Search Results (2,644)

The increasing demand for lithium in energy storage technologies has renewed interest in clay-type deposits as alternative resources to brines and hard rock ores. This study investigates the leaching behavior of a Mexican clay-type lithium ore through conventional, hot, and pressure leaching using sulfuric acid. Mineralogical characterization (XRD and SEM–EDS) revealed that montmorillonite (~56 wt.%) is the primary lithium-bearing phase. Conventional leaching with 1–8 M H₂SO₄ resulted in limited lithium dissolution (<30% after 24 h), whereas hot leaching at 80 °C increased extraction to ~39%. Pressure leaching with oxygen overpressure significantly enhanced lithium dissolution, achieving ~64% within 180 min under 8 M H₂SO₄ and 80 °C. Kinetic modeling using a pseudo-first-order model accurately reproduced the extraction profiles, yielding increasing rate constants and equilibrium conversions with temperature. The low activation energy (~12 kJ·mol⁻¹) indicates that lithium dissolution proceeds through weakly activated reaction–solution interactions rather than diffusion through a product layer. These findings provide a mechanistic basis for understanding lithium release from clay-hosted ores and highlight the importance of optimizing acid concentration, temperature, and oxygen availability to improve hydrometallurgical processing of clay-type lithium deposits. Full article

Electrochemical ocean alkalinity enhancement is a form of marine carbon dioxide removal, a rapidly growing industry that is powered by efficient onshore or offshore energy sources. As more and larger deployments are being planned, it is important to consider how variable energy sources like tidal energy can impact plant performance and costs. An open-source Python-based generalizable model for electrodialysis-based ocean alkalinity enhancement has been developed that can capture key system-level insights of the electrochemistry, ocean chemistry, acid disposal, and co-product creation of these plants under various conditions. The model additionally accounts for hybrid energy system performance profiles and costs via the National Laboratory of the Rockies’ H2Integrate tool. The model was used to analyze an example theoretical plant deployment in North Admiralty Inlet, including how the plant is impacted by the available energy sources in the region and the scale at which plant costs are covered by the co-products it generates, such as recycled concrete aggregates, without requiring carbon credits. The results show that the example plant could be profitable without carbon credits at commercial scales of 100,000 to 1 million tons of carbon dioxide removal per year, so long as it uses low-cost electricity sources and either sells acid or recovers recycled concrete aggregates with about 1 molar acid concentrations, though more research is needed to confirm these results. Full article

Background/Objectives: Variants in the lipoprotein lipase (LPL) gene have been associated with lipid level variability and obesity; however, their role in energy homeostasis remains unclear. The aim of this study was to investigate the association of LPL single-nucleotide variants (SNVs) with lipid parameters and leptin concentrations in a cohort of prepubertal children. The sample population comprised 635 boys and 631 girls, with available information on lipid profiles and leptin levels. Methods: Five LPL SNVs (rs258, rs316, rs326, rs320, and rs328) were genotyped by Real-Time PCR using predesigned TaqMan™ Genotyping Assays. Results: An association of the LPL SNV rs258 was found with non-esterified fatty acid (NEFA) levels in males and with leptin concentrations in both sexes. On the other hand, an association of the LPL SNV rs326 was observed with low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (Apo-B) levels, displaying opposite trends in males and females. No significant associations with any of the parameters under study were observed for the remaining LPL SNVs. Conclusions: These results suggest that functional differences among LPL SNVs may either be related to an enhancement of catalytic activity or modulation of lipoprotein binding affinity, influencing the efficiency of remnant lipoprotein clearance. Full article

Applications of smart cities, smart buildings, smart agriculture systems, smart grids, and other smart systems benefit from Internet of Things (IoT) protocols, networks, and architecture. Wireless Sensor Networks (WSNs) in smart systems that employ IoT use wireless communication technologies between sensors in the Things layer and the Fog layer hub. Such wireless protocols and networks include WiFi, Bluetooth, and Zigbee, among others. However, the payload formats of these protocols are heterogeneous, and thus, they lack a unified frame format that ensures interoperability. In this paper, a lightweight, interoperable frame format for low-rate, small-size Wireless Sensor Networks (WSNs) in IoT-based systems is designed, implemented, and tested. The practicality of this system is underscored by the development of a gateway that transfers collected data from sensors that use the unified frame to online servers via message queuing and telemetry transport (MQTT) secured with transport layer security (TLS), ensuring interoperability using the JavaScript Object Notation (JSON) format. The proposed frame is tested using market-available technologies such as Bluetooth and Zigbee, and then applied to smart home applications. The smart home scenario is chosen because it encompasses various smart subsystems, such as healthcare monitoring systems, energy monitoring systems, and entertainment systems, among others. The proposed system offers several advantages, including a low-cost architecture, ease of setup, improved interoperability, high flexibility, and a lightweight frame that can be applied to other wireless-based smart systems and applications. Full article

