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Nano-engineered oxides play a frontier role in the development of next-generation catalysts and microelectronics. Recently, metal exsolution from oxides has emerged as a promising nano-structuring tool to fabricate nanoparticle-decorated oxides. However, controlling the size, density, composition, and location of exsolved nanoparticles remains a challenge, limiting the ultimate performance achievable by these nanostructures. Here, we present ion irradiation as a general platform to allow control over these parameters during metal nanoparticle exsolution, by simultaneous sputtering, implantation, and defect generation mechanisms. Using thin-film perovskite and binary oxides as model systems, we showed ion beams can controllably reduce the size of exsolved nanoparticles down to 2 nm through ion sputtering. Meanwhile, we tailored the exsolved nanoparticle composition from unitary metal to metal alloy via ion implantation. Furthermore, irradiation creates point defects and defect clusters, which serve as nucleation sites for metal exsolution. By leveraging this process, we tuned the density and spatial distribution of exsolved nanoparticles. Finally, we demonstrated that nanocatalysts prepared by irradiation-assisted exsolution exhibit superior catalytic activity toward water-splitting reactions than those produced using conventional exsolution methods. These findings highlight the potential of ion irradiation for engineering nanoparticle exsolution in diverse materials systems, with broad implications for electrochemical and electronic applications.

We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of “sweet spots”

A persistent challenge plaguing lithium-ion batteries (LIBs) is the consumption of active lithium with the formation of SEI. This leads to an irreversible lithium loss in the initial cycle and a gradual further exhaustion of active lithium in subsequent cycles. While prelithiation has been proven effective in compensating for this loss by introducing additional active lithium into batteries, prior studies have predominantly concentrated on offsetting the initial lithium loss, often overlooking the continuous lithium consumption that occurs throughout cycling. To address this challenge, we employed a sustained in situ lithium replenishment strategy that involves the systematic release of additional lithium inventory through precise capacity control during long-term cycling. Our method utilizes a lithium replenishment separator (LRS) coated with dilithium squarate-carbon nanotube (Li2C4O4–CNT) as the lithium compensation reagent. Placing Li2C4O4 on the separator rather than within the cathode significantly reduces disruptions in conduction pathways and inhibits catalytic reactions with LiFePO4, preventing the formation of carbon residues. When implemented in the LiFePO4||graphite battery system, our approach resulted in an impressive 12.9% capacity improvement in the initial cycle and a remarkable 97.2% capacity retention over 700 cycles, surpassing the comparison group, which exhibited 80% capacity retention after 426 cycles., Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China

Wildfires lead to dramatic increases in fine particulate matter pollution concentrations. Based on the premise that higher-income households purchase more defensive investments to reduce the degree to which outdoor pollution infiltrates indoors, in this study, we investigate how income contributes to outdoor–indoor pollution infiltration rates during wildfire events. Using crowd-sourced data from the PurpleAir Real-Time Air Quality Monitoring Network and econometric models that explore variations in monitor readings over time, we find increases in outdoor pollution lead to significant increases in indoor pollution, but disproportionately so in lower-income areas. The results highlight a new inequality in pollution exposure: not only are outdoor pollution levels higher for lower-income individuals, but indoor pollution levels are higher even for similar outdoor pollution levels.

Sr(Ti,Fe)O3−y perovskite solid solutions are relevant functional materials for energy conversion and electronic devices such as solid oxide fuel and photoelectrochemical cells, electrolyzers, oxygen sensors, resistive random access memories or synaptic transistors. The Raman spectra and vibrational characteristics of the Sr(Ti,Fe)O3−y materials class are suitable for describing their defect chemistry and the iron valence state, which governs a multitude of its mixed ionic–electronic transport and other characteristics. We synthesize a standard range of compositions containing 1–75 mol% of iron including the end members in the form of macrocrystalline bulk pellets, nanocrystalline poly- and single crystalline thin films. Through the change in both iron substitution level and microstructure, we directly see the effect of defect chemistry such as its phase, transition metal ion valence and oxygen nonstoichiometry on the Raman spectra. These are discussed in terms of in and ex situ experiments under oxidizing/reducing atmosphere. In contrast to long range structural X-ray diffraction measurements, Raman spectroscopy provides valuable insights into oxygen vacancy ordering and oxygen nonstoichiometry for the Sr(Ti,Fe)O3−y material class., Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with

Designing efficient catalysts that possess large number of active sites, high catalytic activity and selectivity while also exhibiting strong antimicrobial activity is a challenging task because of poor control over material fabrication. Therefore, developing new and innovative approaches for the synthesis of catalytic materials is crucial for addressing these challenges. Here, we report the controlled fabrication of GQDs/Y-doped MgO nanoparticles achieved by co doping of yttrium (Y) and graphene quantum dots (GQDs) in magnesium oxide (MgO) based nanostructures (NSs) using the co-precipitation method. The co-doping of GQDs and Y was controlled by manipulating the ratio of precursors where introduction of GQD resulted in higher surface area and enhanced conductivity while the doping of Y enhanced the number of active sites in the final product. The GQDs/Y-doped MgO exhibited an average particle size of similar to 50 nm and a bandgap of 3.6 eV. Owing to these excellent characteristics, the GQDs/Y-doped MgO was utilized as a catalyst for treatment of the organic pollutants from water as well as antibacterial activity. The modified GQDs/Y-doped MgO nanostructure exhibited excellent activity of over 99.9 % for dye removal and versatility in a broad range of pH which clearly indicated the application in a range of different environments. Furthermore, the GQDs/Y-doped MgO exhibited excellent antibacterial activity against Escherichia Coli (E. Coli) bacteria. To gain further insights into the origins of this excellent activity response, the molecular docking simulations (MDS) is utilized against DNA gyrase and FabI (two enzymes critical to nucleic acid and fatty acid biosynthesis, respectively), to uncover the mechanism behind the observed antibacterial effects. In summary, the

One challenge of teaching and learning the lack of information during these processes, including information about students’ emotions. Emotions play a role in learning and processing information, impacting accurate comprehension. Furthermore, emotions affect students’ academic engagement and performance. Consideration of students’ emotions, and therefore their well-being, contributes to building a more sustainable society. A new way of obtaining such information is by monitoring students’ facial emotions. Accordingly, the purpose of this study was to explore whether the use of such advanced technologies can assist the teaching–learning process while ensuring the emotional well-being of secondary school students. A model of Emotional Recognition (ER) was designed for use in a classroom. The model employs a custom code, recorded videos, and images to identify faces, follow action units (AUs), and classify the students’ emotions displayed on screen. We then analysed the classified emotions according to the academic year, subject, and moment in the lesson. The results revealed a range of emotions in the classroom, both pleasant and unpleasant. We observed significant variations in the presence of certain emotions based on the beginning or end of the class, subject, and academic year, although no clear patterns emerged. Our discussion focuses on the relationship between emotions, academic performance, and sustainability. We recommend that future research prioritise the study of how teachers can use ER-based tools to improve both the well-being and performance of students.

This research paper presents a data-driven framework for design optimization of renewable energy communities (RECs) in the residential sector, considering both techno-economic challenges and environmental impact. The study's focus is to determine suitable sizes for photovoltaic systems, wind turbines, and battery electrical energy systems by evaluating energy, economic, and environmental criteria. To achieve this, we develop a data-driven model that incorporates Homer Pro and an in-house tool developed in Python programming language that integrates a machine learning algorithm, life cycle cost (LCC), life cycle assessment (LCA) calculations of the REC model. Furthermore, a multi-objective optimization model is established to minimize the LCC and LCA parameters while maximizing green energy use. Moreover, a multi-criteria decision-making approach based on Weighted Sum Model (WSM) is proposed to help the stakeholders to see beyond the selection criteria based on LCC and LCA to choose the most appropriate scenario optimal solution for the desired energy community and interpret the effect of various economic parameters on the sustainable performance of REC. The framework application is illustrated through a case study for the optimal design of REC for a residential community in Tarragona, Spain, consisting of 100 buildings. The results revealed a substantial improvement in economic , environmental benefits for designing REC, the optimal minimum cost solution with a levelized cost of energy (LCOE = 0.044 $/kWh) and a payback period of 7.1 years with an LCOE reduction of 85.04% compared to the base case. The minimum impact with an LCOE = 0.220 $/kWh and a payback period of 12.5 years with a reduction in environmental impact of 54.59% compared to the base case. Overall, the de

Renewable energies can reduce emissions from fossil fuel-based power production by a significant amount, reducing climate change. Among the several options being investigated, hydrogen is now recognized as one of the primary enabling technologies for future large-scale and long-term green storage of renewable energy. However, using clean and renewable energy sources like water and sunlight is the best option. According to recent studies, photoelectrochemical (PEC) water splitting is a potential technology for creating hydrogen using free solar energy since hydrogen and oxygen gases may be easily identified in PEC. This study uses bibliometric and systematic literature review methodologies to examine the scientific understanding of PEC water splitting and hydrogen generation, evaluating 936 articles issued in the Web of Science database from 1970 to 2022. The authors looked at how PEC water splitting research has developed compared to prior research, what criteria make up practical approaches, and what subjects have emerged as new trends in reacting to changing situations. The key findings from the article analysis, future research potential, and practical implications for the field are discussed.

Years of study have shown that creating a commercial photoelectrode to solve particular bottlenecks, such as low charge separation and injection efficiency, short carrier diffusion length and lifespan, and poor stability, requires the employment of a variety of components. Developing photovoltaic-electrolysis, photocatalytic, and photoelectrochemical approaches to accelerate hydrogen production from solar energy has been highly competitive. Photoelectrochemical water splitting utilizing nanoporous materials is one of the promising approaches to produce hydrogen more efficiently, cost-effectively, and on a long-term basis. Nanoporous materials have been highly used in photoelectrochemical water-splitting systems and are crucial in numerous applications. Those materials have a porous structure and excellent conductivity, enabling the deposition of transition metal atoms and electrochemically active chemicals on a large active surface area. However, there remains a dearth of review articles exploring the application of nanoporous materials in photoelectrochemical reactions. Therefore, this review provides bibliometric statistics and various perspectives on a range of nanoporous materials, including indium, nickel, gold, copper, lead, silver, aluminum, silicon, tin, iron, zinc, titanium, bismuth vanadate, cadmium sulfide, and zeolites. Additionally, this review offers a comprehensive assessment of worldwide studies on utilizing nanoporous materials in photoelectrochemical cells. We show how morphological modifications to materials may improve charge transfer and, as a consequence, overall power conversion efficiency.ke The superior catalytic performance of nanostructures with varying levels of complexity has been discovered in photoelectrochemical reactions. Finally, signif

The study addresses the growing concern of microplastic pollution in environmental matrices, emphasizing the significance of monitoring to understand their distribution, sources, and mitigation. Laboratory-based spectral reflectance analysis of water samples containing visible microplastics revealed distinctive spectral signatures. Coastal water samples collected over two campaigns were subjected to pretreatment to extract microplastics, and spectroscopic confirmation was followed by microscopic inspection. Results indicated average microplastics concentrations of 0.633 and 0.324 mg/L and 7.85 and 5.30 items/L in the datasets. Leveraging these findings, along with Sentinel-2 (Level-1C) data and spectral signatures, an empirical spectral microplastics model was developed to convert Sentinel-2's reflectance into microplastics concentrations. This model displayed an R2 of 87.30% and RMSE of +/- 0.015 mg/L. Subsequently, the model was employed to estimate microplastics concentrations in 2018, 2019, 2020, and 2021, showcasing its potential for monitoring microplastic pollution in the study area and similar regions.

The European chemical industry plays a crucial role in the economy, manufacturing thousands of products that find applications across many sectors. On the other hand, the sector is also one of the largest energy consumers, and emits significant amounts of greenhouse gases, together with toxic, bioaccumulative and persistent chemicals. Acknowledging the need for a shift towards a greener industry model, this contribution assesses the absolute sustainability level of the chemical sector by contrasting the impacts it causes with the ecological capacity of the planet, as given by the planetary boundaries framework. Results indicate that impacts incurred by the European chemical industry transgress CO2-based thresholds significantly, with burdens on atmospheric CO2 concentration, energy imbalance, and ocean acidification being 15, 16 and 6 times larger than planetary limits. The biosphere integrity boundary, assessed here based on functional diversity, is also transgressed by 3 %. The five chemicals with the highest production volume, namely ammonia, polypropylene, high-density polyethylene, styrene, and benzene, are responsible for around 50 % of impacts in all the planetary boundaries. This suggests that they should be the target of dedicated research and policies. Finally, the study assesses a set of improvement pathways for the European chemical industry to become sustainable. It is found that the deployment of carbon capture and storage technologies to compensate for the greenhouse gases emitted by the sector would allow the chemical industry to meet all the planetary boundaries concurrently, yet burden-shifting would still cause other planetary boundaries to be deteriorated. This evidences the need to employ holistic approaches to assess the wide implications of any so