Mithun Kanchan

Hydrogen peroxide (H₂O₂) is a reactive oxygen species that is often used as a model analyte in sensing studies. This study presents a proof-of-concept-- microwell colorimetric platform fabricated by sequential stacking of adhesive polyvinyl chloride (PVC) films on glass substrates, combined with smartphone imaging and convolutional neural network (CNN) analysis. A PVA–KI matrix produced a consistent colorimetric response across hydrogen peroxide concentrations of 0.0008 M, 0.008 M, 0.08 M, 0.8 M, and 8.8 M, which was qualitatively reproduced using MATLAB R2025b simulations. A total of 14 independent microwell chips were fabricated, each tested once across five peroxide concentrations, generating a dataset of 4,400 images under varied lighting conditions and smartphone models for CNN training. The custom classifier (‘DoseVision’) achieved 99.86% accuracy (R² = 0.9963, Cohen’s κ = 0.9983), demonstrating reliable dose classification without the need for calibration curves. This study establishes the feasibility of a minimally instrument-dependent- approach for hydrogen peroxide detection, providing a methodological foundation for future translation into biomedical and environmental sensing applications.

The simultaneous increase in harmful Sargassum blooms and the high carbon footprint of the polymer industry necessitate sustainable material solutions. This study presents a circular economy approach for valorizing Sargassum biomass into a multifunctional inorganic ash filler for recycled polypropylene (rPP) composites. Sargassum biomass was calcined to produce mineral-rich ash and surface-modified using a silane coupling agent to improve compatibility with the rPP matrix. Composites containing untreated and silane-treated ash at different filler loadings were fabricated and evaluated for mechanical, thermal, flame-retardant, and environmental performance. The composite containing 10 wt% silane-treated ash exhibited optimal mechanical properties, achieving a 21.4% increase in tensile strength and a 22.2% increase in flexural strength compared to neat rPP. Heat Deflection Temperature increased from 95 to 110 °C, while the Limiting Oxygen Index increased from 17.5 to 23.5% at 15 wt% ash loading, indicating improved flame retardancy. Life Cycle Assessment showed that Sargassum-derived ash exhibited an approximately 88% lower estimated Global Warming Potential than commercial talc filler. These findings demonstrate the potential of Sargassum ash as a sustainable multifunctional filler supporting circular economy and environmental sustainability goals.

Maintaining optimal health and preventing diabetes-related complications require accurate and timely monitoring of blood glucose levels. In this context, the present study develops an affordable, reliable, and precise Point-of-Care (POC) diagnostic platform for glucose detection by integrating microfluidic and colorimetric principles. The system uses a custom-fabricated microfluidic chip that enables efficient enzymatic color reactions with only ~20 µL of sample per microwell, achieving complete color development within 3–4 minutes. The chip is enclosed in a compact, USB-powered, 3D-printed imaging module equipped with a high-resolution fixed-focus camera to ensure stable control of focal distance, alignment, and illumination. The workflow is designed for seamless compatibility with embedded systems or laptops, eliminating dependence on smartphones or external calibration tools and supporting real-time POC deployment. A dataset of 1280 images representing 16 glucose concentrations from 50 to 200 mg/dL was captured under standardized conditions, labelled, and uniformly preprocessed. Engineered image features extracted from the processed images were evaluated using supervised machine learning models, including Random Forest, Support Vector Machine, K-Nearest Neighbours, and a Feedforward Neural Network, to create a robust predictive framework for rapid and consistent glucose estimation. Among these, the Random Forest model achieved the highest cross-validation precision of 98% and specificity approaching 100%, effectively distinguishing glucose levels. Confusion matrix and ROC analyses further confirmed reliability, showing minimal misclassification and a mean AUC near 1. Overall, the proposed image-based glucose estimation approach demonstrates a cost-effective, scalable, and accurate solution for real-time monitoring in diverse healthcare settings. The system also offers low reagent consumption, rapid analysis time, and straightforward operation, making it suitable for decentralized screening, routine monitoring, and resource-limited clinical environments where conventional laboratory testing may be inaccessible or delayed. Future work will focus on expanding concentration ranges, validating performance with clinical samples, and integrating automated calibration to support large-scale and long-term usability.

Engineering education must evolve to prepare graduates as ethical, socially responsible, and sustainable designers responding to climate, resource, urbanization, and equity challenges, not just technical specifications. This paper examines sustainability in engineering education through the integrated lenses of transformative learning theory (TLT), triple bottom line (TBL), and education for sustainable development (ESD) to link pedagogy with measurable learning and impact. The research question guiding this study is stated once and consistently: In what ways do distinctive pedagogical approaches and curricular changes within engineering education facilitate transformative learning processes that develop students’ sustainability competencies?. Using a mixed‑methods design, the study analyzes fifteen qualitative case studies (curricular, co‑curricular, outreach, industry/research) for evidence of disorienting dilemmas, critical reflection, and perspective transformation, and synthesizes quantitative indicators (participation, tangible outputs, composite Sustainability Impact Score) across five representative cases. Findings show that experiential, interdisciplinary, and community‑engaged formats catalyze holistic TBL reasoning, ethics and stewardship, systems and critical thinking, and theory‑to‑practice application; technically intensive projects yield clearer measurable outcomes, while community initiatives benefit from stronger pre/post assessment. The integrated TLT–TBL–ESD model and the Sustainability Impact Score offer practical guidance for course design, faculty development, and institutional change aligned to WFEO 2030 competencies, supporting scalable, evidence‑based transformation in engineering education.