This study presents a geospatial assessment and modeling of the water–energy–food (WEF) nexus to enrich the sustainable paddy cultivation of the North Central Province (NCP) of Sri Lanka in the Dry Zone. Increasing climatic variability and limited resources have raised concerns about the need for efficient resource management to restore food security globally. The study analyzed the three components of the WEF nexus for their synergies and trade-offs using GIS and remote sensing applications. The food productivity potential was derived using the Normalized Difference Vegetation Index (NDVI), Soil Organic Carbon (SOC), soil type, and land use, whereas water availability was assessed using the Normalized Difference Water Index (NDWI), Soil Moisture Index (SMI), and rainfall data. Energy potential was mapped using WorldClim 2.1 datasets on solar radiation and wind speed and the proximity to the national grid. Scenario modeling was conducted through raster overlay analysis to identify zones of WEF constraints and synergies such as low food–low water areas and high energy–low productivity areas. To ensure the accuracy of the created model, Pearson correlation analysis was used to internally validate between hotspot layers (representing extracted data) and scenario layers (representing modeled outputs). The results revealed a strong positive correlation (r = 0.737), a moderate positive correlation for energy (r = 0.582), and a positive correlation for food (r = 0.273). Those values were statistically significant at p > 0.001. These results confirm the internal validity and accuracy of the model. This study further calculated the total greenhouse gas (GHG) emissions from paddy cultivation in NCP as 1,070,800 tCO_2eq yr⁻¹, which results in an emission intensity of 5.35 tCO_2eq ha⁻¹ yr⁻¹, with CH₄ contributing around 89% and N₂O 11%. This highlights the importance of sustainable cultivation in mitigating agricultural emissions that contribute to climate change. Overall, this study demonstrates a robust framework for identifying areas of resource stress or potential synergy under the WEF nexus for policy implementation, to promote climate resilience and sustainable paddy cultivation, to enhance the food security of the country. This model can be adapted to implement similar research work in the future as well. Full article

The escalating prevalence of counterfeiting and forgery has imposed unprecedented demands on advanced anti-counterfeiting technologies. Traditional luminescent materials, relying on single-mode or static emission, are inherently vulnerable to replication using commercially available phosphors or simple spectral blending. Multimode luminescent materials exhibiting excitation wavelength-dependent emission offer significantly higher encoding capacity and forgery resistance. Herein, we report the colloidal synthesis of lanthanide-doped Cs₂ZrCl₆ nanocrystals (Ln³⁺ = Tb, Eu, Pr, Sm, Dy, Ho) via a robust hot-injection route. These nanocrystals universally exhibit efficient host-to-guest energy transfer from self-trapped excitons (STEs) under 254 nm, yielding sharp characteristic Ln³⁺ f–f emission alongside the intrinsic broadband STE luminescence. Critically, Tb³⁺ enables direct 4f → 5d excitation at ~275 nm, while Eu³⁺ introduces a low-energy Eu³⁺ ← Cl⁻ LMCT band at ~305 nm, completely bypassing STE emission. Due to their multimode luminescent characteristics, we fabricate a triple-mode anti-counterfeiting label displaying different colors under different types of excitation. These findings establish a breakthrough excitation-encoded multimode platform, offering potential applications for next-generation photonic security labels, scintillation detectors, and solid-state lighting applications. Full article

Effective mitigation of geotechnical risk and safety management of underground mine requires accurate estimation of rockburst damage potential. The inherent complexity of the rockburst phenomena due to nonlinear, high dimensional, and interdependent nature of the geological factors involved, however, makes predictive modeling a difficult task. The proposed research is based on the use of the K-Nearest Neighbor (KNN) algorithm to predict the risk of rockbursts with the use of microseismic monitoring data. Several key features like the ratio of total maximum principal stress to uniaxial compressive strength, energy capacity of support system, excavation span, geology factor, Richter magnitude of seismic event, distance between rockburst location and microseismic event, and rock density were applied as input parameters to extract critical rockburst precursor activities. In the test stage, the proposed KNN model recorded an accuracy of 75.50%, a precision of 0.913, a recall value of 0.509, and F1 Score of 0.576. The model is reliable with a significant performance indicating its efficacy in practice. The KNN model showed better classification results as compared to recently available models in literature and provided better generalization and interpretability. The model exhibited high prediction in classified low-risk incidents and had strong indicative capabilities towards high-risk situations, attributed to being a useful tool in rockburst hazard measurement. Full article

The use of wireless sensor networks (WSNs) enables multidimensional and high-precision forest environment monitoring around the clock. However, the limited energy supply of sensor nodes using solely batteries is insufficient to support long-term data collection. Furthermore, since the complex terrain, dense vegetation, and variable weather in forests present unique challenges, relying on a single energy source is insufficient to ensure a stable energy supply for sensor nodes. Combining multiple energy sources is a promising way which has not been well studied. In this paper, to effectively utilize multiple energy sources, we propose a novel dynamic clustering routing protocol which considers the inherent diversity and intermittency of energy sources of the WSN in the forest. First, to address the inconsistency in residual energy caused by uneven energy harvesting among sensor nodes, a cluster head selection weight function is developed, and a dynamic weight-based cluster head election algorithm is proposed. This mechanism effectively prevents low-energy nodes from being selected as cluster heads, thereby maximizing the utilization of harvested energy. Second, a Q-learning-based adaptive hybrid transmission scheme is introduced, integrating both single-hop and multi-hop communication. The scheme dynamically optimizes intra-cluster transmission paths based on the current network state, reducing energy consumption during data transmission. The simulation results show that the proposed routing algorithm significantly outperforms existing methods in total network energy consumption, network lifetime, and energy balance. These advantages make it particularly suitable for forest environments characterized by strong fluctuations in harvested energy. In summary, this work provides an energy-efficient and adaptive routing solution suitable for forest environments with fluctuating energy availability. Full article

