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This study aims to provide a microfluidic blood glucose sensing platform based on integrated interdigitated electrode arrays (IDEAs) on a flexible quartz glass substrate, adhering closely to pertinent electrochemical characterizations.

Sensors are the key elements of the modern electronics era through which all the possible physical quantities can be detected and converted into their equivalent electrical form and processed further. But to make the sensing environment better, various types of innovative architectures are being developed nowadays and among them interdigitated electrodes are quite remarkable in terms of their sensing capability. They are a well-qualified candidate in the field of gas sensing and biosensing, but even their sensitivities are getting saturated due to their physical dimensions. Most of the thin film IDEAs fabricated by conventional optical lithographic techniques do not possess a high surface-to-volume ratio to detect the target specified and that reduces their sensitivity factor. In this context, a classic conductive carbon-based highly sensitive three dimensional (3D) IDEA-enabled biosensing system has been conceived on a transparent and flexible substrate to measure the amount of glucose concentration present in human blood. 3D IDEA possesses a way better capacitive sensing behavior compared to conventional thin film microcapacitive electrodes. To transmit the target biological analyte sample property for the detection purpose to the interdigitated array-based sensing platform, the design of a microfluidic channel is initiated on the same substrate. The complex 3D Inter Digital array structure improves the overall capacitance of the entire sensing platform and the reactive surface area as well. The manufactured integrated device displays a decent value of sensitivity in the order of 5.6 µA mM⁻¹ cm⁻².

Development of a low-cost array-based integrated and highly flexible microfluidic biochip to extract the quantity of glucose present in human blood.

Potential future research opportunities in the realm of integrated miniaturized, low-cost smart biosensing systems may arise from this study.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

The purpose of this paper is to study the two-dimensional micropolar fluid flow with conjugate heat transfer and mass transpiration. The considered nanofluid has graphene nanoparticles.

Governing nonlinear partial differential equations are converted to nonlinear ordinary differential equations by similarity transformation. Then, to analyze the flow, the authors derive the dual solutions to the flow problem. Biot number and radiation effect are included in the energy equation. The momentum equation was solved by using boundary conditions, and the temperature equation solved by using hypergeometric series solutions. Nusselt numbers and skin friction coefficients are calculated as functions of the Reynolds number. Further, the problem is governed by other parameters, namely, the magnetic parameter, radiation parameter, Prandtl number and mass transpiration. Graphene nanofluids have shown promising thermal conductivity enhancements due to the high thermal conductivity of graphene and have a wide range of applications affecting the thermal boundary layer and serve as coolants and thermal management systems in electronics or as heat transfer fluids in various industrial processes.

Results show that increasing the magnetic field decreases the momentum and increases thermal radiation. The heat source/sink parameter increases the thermal boundary layer. Increasing the volume fraction decreases the velocity profile and increases the temperature. Increasing the Eringen parameter increases the momentum of the fluid flow. Applications are found in the extrusion of polymer sheets, films and sheets, the manufacturing of plastic wires, the fabrication of fibers and the growth of crystals, among others. Heat sources/sinks are commonly used in electronic devices to transfer the heat generated by high-power semiconductor devices such as power transistors and optoelectronics such as lasers and light-emitting diodes to a fluid medium, thermal radiation on the fluid flow used in spectroscopy to study the properties of materials and also used in thermal imaging to capture and display the infrared radiation emitted by objects.

Micropolar fluid flow across stretching/shrinking surfaces is examined. Biot number and radiation effects are included in the energy equation. An increase in the volume fraction decreases the momentum boundary layer thickness. Nusselt numbers and skin friction coefficients are presented versus Reynolds numbers. A dual solution is obtained for a shrinking surface.

This study investigates whether logistics and supply chain resilience strategies (SCREST) can help mitigate the negative impacts of disruptions on firm performance and logistics and supply chain (SC) activities of companies, using the COVID-19 pandemic as a case study.

The authors collected primary data on the implementation of different types of SCRESTs and measured the impact of COVID-19 in terms of firm performance and logistics and SC metrics through a survey of Japanese manufacturing companies in four sectors. The authors used these data to illustrate whether the companies benefitted from SCRESTs in mitigating the negative impacts of COVID-19. A questionnaire comprising structured and open-ended questions was sent to 8,000 companies all over Japan that met the selection criteria, using a combination of mail and web-based media. The respondents were logistics and SC professionals. A combination of qualitative and quantitative analysis was performed for data analysis and interpretation.

Research conducted within the case of the Japanese context revealed that findings varied depending on the methodology applied. The use of a direct analysis approach and qualitative analysis suggested that the implementation of SCRESTs is beneficial in addressing the negative impacts of COVID-19 on firm performance and logistics and SC activities, whereas the application of indirect analysis approach yielded mixed results. The analysis also indicated a shift in the preferred SCRESTs during COVID-19.

To the best of the authors’ knowledge, this is the first study to examine the benefits of implementing SCRESTs using primary data from the manufacturing sector of Japan. Furthermore, empirical research on this topic is generally lacking.

The ripple effect (i.e. disruption propagation in networks) belongs to one of the central pillars in supply chain resilience and viability research, constituting a type of systemic disruption. A considerable body of knowledge has been developed for the last two decades to examine the ripple effect triggered by instantaneous disruptions, e.g. earthquakes or factory fires. In contrast, far less research has been devoted to study the ripple effect under long-term disruptions, such as in the wake of the COVID-19 pandemic.

This study qualitatively analyses secondary data on the ripple effects incurred in automotive and electronics supply chains. Through the analysis of five distinct case studies illustrating operational practices used by companies to cope with the ripple effect, we uncover a disruption propagation mechanism through the supply chains during the semiconductor shortage in 2020–2022.

Applying a theory elaboration approach, we sequence the triggers for the ripple effects induced by the semiconductor shortage. Second, the measures to mitigate the ripple effect employed by automotive and electronics companies are delineated with a cost-effectiveness analysis. Finally, the results are summarised and generalised into a causal loop diagram providing a more complete conceptualisation of long-term disruption propagation.

The results add to the academic discourse on appropriate mitigation strategies. They can help build scenarios for simulation and analytical models to inform decision-making as well as incorporate systemic risks from ripple effects into a normal operations mode. In addition, the findings provide practical recommendations for implementing short- and long-term measures during long-term disruptions.

The study aims to develop an inexpensive metal oxide semiconductor gas sensor with high sensitivity, excellent selectivity for a specific gas and rapid response time.

This study synthesized Zn2SnO4 nanostructures using a hydrothermal method with a 1 M concentration of zinc chloride (ZnCl2) as the zinc source and a 0.7 M concentration of tin chloride (SnCl4) as the tin source. Thick films of nanostructured Zn2SnO4 were then produced using screen printing. The structural properties of Zn2SnO4 were confirmed using X-ray diffraction, and the formation of Zn2SnO4 nanoparticles was verified by transmission electron microscopy. Scanning electron microscopy was used to analyse the surface morphology of the fabricated material, while energy dispersive spectroscopy provided insight into the chemical composition of the thick film. These fabricated thick films underwent testing for various hazardous gases, including nitrogen dioxide, ammonia, hydrogen sulphide (H2S), ethanol and methanol.

The nanostructured Zn2SnO4 thick film sensor demonstrates a notable sensitivity to H2S gas at a concentration of 500 ppm when operated at 160°C. Its selectivity, response time and recovery time were assessed and documented.

The primary limitations of this research on metal oxide semiconductor gas sensors include poor selectivity to specific gases, limited durability and challenges in achieving detection at room temperature.

The nanostructured Zn2SnO4 thick film sensor demonstrates a strong response to H2S gas, making it a promising candidate for commercial production. The detection of H2S is crucial in various sectors, including industries and sewage plants, where monitoring this gas is essential.

Currently, heightened global apprehension about atmospheric pollution stems from the existence of perilous toxic and flammable gases. This underscores the imperative need for monitoring such gases. Toxic and flammable gases are frequently encountered in both residential and industrial environments, posing substantial hazards to human health. Noteworthy accidents involving flammable gases have occurred in recent years. It is crucial to comprehend the presence and composition of these gases in the surroundings for precise detection, measurement and control. Thus, there has been a significant push for extensive research and development in diverse sensor technologies using various materials and methodologies to monitor and regulate these gases effectively.

In this research, Zn2SnO4 nanostructures were synthesized using a hydrothermal method with ZnCl2 at a concentration of 1 M for zinc and SnCl4 at a concentration of 0.7 M for tin. Thick films of nanostructured Zn2SnO4 were then fabricated via screen printing technique. Following fabrication, all thick films were subjected to testing with various toxic gases, and the results were compared to previously published data. The analysis indicated that the nanostructured Zn2SnO4 thick film sensor demonstrated outstanding performance concerning gas response, gas concentration, selectivity and response time, particularly towards H2S gas.

Understanding the mechanical and thermal behavior of materials is the goal of the branch of study known as fractional thermoelasticity, which blends fractional calculus with thermoelasticity. It accounts for the fact that heat transfer and deformation are non-local processes that depend on long-term memory. The sphere is free of external stresses and rotates around one of its radial axes at a constant rate. The coupled system equations are solved using the Laplace transform. The outcomes showed that the viscoelastic deformation and thermal stresses increased with the value of the fractional order coefficients.

The results obtained are considered good because they indicate that the approach or model under examination shows robust performance and produces accurate or reliable results that are consistent with the corresponding literature.

This study introduces a proposed viscoelastic photoelastic heat transfer model based on the Moore-Gibson-Thompson framework, accompanied by the incorporation of a new fractional derivative operator. In deriving this model, the recently proposed Caputo proportional fractional derivative was considered. This work also sheds light on how thermoelastic materials transfer light energy and how plasmas interact with viscoelasticity. The derived model was used to consider the behavior of a solid semiconductor sphere immersed in a magnetic field and subjected to a sudden change in temperature.

This study introduces a proposed viscoelastic photoelastic heat transfer model based on the Moore-Gibson-Thompson framework, accompanied by the incorporation of a new fractional derivative operator. In deriving this model, the recently proposed Caputo proportional fractional derivative was considered. This work also sheds light on how thermoelastic materials transfer light energy and how plasmas interact with viscoelasticity. The derived model was used to consider the behavior of a solid semiconductor sphere immersed in a magnetic field and subjected to a sudden change in temperature.

Field-effect transistor (FET) has excellent electronic properties and inherent signal amplification, and with the development of nanomaterials technology, FET biosensors with nanomaterials as channels play an important role in the field of heavy metal ion detection. This paper aims to review the research progress of silicon nanowire, graphene and carbon nanotube field-effect tube biosensors for heavy metal ion detection, so as to provide technical support and practical experience for the application and promotion of FET.

The article introduces the structure and principle of three kinds of FET with three kinds of nanomaterials, namely, silicon nanowires, graphene and carbon nanotubes, as the channels, and lists examples of the detection of common heavy metal ions by the three kinds of FET sensors in recent years. The article focuses on the advantages and disadvantages of the three sensors, puts forward measures to improve the performance of the FET and looks forward to its future development direction.

Compared with conventional instrumental analytical methods, FETs prepared using nanomaterials as channels have the advantages of fast response speed, high sensitivity and good selectivity, among which the diversified processing methods of graphene, the multi-heavy metal ions detection of silicon nanowires and the very low detection limit and wider detection range of carbon nanotubes have made them one of the most promising detection tools in the field of heavy metal ions detection. Of course, through in-depth analysis, this type of sensor has certain limitations, such as high cost and strict process requirements, which are yet to be solved.

This paper elaborates on the detection principle and classification of field-effect tube, investigates and researches the application status of three kinds of FET biosensors in the detection of common heavy metal ions. By comparing the advantages and disadvantages of each of the three sensors in practical applications, the paper focuses on the feasibility of improvement measures, looks forward to the development trend in the field of heavy metal detection and ultimately promotes the application of field-effect tube development technology to continue to progress, so that its performance continues to improve and the application field is constantly expanding.

The purpose of this study is to examine how the implementation of edge computing can enhance the progress of the circular economy within supply chains and to address the challenges and best practices associated with this emerging technology.

This study utilized a streamlined evaluation technique that employed Latent Dirichlet Allocation modeling for thorough content analysis. Extensive searches were conducted among prominent publishers, including IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi, utilizing pertinent keywords associated with edge computing, circular economy, sustainability and supply chain. The search process yielded a total of 103 articles, with the keywords being searched specifically within the titles or abstracts of these articles.

There has been a notable rise in the volume of scholarly articles dedicated to edge computing in the circular economy and supply chain management. After conducting a thorough examination of the published papers, three main research themes were identified, focused on technology, optimization and circular economy and sustainability. Edge computing adoption in supply chains results in a more responsive, efficient and agile supply chain, leading to enhanced decision-making capabilities and improved customer satisfaction. However, the adoption also poses challenges, such as data integration, security concerns, device management, connectivity and cost.

This paper offers valuable insights into the research trends of edge computing in the circular economy and supply chains, highlighting its significant role in optimizing supply chain operations and advancing the circular economy by processing and analyzing real time data generated by the internet of Things, sensors and other state-of-the-art tools and devices.

This paper aims to address the challenge raised in the literature regarding whether and how digitalization supports a servitized new product development (NPD) process, considering the customer’s involvement from the early stage of the process.

Pragmatic constructivism (PC) has been adopted for conceptualizing the NPD process as the construction of a new reality. PC is the method theory used for interpreting the field evidence drawn from a qualitative case study carried out at a multinational company operating in the semiconductor industry.

This study shows how digitalization supports the alignment to the overarching topoi of the company servitization strategy by enabling the integration and merging of different organizational topoi during the NPD process.

This study is confined to a single-case study and context.

The results of this study are relevant for managers involved in the stage-gate product development of manufacturing companies, informing them on how the use of digital tools enables or hinders the progression of product development projects.

This paper contributes to the servitization literature by offering field evidence that demonstrates the importance for manufacturing firms of acquiring customer feedback from an early NPD phase. Another contribution is related to the literature on the role of digitalization in NPD processes, describing how digital tools give support during the different phases of the NPD process.