Electrochemical water splitting is a promising approach for sustainable hydrogen production, but the oxygen evolution reaction (OER) remains a bottleneck due to sluggish kinetics, poor activity, and limited stability and scalability. Here, a MoN-functionalized nickel is designed foam (NF@MoN) and subsequently transform into a MoN/NiSe/NiP multi-phase heterostructure through selenization and phosphorization, to address these challenges. The optimized NF@MoN/NiSe/NiP catalyst integrates three key strategies: (I) functionalizing NF with MoN to enhance conductivity and charge transfer, (II) engineering a collaborative multi-interface heterostructure to optimize active sites and reaction kinetics, and (III) precisely controlling phase formation through selenization and phosphorization to mitigate surface reconstruction and ensure long-term stability. The catalyst not only achieves an overpotential of 242 mV@10 mA cm and remarkable stability over 350 h, but also achieves a low overpotential of 395 mV at a high current density of 800 mA cm, outperforming the pristine other control samples. Theoretical analysis reveals that the MoN-stabilized NiSe/NiP heterostructure on NF enhances conductivity and optimizes adsorption energies of OER intermediates, leading to improved catalytic performance and stability. This work provides a new strategy for designing high-performance, non-precious metal OER catalysts for industrial applications and advancing sustainable hydrogen production.

Syntrophic propionate- and acetate-oxidising bacteria (SPOB and SAOB) play a crucial role in biogas production, particularly under high ammonia conditions that are common in anaerobic degradation of protein-rich waste streams. These bacteria rely on close interactions with hydrogenotrophic methanogens to facilitate interspecies electron transfer and maintain thermodynamic feasibility. However, the impact of mixing-induced disruption of these essential syntrophic interactions in biogas systems remains largely unexplored. This study investigates how magnetic stirring and orbital shaking influence degradation dynamics, microbial community composition, and gene expression in syntrophic enrichment communities under high-ammonia conditions.

Nowadays, energy conservation is a top priority for sustainable societies, which focus on environmental and economic sustainability to address fossil fuel scarcity, climate change, and increasing environmental pollution. Therefore, planning for energy management and forecasting energy consumption is essential, especially with societies striving to achieve sustainable development. Predictive distribution plans for consumers and utilities can be improved using data mining-based models, the most common of which are big data-based Machine Learning (ML) models. This work presents a comprehensive ML model that combines the use of MATLAB for data reduction using Principal Component Analysis (PCA), and one of the latest data mining tools Orange 3 to build a classification model consisting of four basic classifiers: AdaBoost, Logistic Regression (LR), Naive Bayes (NB), and Stochastic Gradient Descent (SGD). The model relies on a dataset of energy consumption of different devices according to the Kaggle platform, where the energy consumption data was collected every 10 min and over approximately 4.5 months using m-bus energy meters. The model was tested based on the confusion matrix, and the results showed that AdaBoost outperformed other models in predicting energy consumption, with 100 % accuracy. In addition, LR, NB, and SGD had classification accuracy of 99.8 %, 99.7 %, and 99.4 %, respectively.

The integration of sustainability into nursing practice is critical for addressing the environmental challenges posed by healthcare systems. Nurses, as frontline healthcare providers, are uniquely positioned to lead sustainability initiatives, though collaboration with other healthcare professionals is essential. However, nurses' engagement in sustainability behaviors is influenced by a complex interplay of attitudes, practices, barriers, and facilitators, which are not fully understood. This study aimed to explore nurses' attitudes, practices, and barriers and facilitators toward sustainability behaviors in clinical settings.

Limiting global warming increasingly relies on the development of environmentally friendly CO capture strategies. Crystallization is renowned for versatile separation and purification, yet traditional compound crystallization-based CO capture still necessitates intricate preparation processes, stringent reaction conditions, and high regenerative energy consumption. As an ambitious sustainability goal, natural water could be used as a precursor of crystallization to construct hydrogen-bonded water cages for CO capture, but main obstacles are slow crystallization kinetics and low capture capacity. Here, a water-activation-induced crystallization strategy by organic magnetic nanoparticles (Methionine@FeO) has been proposed for efficient CO capture. Local water ordering strengthened by hydrophobic amino acids and abundant nucleation sites provided by nanoparticles create hotspots for hydration phase transition and crystal growth, with a CO capture capacity of 118.7 v/v (22.7 wt%). Favorable biocompatibility and stable performance are conducive to the industrial application of this nanomaterial, and the excellent magnetic recyclable property enables simple separation from clean water. This strategy demonstrates an extraordinary CO capture potential compared to state-of-the-art systems, thus providing an inspiration for sustainable CO capture and storage with zero resource depletion (ZRD).

A volumetric lattice Boltzmann (LB) method is developed for the particle-resolved direct numerical simulation of thermal particulate flows with conjugate heat transfer. This method is devised as a single-domain approach by applying the volumetric interpretation of the LB equation and introducing a solid fraction field to represent the particle. The volumetric LB scheme is employed to enforce the nonslip velocity condition in the solid domain, and a specialized momentum exchange scheme is proposed to calculate the hydrodynamic force and torque acting on the particle. To uniformly solve the temperature field over the entire domain with high numerical fidelity, an energy conservation equation is first derived by reformulating the convection term into a source term. A corresponding LB equation is then devised to automatically achieve the conjugate heat transfer condition and correctly handle the differences in thermophysical properties. Theoretical analysis of this LB equation is also performed to derive the constraints to preserve the numerical fidelity even near the solid-fluid interface. Numerical tests are first performed to validate the present volumetric LB method in various aspects. Then the sedimentation of a cold particle with conjugate heat transfer in a long channel is investigated. It is found that the sedimentation process can be divided into the accelerating, decelerating, and equilibrium stages. As a further application to dense particulate flows, the sedimentation of 2048 cold particles with conjugate heat transfer in a square cavity is simulated. The particulate Rayleigh-Bénard convection is successfully captured in this particle-resolved simulation.

