2 outperformed BC1 (with few exceptions) in promoting plant growth and PHCs elimination. Therefore, using multi-trait BC for PHCs degradation and plant growth improvement under PHCs stress may be an efficient and environmentally friendly strategy to deal with PHCs pollution and toxicity.

A Realistic Ingeniously Designed Cleft Palate Simulator for Training of Residents: Bridging the Gap Between Theory and Practice.

Cleft palate is a congenital deformity that presents significant challenges in surgical correction. Proper training with an educational model for can greatly enhance the learning curve and improve patient outcomes. This article discusses the development and evaluation of an economical cleft palate model designed to enhance surgical skills and confidence among residents. The model was constructed using dental casts, acrylic resin, foam sheets, double-sided adhesives, infant feeding tubes, and red dye. Tertiary care center. Surgical residents. The assembly time and material costs were recorded. To evaluate its effectiveness, participants' feedbacks were collected and analyzed. The cleft palate model was produced at a cost of 250 INR per unit, with assembly taking approximately 15 minutes. Overall, 32 residents completed the simulation exercise of cleft surgery on the model. Participants gave the model an average score of 4 (±0.60) for realistic anatomical appearance, 3.5 (±1.3) for replicating the tactile nature of human tissue. Anatomical accuracy and ability to manipulate tissue and perform suturing received a rating of 3.4 (±1.1) and 3.7 (±0.81), respectively. The model's value as a training tool scored 4.4 (±0.51), with an overall satisfaction rating of 4.6 (±0.22) among residents. Our cleft palate model offers an economical, practical, and accessible solution for surgical training. This model is a viable alternative to more complex and expensive training tools and potentially enhancing the training of healthcare professionals in cleft palate repair.

Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods

Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.

Modeling and economic analysis of block absorber (RadFrac) performance with stage variations for post-combustion CO2 capture

This study presents a comprehensive modeling and economic evaluation of a Monoethanolamine (MEA)-based post-combustion carbon capture (PCC) absorber unit. The simulation provides detailed insights into the absorber's internal profiles, including temperature gradients and the percentage of CO2 captured, with a particular focus on the effects of varying the number of stages and absorber diameters. Validation against experimental data demonstrated close alignment between simulated and observed values, with an average Root Mean Square Deviation (RMSD) of 0.018 and 4.0129, respectively, confirming the reliability of the simulation in capturing the complex dynamics of the absorber.The economic analysis, conducted using the Aspen Process Economic Analyzer (APEA), simplified the complex relationship between absorber segmentation, capital and operational costs, and absorber diameter. The study revealed that increasing the absorber diameter and the number of stages leads to a significant rise in Total Capital Cost (TCC), Total Operating Cost (TOC), Equipment Cost, and Total Installed Cost (TIC), with the highest costs observed at a stage number of 90 and a diameter of 1.0 m. However, the analysis identified an optimal configuration at an absorber diameter of 0.45 m with either 10 or 20 stages. This setup effectively balances cost and CO2 capture efficiency, offering a more economical solution compared to configurations with larger diameters and higher stage numbers, which substantially increase expenses. The findings highlight the critical trade-offs between operational efficiency and capital expenditure, stressing on the crucial role of absorber diameter in determining the economic feasibility of the absorber block in PCC systems.

Transcending disciplinary boundaries: Nature based solutions (NbSs) for Caribbean coastal resilience

Caribbean Small Island Developing States are experiencing multiple interconnected crises including susceptibility to natural hazards, increased vulnerability to climate change, biodiversity loss and reduced accessibility to development funding. Nature-based Solutions can provide an integrated approach to boost Caribbean coastal resilience as they are economic and practical solutions that capably balance competing interests. This study used a participatory method, Group Model Building, to engage stakeholders across varying disciplines and levels of decision-making to collaboratively identify the underlying causes of low Nature-based Solutions uptake regionally and potential intervention mechanisms. Six overarching factors contributed to low adoption, including data and knowledge limitations, a propensity towards hard engineering, low collaborative engagement, and limited implementation capacity. Some areas identified for intervention were comprehensive data collection, reducing uncertainty surrounding the implementation of Nature-based Solutions, effective collaboration, using case studies and best practices, and knowledge translation and dissemination. The outputs demonstrated a critical role for the engineer in mainstreaming Nature-based Solutions through adopting a systematic and holistic design approach. Concomitant to this is the need for engineers to operate successfully within transdisciplinary teams to enhance transformative communication skills and ensure that solutions not only minimize environmental impact but also support the conservation of biodiversity and ecosystem services.

Fabrication and Fluorescence Analysis of Rhodamine Dyes in Polycarbonate Serpentine Microfluidic System.

The synthesis of rhodamine dyes (R6G and R1010) and their fluorescence characterization within polymer-based microfluidics, offers an exciting and novel approach in materials science and chemical analysis. This work investigates the emission of polycarbonate substrates (PC) by UV-visible. The ablation threshold (16mj.sec-1) of PC at 193nm wavelength after that ablation process continued to produce microfluidic serpentine channels on PC by using G-Code. The fluorescence characteristics of Rhodamine 6G and Rhodamine 101 are investigated. Absorption and emission at peak wavelength were analyzed against R6G and R101 concentrations. Furthermore, the refractive indices of both R6G and R101 vis concentrations are examined. As a result at low concentrations, there was the highest overlapping, and at high concentrations, there was the smallest overlapping. R101 showed better photostability and a more consistent diffusion, whereas R6G had a faster diffusion and stronger fluorescence intensity. These differences were caused by the different molecular structures of the dyes and their interactions with the PCmicrochannel. Incorporating R6G and R101 dyes into a polycarbonate PC microfluidic chip would enhances both the resolution and sensitivity of fluorescence detection. The limited microfluidic setup facilitates ultra-high-resolution investigation and minimizing sample volumes, making it suitable for applications requiring precise measurements. The innovation relies on the utilization of the unique fluorescence characteristics of R6G (Rhodamine 6G) and R101 (Rhodamine 101) dyes to enhance the performance of polycarbonate microfluidic devices. R6G has high fluorescence quantum yield and stability, rendering it suitable for sensitive detection, while R101 offers superior brightness and improved resistance to photobleaching. Incorporation of these dyes into polymeric microfluidics improves sensitivity and facilitates real-time, dynamic sample analysis. This method offers a portable, economical solution with high-throughput capabilities, greatly enhancing both analytical and process accuracy across a variety of applications.