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The utilization of fossil fuels for power generation results in the production of a greater quantity of pollutants and greenhouse gases, which exerts detrimental impacts on the ecosystem. A range of solar energy technologies can be employed to address forthcoming energy demands, concurrently mitigating pollution and protecting the world from global threats. This study critically reviewed all four generations of photovoltaic (PV) solar cells, focusing on fundamental concepts, material used, performance, operational principles, and cooling systems, along with their respective advantages and disadvantages. The manuscript analyzes various materials, including their performance, physical properties (electronic and optical), biodegradability, availability, cost, temperature stability, degradation rate, and other parameters. The sensible engineering of effective solar devices made of cutting -edge materials along with nanostructured ternary metal sulphides, and three-dimensional graphene are also briefly discussed which are more versatile, stable, thin and light weight with high performance as compare to third generation solar cells. The impact of material alterations is delineated in PV, where the efficiency of solar cell technology has improved from 4% to 47.1%. Further the research article deals with different internal and external stress factors affecting the solar PV module performance.

The empirical finding that countries endowed with vast reserves of natural resources are expected to experience slower economic growth – the resource curse hypothesis – has sparked debate in the literature about whether natural resources are a curse or a boon. In this study, we re-investigate the natural resource, corruption and growth nexus by using a relatively longer dataset for a panel of countries. Unlike previous attempts, we take into account the potential endogeneity and asymmetric effect in our analysis by applying a recently developed panel quantile estimator. We also focus on the role of corruption in influencing the impact of natural resources on economic growth. Broadly the findings are indicative of an asymmetric effect of resources as the sign and magnitude of natural resources’ impact on economic growth varies over different income quantiles. Although the overall results are mixed, but the results based on fuel export and oil–gas rents as measures of resource endowment are consistent with the ‘resource curse’ hypothesis. Nonetheless, the findings suggest that corruption is critical in determining the marginal impact of natural resources on growth and in many cases, it has effectively transformed the negative effects of natural resources to positive effects in low-to-middle-income countries.

Plastic pollution is ubiquitous in terrestrial and aquatic ecosystems. Plastic waste exposed to the environment creates problems and is of significant concern for all life forms. Plastic production and accumulation in the natural environment are occurring at an unprecedented rate due to indiscriminate use, inadequate recycling, and deposits in landfills. In 2019, the global production of plastic was at 370 million tons, with only 9% of it being recycled, 12% being incinerated, and the remaining left in the environment or landfills. The leakage of plastic wastes into terrestrial and aquatic ecosystems is occurring at an unprecedented rate. The management of plastic waste is a challenging problem for researchers, policymakers, citizens, and other stakeholders. Therefore, here, we summarize the current understanding and concerns of plastics pollution (microplastics or nanoplastics) on natural ecosystems. The overall goal of this review is to provide background assessment on the adverse effects of plastic pollution on natural ecosystems; interlink the management of plastic pollution with sustainable development goals; address the policy initiatives under transdisciplinary approaches through life cycle assessment, circular economy, and sustainability; identify the knowledge gaps; and provide current policy recommendations. Plastic waste management through community involvement and socio-economic inputs in different countries are presented and discussed. Plastic ban policies and public awareness are likely the major mitigation interventions. The need for life cycle assessment and circularity to assess the potential environmental impacts and resources used throughout a plastic product’s life span is emphasized. Innovations are needed to reduce, reuse, recycle, and recover plastics and find eco-friendly replacements for plastics. Empowering and educating communities and citizens to act collectively to minimize plastic pollution and use alternative options for plastics must be promoted and enforced. Plastic pollution is a global concern that must be addressed collectively with the utmost priority.

The spectrum of Internet of Things (IoT) applications is exponentially growing, driving the demand for better energy performance metrics. In conjunction, Low Power Wide Area Networks (LPWAN) have evolved as long-range connectivity enabler with low management cost. The integration of LPWAN communication assists in reliable IoT operation with extended lifetime. Notable LPWAN technologies that contend for many of the IoT applications are LoRaWAN, DASH7, Sigfox, and NB-IoT. Most of the end-devices such as sensors and actuators are battery powered, therefore investigating energy consumption becomes crucial. To estimate the consumed power, it is important to analyze the energy consumption in wireless communication. This paper describes an empirical evaluation of energy consumption for LPWAN wireless technologies. We measure the current consumption of LoRaWAN, DASH7, Sigfox, and NB-IoT and derive the respective battery lifetime. These measurements help to quantify the energy performance of different protocols. We observe that LoRaWAN and DASH7 are more energy efficient when compared to Sigfox and NB-IoT. Finally, a case study on energy consumption is done on precision agriculture in the greenhouse, showing that battery lifetime in real applications can drop significantly from the ideal case. These results can be used for increasing the effectiveness of the IoT application by selecting the right technology and battery capacity.

Cell senescence (CS) is at the nexus between aging and associated chronic disorders, and aging increases the burden of CS in all major metabolic tissues. However, CS is also increased in adult obesity, type 2 diabetes (T2D), and nonalcoholic fatty liver disease independent of aging. Senescent tissues are characterized by dysfunctional cells and increased inflammation, and both progenitor cells and mature, fully differentiated and nonproliferating cells are afflicted. Recent studies have shown that hyperinsulinemia and associated insulin resistance (IR) promote CS in both human adipose and liver cells. Similarly, increased CS promotes cellular IR, showing their interdependence. Furthermore, the increased adipose CS in T2D is independent of age, BMI, and degree of hyperinsulinemia, suggesting premature aging. These results suggest that senomorphic/senolytic therapy may become important for treating these common metabolic disorders.