This article discusses contemporary strategies to deal with immersed boundary (IB) frameworks useful for analyzing flow–structure interaction in complex settings. It focuses on immense advancements in various fields: biology, oscillation of structures due to fluid flow, deformable materials, thermal processes, settling particles, multiphase systems, and sound propagation. The discussion also involves a review of techniques addressing moving boundary conditions at complex interfaces. Evaluating practical examples and theoretical challenges that have been addressed by these frameworks are another focus of the article. Important results highlight the integration of IB methods with adaptive mesh refinement and high-order accuracy techniques, which enormously improve computational efficiency and precision in modeling complex solid–fluid interactions. The article also describes the evolution of IB methodologies in tackling problems of energy harvesting, bio-inspiration propulsion, and thermal-fluid coupling, which extends IB methodologies broadly in many scientific and industrial areas. More importantly, by bringing together different insights and paradigms from across disciplines, the study highlights the emerging trends in IB methodologies towards solving some of the most intricate challenges within the technical and scientific domains.

The “immersed boundary method (IBM)” is considered to be the most efficacious and versatile technique to solve flow problems associated with intricate geometries. The first part of this review examines recent advancements in IBM, essential for the simulation of “fluid–structure interactions (FSIs)” in sophisticated systems. This review highlights significant developments in turbulence modeling, adaptive mesh refinement, and complex geometric simulations, demonstrating IB methods’ capacity to seamlessly integrate arbitrary geometries into structured computational grids while preserving computational efficiency. Various IB techniques are analyzed for enforcing boundary conditions on dynamic immersed boundaries, with notable breakthroughs in managing velocity discontinuities, spurious oscillations, and large-scale deformations. Recent findings illustrate the versatility of IB methods, with applications encompassing biological fluid dynamics, turbulent multiphase flows, and cavitating flows. These innovations not only enhance computational performance but also address evolving challenges across engineering and scientific fields, establishing IB methods as a robust tool for resolving complex, multidisciplinary problems with high accuracy and efficiency.

Purpose. The purpose of this study was to utilize Machine Learning (ML) to optimize the hardness of aluminum-silicon (Al-Si) alloy composites reinforced with Coconut Shell Ash (CSA). Specifically, the study focused on leveraging ML techniques to accurately predict material properties and streamline optimization. Also, the study intended to optimize the composition and process parameters simultaneously through the ML approach. Methodology. The study used Minitab’s Automated Machine Learning (AutoML), specifically the TreeNet model, to develop a predictive model for hardness. The input parameters included Al-Si alloy content, CSA content, melting temperature, and stirring speed. The model was trained and validated using experimental data, and hyperparameter tuning was performed to improve accuracy. The optimal settings were then experimentally verified to assess the model’s reliability. Findings. The optimal composition for maximizing hardness was 90 wt% Al-Si alloy and 10 wt% CSA, with a melting temperature of 800 °C and a stirring speed of 800 rpm. The experimental results validated the model’s predictions, with a hardness value of 70.9 BHN. The study demonstrated that CSA can be an effective, eco-friendly reinforcement for Al-Si composites, enhancing mechanical properties and promoting sustainability. Practical Implications. The findings have significant implications for industries such as automotive, aerospace, and defense, where lightweight, high-strength materials are critical. The ML-based approach used in this study can reduce the need for extensive experimental testing, offering a practical and efficient method for optimizing composite materials. Using CSA as a reinforcement also contributes to sustainable manufacturing by utilizing agricultural waste. Originality. This study presents an innovative approach by integrating ML into optimizing metal matrix composites, specifically using an eco-friendly reinforcement material. The article offers a novel contribution to materials science and sustainable engineering through a novel and structured step-by-step AutoML approach.

In this study, a hybrid piezo-flexural-hydraulic actuator is modeled and simulated using bond graph methodology. The hybrid actuator comprises piezoelectric stack actuator, mechanical flexural amplifier, and hydraulic piston actuator. The piezoelectric stack actuator produces electrically controllable displacement. This displacement is amplified by a cascading combination of flexural amplifier and hydraulic actuator. A domain-independent bond graph model for the proposed hybrid actuator is developed. Using this bond graph, a mathematical model and a state space representation for the hybrid actuator are derived. The bond graph model is simulated using a 20-sim bond graph simulation software. The results of the simulation provide displacement characteristics and sensitivity analysis for each component and the hybrid actuator as a whole. The study plays a significant role in understanding the dynamic behavior of a multi-domain system using the bond graph methodology.

Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects.