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The inclusion of conductive carbon materials into lithium-ion batteries (LIBs) is essential for constructing an electrical network of electrodes. Considering the demand for cells in electric vehicles (e.g., higher energy density and lower cell cost), the replacement of the currently used carbon black with carbon nanotubes (CNTs) seems inevitable. This review discusses how CNTs can contribute to the development of advanced LIBs for EVs. First, the reason for choosing CNTs as a conducting agent for the cathode is discussed in terms of energy density. Second, the reinforcing effect of CNTs on the anode is described with respect to the choice of silicon as the active material. Third, the development of water-based cathode fabrication as well as dry electrode fabrication with aid of CNTs is discussed. Fourth, three technical hurdles, that is, the price, dispersion issue, and entrapped metal impurities, for widespread use of CNTs in LIBs are discussed. Graphical abstract

Microglia are resident immune cells of the central nervous system that act as brain-specific macrophages and are also known to regulate the innate immune functions of astrocytes through secretory molecules. This communication plays an important role in brain functions and homeostasis as well as in neuropathologic disease. In this study, we aimed to elucidate whether astrocytes and microglia could crosstalk to induce microglial polarization and proliferation, which can be further regulated under a microenvironment mimicking that of brain stroke. Microglia in a mixed glial culture showed increased survival and proliferation and were altered to M2 microglia; CD11b−GFAP+ astrocytes resulted in an approximately tenfold increase in microglial cell proliferation after the reconstitution of astrocytes. Furthermore, GM-CSF stimulated microglial proliferation approximately tenfold and induced them to become CCR7+ M1 microglia, which have a phenotype that could be suppressed by anti-inflammatory cytokines such as IL-4, IL-10, and substance P. In addition, the astrocytes in the microglial co-culture showed an A2 phenotype; they could be activated to A1 astrocytes by TNF-α and IFN-γ under the stroke-mimicking condition. Altogether, astrocytes in the mixed glial culture stimulated the proliferation of the microglia and M2 polarization, possibly through the acquisition of the A2 phenotype; both could be converted to M1 microglia and A1 astrocytes under the inflammatory stroke-mimicking environment. This study demonstrated that microglia and astrocytes could be polarized to M2 microglia and A2 astrocytes, respectively, through crosstalk in vitro and provides a system with which to explore how microglia and astrocytes may behave in the inflammatory disease milieu after in vivo transplantation.

Tears have emerged as a promising alternative to blood for diagnosing diabetes. Despite increasing attempts to measure tear glucose using smart contact lenses, the controversy surrounding the correlation between tear glucose and blood glucose still limits the clinical usage of tears. Herein, we present an in-depth investigation of the correlation between tear glucose and blood glucose using a wireless and soft smart contact lens for continuous monitoring of tear glucose. This smart contact lens is capable of quantitatively monitoring the tear glucose levels in basal tears excluding the effect of reflex tears which might weaken the relationship with blood glucose. Furthermore, this smart contact lens can provide an unprecedented level of continuous tear glucose data acquisition at sub-minute intervals. These advantages allow the precise estimation of lag time, enabling the establishment of the concept called ‘personalized lag time’. This demonstration considers individual differences and is successfully applied to both non-diabetic and diabetic humans, as well as in animal models, resulting in a high correlation. The correlation between tear glucose and blood glucose is still controversial. Here, the authors demonstrated the correlation between tear glucose and blood glucose in both animal models and human subjects using smart contact lenses.

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model.

Various types of photovoltaic (PV) modules have been developed to exploit solar energy, a major renewable energy resource. One of the popular types of PV modules is building-integrated photovoltaics (BIPV), which are PV modules used as building materials. The goal of this study is to conduct an economic feasibility analysis of BIPV on the rooftop of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas. To this end, a polynomial regression (PR) model is developed to estimate the electricity generation quantity of solar energy in the subject area, where the maximum temperature is 104 °F (40 °C) in summer. The developed PR models are used to estimate the potential profit of BIPV on the rooftop of the subject building, and the results are compared with the profit of a green roof system at the subject building. The economic feasibility analyses show that the levelized cost of electricity (LCOE) of the green roof system is approximately 39.77% higher than that of the BIPV system at a discount rate of 5%. Thus, the BIPV system is more profitable than the green roof system; consequently, this research will contribute to the implementation of BIPV on building rooftops and the expansion of renewable energy use in preference to fossil fuel.

Building-integrated photovoltaics (BIPVs) entail the use of photovoltaics as building materials, such as windows, roofs, and walls. Owing to their electricity-generation ability, BIPVs have become popular building materials for green buildings. This study involves an economic feasibility analysis of BIPVs for an agricultural research and development center in Fiji. The computerized relative allocation of facilities technique (CRAFT) is extended for cost-efficient facility design, and the levelized cost of electricity (LCOE) of BIPVs is evaluated to identify feasible alternatives from among three options, namely roof-integrated, window, and rooftop BIPVs. From the experiments, the LCOE values of the roof-integrated, window, and rooftop BIPVs were USD 0.13/kWh, 0.17/kWh, and 0.09/kWh, respectively. Moreover, the profit for the window BIPV was negative when the discount rate was greater than 0.08 due to its inefficient productivity. Contrarily, the roof-integrated BIPV showed a reasonable LCOE even though it required the highest investment cost. Nevertheless, further efforts are needed to reduce the cost of BIPVs for practical implementation.

Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled restaurant grease incurs higher production costs, so its supply chain should be managed efficiently for operational cost reduction. To this end, multiple machine learning technologies have recently been applied to estimate feedstock yield, biodiesel productivity, and biodiesel quality. This study aims to identify the machine learning technologies useful in particular areas of supply chain management by review of the scientific literature. As a result, nine machine learning algorithms, the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR). and multilayer perceptron (MLP), are used for feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Among these, RF and ANN were identified as the most appropriate algorithms, providing high prediction accuracy. This finding will help engineers and managers understand concepts of machine learning technologies so they can use appropriate technology to solve operational problems in supply chain management.

Bioadhesive devices can be used to create conformable tissue–device interfaces without suturing. However, the development of such technology faces challenges related to the need for external stimuli or long periods of time for tissue adhesion, fatigue-related breakdown of the stretchable electrodes and the use of solid substrates with non-uniform surface coverage of the tissue. Here, we report a bioelectronic patch that is capable of instantaneous and conformable tissue adhesion on a heart for precise cardiac monitoring. The patch is composed of three layers: an ionically conductive tissue adhesive, a viscoelastic networked film and a fatigue-resistant conducting composite. The system provides conformable tissue adhesion in less than 0.5 s without external stimuli, spontaneous modulus matching based on efficient strain adaptivity and small resistance changes of less than 0.2% at 50.0% tensile strain after 1,000 stretching cycles. We show that the patch can be used for the long-term measurement of electrocardiogram signals (up to four weeks of implantation) in awake rats without causing tissue damage, as well as spatiotemporal mapping in a myocardial ischaemia reperfusion model. A bioelectronic patch that is composed of three layers—an ionically conductive tissue adhesive, a viscoelastic networked film and a fatigue-resistant conducting composite—is capable of instantaneous and conformable tissue adhesion on a heart for precise cardiac monitoring and feedback stimulation.

Ethanol, a common renewable energy resource, can reduce greenhouse gas (GHG) emissions to resolve the problem of global warming worldwide. Various feedstocks such as corn, sugarcane, maize stover, and wheat straw can be utilized for ethanol production. They determine production operations and relevant costs. Although there are monetary incentives and government policies in different countries to increase ethanal use, it is still challenging to make its sales price competitive due to the inefficient supply chain of ethanol. Unlike fossil fuels such as coal, oil, and natural gas using a well-designed supply chain in the long history of mankind, additional efforts are needed to organize and stabilize the supply chain of ethanol efficiently. The goal of this study is to investigate how simulation modeling techniques can be applied to various supply chain management issues of ethanol. Particularly, application cases of three major simulation paradigms such as discrete-event simulation, system dynamics, and agent-based simulation are investigated by conducting a scientific literature review. The findings of this study will contribute to the expansion of simulation use in the field of biofuel supply chain management.

Improving the accuracy of yield predictions for cash crops such as pepper ( Capsicum annum L.) has increasingly captured the interest of many scientists in South Korea. This study marks the first initiative to develop yield prediction tools for peppers cultivated under heat stress conditions. To refine the yield prediction model, field studies were conducted to establish the plant growth curve and parameter sets for two different pepper accessions, PHR18 and PHR23, under heat stress conditions. According to field studies, the two pepper accessions exhibited distinct growth patterns under prolonged heat stress conditions. PHR18 experienced significant heat stress effects in the first month of exposure, whereas it demonstrated stress priming to regain growth by the 75 th day of heat stress exposure. PHR23, having a larger leaf area, accumulated more biomass than fruit yields in the initial month of exposure, thus increasing its yields at higher temperature conditions due to enhanced photosynthesis rates. The crop growth curve and parameters were formulated based on these studies, and the open field simulations were calibrated with measured yields from multiple locations in South Korea from 2020-2024. Consequently, a robust pepper growth model was developed and employed to assess the effects of heat stress on the yields of two pepper accessions across various South Korean locations. The development of this crop growth model under stressful conditions will aid farmers and policymakers in making informed decisions during extreme events.