The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

Since the second half of the 20th century, bioceramics are widely used as a substitute for hard tissue engineering. Bioceramics including zirconia, alumina, hydroxyapatite, and bioactive glass plays a vital role in the design of tissue engineering scaffolds with tailor-made properties. The bioceramics scaffold has been used to replace or regenerate the tissue of the human body due to its excellent mechanical strength, biocompatibility, chemical stability, corrosion restriction behavior, and wear resistance. The advancement in technology has made the form of bioceramics and their function, structure, and composition more diversified, and manifold. Considering the promising role of the use of bioceramics in tissue engineering, we proposed this review, presenting the classification of bioceramics, their physiochemical properties, degradation pathway, product development so far, and their application in tissue engineering. The calcium phosphate and bioglass-based composite scaffold have also been discussed in detail. Furthermore, this paper also discussed the toxicity evaluation and market perspective of bioceramics. Thus, this review gives a complete insight into bioceramics for tissue engineering applications.

Strategies combining high-throughput (HT) and machine learning (ML) to accelerate the discovery of promising new materials have garnered immense attention in recent years. The knowledge of new guiding principles is usually scarce in such studies, essentially due to the ‘black-box’ nature of the ML models. Therefore, we devised an intuitive method of interpreting such opaque ML models through SHapley Additive exPlanations (SHAP) values and coupling them with the HT approach for finding efficient 2D water-splitting photocatalysts. We developed a new database of 3099 2D materials consisting of metals connected to six ligands in an octahedral geometry, termed as 2DO (octahedral 2D materials) database. The ML models were constructed using a combination of composition and chemical hardness-based features to gain insights into the thermodynamic and overall stabilities. Most importantly, it distinguished the target properties of the isocompositional 2DO materials differing in bond connectivities by combining the advantages of both elemental and structural features. The interpretable ML regression, classification, and data analysis lead to a new hypothesis that the highly stable 2DO materials follow the HSAB principle. The most stable 2DO materials were further screened based on suitable band gaps within the visible region and band alignments with respect to standard redox potentials using the GW method, resulting in 21 potential candidates. Moreover, HfSe2 and ZrSe2 were found to have high solar-to-hydrogen efficiencies reaching their theoretical limits. The proposed methodology will enable materials scientists and engineers to formulate predictive models, which will be accurate, physically interpretable, transferable, and computationally tractable.

Anode-free or ‘zero-excess’ lithium metal batteries offer high energy density compared to current lithium-ion batteries but require electrolyte innovation to extend cycle life. Due to the lack of universal design principles, electrolyte development for anode-free lithium metal batteries is slow and incremental and mainly driven by trial-and-error. Here, we demonstrate the use of active learning as an alternative approach to accelerate electrolyte discovery for anode-free lithium metal batteries. Unlike conventional data-intensive frequentist machine learning techniques, our active learning framework employs sequential Bayesian experimental design with Bayesian model averaging to efficiently identify optimal candidates in typical data-scarce and noisy label settings. Using capacity retention in real Cu||LiFePO 4 cells as the target property, our approach integrates experimental feedback to iteratively refine predictions. Starting with just 58 data points from an in-house cycling dataset, the active learning framework explored a virtual search space of 1 million electrolytes, rapidly converging on optimal candidates. After seven active learning campaigns with about ten electrolytes tested in each, four distinct electrolyte solvents are identified that rival state-of-the-art electrolytes in performance. This work showcases the promise of active learning approaches in navigating large electrolyte chemical spaces for next-generation batteries. Next-generation batteries require innovative electrolytes, but conventional methods are tedious and costly. Here, authors develop an active learning framework to rapidly identify seven efficient electrolytes for anode-free lithium-metal batteries, accelerating electrolyte discovery.

Earthen embankments are built to prevent flooding and protect communities from the dangers of floods and high water levels. However, these geotechnical structures may not always remain serviceable and can fail due to long-term seepage and ponding. For instance, erosion causes the earthen structure to weaken and eventually fail, which may be due to several factors, including the velocity of the water, soil water characteristics, fine content, and gradation of the soil. The presented research explores an advanced approach to address the critical issue of identifying the seepage and ponding through the embankment by assimilating the passive infrared thermographic imageries with Deep Learning (DL) algorithms. To facilitate the development and validation of developed DL frameworks, a physical experimentation setup at the model scale is developed. This platform enabled the generation of a comprehensive dataset of thermal images across various environmental scenarios, including vegetation coverage and rainfall. Multiple DL frameworks were initially explored within the framework and the models were designed to process sequences of thermal images and predict the extent of seepage and ponding. This research builds upon effectively transforming the complex task of embankment leakage identification into an image classification problem. Moreover, the developed framework demonstrates that mapping of seepage and ponding can be achieved with great accuracy and is vital in enhancing embankment safety and disaster prevention strategies in flood-prone areas.