As the sole commercial polycarbonate derived from CO2, poly(propylene carbonate) (PPC) is still hindered by poor thermal stability and a low glass transition temperature. Hererin, we first report the terpolymerization of CO2, propylene oxide (PO) and epichlorohydrin (ECH) to synthesize PPC-ECH terpolymers via one-pot and metal-free method using multi-nuclear organoboron catalysts. The PPC-ECH terpolymers with well-rounded properties can be easily synthesized by adjusting reaction conditions, monomer ratios, catalyst types and loading. The molecular weight of the PPC-ECH terpolymer can reach up to 59.4 kg/mol, which is the highest known. Notably, the synthesized PPC-ECH exhibits outstanding barrier performance with oxygen permeability as low as 1.31 cm3·mm/(m2·day) and water vapor permeability as low as 0.016 g·mm/(m2·day), significantly lower than those of pure PPC or other common packaging materials. Importantly, the introduction of a small amount of ECH not only preserves the biodegradability of polycarbonate but also markedly enhances its mechanical properties, glass transition temperature, thermal stability and flame retardancy, offering promising prospects for application in packaging materials.

Guangdong's carbon emissions have surpassed the world's 11th largest emitter. It is indispensable for this province to find a robust cost-effective strategy for reducing carbon emissions. This study employed the Low Emissions Analysis Platform model, marginal cost curves, and Monte Carlo methods to simulate the energy consumption, carbon emissions, and economic benefits of emission reduction in Guangdong Province from 2020 to 2030 under the application of various structural optimization policies and energy-saving technologies. The main findings are as follows: In 2030, Guangdong Province is projected to achieve a carbon emission reduction of 273.6 to 304.6 million t CO with a total reduction cost ranging from 1030.9 to 1452.2 billion yuan. Increasing the share of renewable energy, which still has significant growth potential, can lead to a 1.4 times greater reduction in carbon emissions compared to the application of energy-saving technologies, despite the latter yielding 2.3 times more energy savings. The emission reduction measures with net-cost can contribute 71.4 % to the total carbon reduction of the province, being much larger than those with net benefits. The power sector plays a critical role in carbon emission reduction within Guangdong Province, with its various measures exerting the most substantial impact on emission reduction quantity and cost, contributing cumulative variance contributions of 90.1 % and 84.3 %, respectively. It has relatively large potential for emission reduction and relatively low cost of structural adjustment.

Within the context of advancing global sustainable development goals, universities are recognized as leaders in energy conservation and emissions reduction within the education sector. Universities should actively engage in the accounting and analysis of carbon emissions. This study uses Sichuan University Jinjiang College(Hereafter referred to as J University) in Sichuan, China, as a case study, where the campus's carbon emissions for the year 2023 were calculated using the Emission Factor Method and the Delphi Method. The uncertainty associated with these emissions was further explored using Monte Carlo simulation. The results indicate that the net carbon emissions of J University amounted to 44,584.33 tons of CO2 equivalent (tCO2e), with per capita emissions of 1.89 tCO2e. The primary sources of campus carbon emissions, in descending order, include electricity (18879.94tCO2e), natural gas (8647.25tCO2e), business travel (5224.55tCO2e), campus commuting (3852.33tCO2e), food (3444.67tCO2e), and thermal energy (2566.63tCO2e). Among these sources, the carbon emissions from electricity, natural gas, and thermal energy were closely correlated with seasonal and regional factors. The uncertainties related to commuting and business travel had the most significant impact on the overall carbon emissions accounting for the campus. The study presents a framework for campus carbon emission accounting, providing a concrete case study for future researchers in this field. In particular, an in-depth exploration of statistical uncertainties is conducted, offering a scientific basis for the accurate calculation of carbon emissions in future studies.

Transmission line icing is a major natural hazard affecting overhead power lines, especially under specific meteorological conditions such as low temperatures, high humidity, and strong winds. Ice overload may cause line faults, structural damage, and even collapse of poles and towers. Traditional ice monitoring approaches have restrictions, such as discontinuous measurement and the inability to support the self-powered operation of the monitoring system. A self-powered monitoring method that utilizes a triboelectric nanogenerator (TENG) and a micro thermoelectric generator (MTEG) to assess the thickness and growth dynamics of ice on transmission lines is presented. A TENG-based ice thickness sensing model (HP-TENG) employing a PR/PDMS composite friction layer fabricated an AAO template method is established, integrated with bismuth telluride-based MTEG modules for enhanced energy harvesting and sensing capabilities. A prototype for monitoring the growth state of ice on transmission lines based on a TENG-MTEG is developed. An experiment system that integrates HP-TENGs, MTEGs, a signal processing unit, and a signal transmission unit is constructed. The system incorporates a multi-directional ice-cover growth signal processing unit, which can concurrently collect and process signals from six HP-TENG channels. The experimental results indicate that the HP-TENGs can accurately sense the ice thickness in the range of 10 mm-20 mm, achieving a maximum error of only 2.14%. It effectively monitors ice growth rates between 0.02 mm s and 1 mm s, with a maximum error of 3.65%. The MTEG unit demonstrates a maximum output voltage of 1.15 V and a maximum current of 180 mA. Furthermore, the multi-directional ice-cover growth signal processing unit processes the output signals from the HP-TENG and wirelessly transmits them to the microcontroller (MCU).