District heating networks (DHNs) are a key technology in the transition toward sustainable heat supply, increasingly integrating renewable sources and thermal energy storage. High-temperature aquifer thermal energy storage (HT-ATES) can enhance DHN efficiency by shifting heat production over time, potentially reducing both costs and greenhouse gas emissions. However, most life cycle assessments (LCAs) remain static, rely on average data, and neglect temporal dispatch dynamics and marginal substitution among heat sources for environmental evaluation. This study introduces a dynamic life cycle inventory framework that explicitly links HT-ATES-operation scheduling in DHNs with marginal life cycle data. The framework expands system boundaries to capture time-varying changes in heat composition, combines a district heating merit-order representation (distinguishing must-run and flexible capacities) with linear programming to determine least-cost dispatch, and translates marginally displaced technologies into environmental and economic consequences. Foreground inputs are derived from an existing third-generation DHN (heat demand, generation assets, efficiencies) and publicly available energy carrier cost data and are linked to consequential background inventory datasets (ecoinvent). The framework is demonstrated for one year of operation for an HT-ATES concept with 50 GWh of injected heat. Hourly resolved results identify the marginally displaced technologies and indicate annual reductions of 5.86 kt CO₂e alongside cost savings of EUR 1.09 M. A comparison of alternative operation schedules shows strong sensitivity of both economic and environmental performance to operational strategy. Overall, the proposed framework provides a replicable and adaptable basis for consequential assessment of HT-ATES operation in DHNs and supports strategic decision-making on seasonal thermal storage deployment in low-carbon heat systems. Full article

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been proposed, their performance is highly dependent on the availability of large training datasets. As a result, these methods generally achieve satisfactory accuracy only when sufficient training samples are available. To address this limitation, this study proposes a novel hybrid strategy that combines a parameter-optimized signal decomposition algorithm with an enhanced neural network architecture, aiming to improve RUL prediction reliability under small-sample conditions. Specifically, we develop a lithium-ion battery capacity prediction method that integrates the Triangle Topology Aggregation Optimizer (TTAO), Variational Mode Decomposition (VMD), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, the TTAO algorithm is used to optimize the number of modes and the quadratic penalty factor in VMD, enabling the decomposition of battery capacity data into multiple intrinsic mode functions (IMFs) while minimizing the impact of phenomena such as capacity regeneration. Key features highly correlated with battery life are then extracted as inputs for prediction. Subsequently, a BiLSTM network is employed to capture subtle variations in the capacity degradation process and to predict capacity based on the decomposed sequences. The prediction results are effectively integrated, and comprehensive experiments are conducted on the NASA and CALCE lithium-ion battery aging datasets. The results show that the proposed TTAO-VMD-BiLSTM model exhibits a small number of parameters, low memory consumption, high prediction accuracy, and fast convergence. The root mean square error (RMSE) does not exceed 0.8%, and the maximum mean absolute error (MAE) is less than 0.5%. Full article

The European Union’s energy transition is based on three fundamental pillars, the realisation of which is intended to achieve climate neutrality by 2050. These pillars comprise the decarbonization of the economy, the development of renewable energy sources (RES), and the improvement of energy efficiency. The prevailing decarbonization trend involves a systematic reduction in the use of fossil fuels across the economy and their replacement with energy derived from low-emission and renewable sources. These objectives pose a significant challenge, particularly for countries such as Poland, where electricity generation remains predominantly reliant on hard coal and lignite. In recent years, a substantial reduction in CO₂ emissions has been observed in the energy sector, primarily due to the increasing share of renewables in the electricity generation mix. The main energy companies, most of which are majority-owned by the State Treasury, have developed specific strategies to meet these targets. This article analyses the strategic documents of domestic energy companies together with other publicly available sources. Based on these documents, projections have been developed regarding the decommissioning of individual generating units in public power plants and combined heat and power facilities fuelled by hard coal and lignite. Scenario-based analyses were then conducted, drawing on these projections and assumptions, to assess the potential scale of CO₂ emission reductions from the domestic energy sector through to 2050. Full article

Voltage monitoring circuits are a fundamental block in energy-harvesting-powered applications, as typically the system operation has to be enabled only after a certain supply voltage is reached after a cold start or intermediate voltage levels have to be detected during start-up. The voltage values of interest vary depending on the specific system; hence, a versatile voltage monitoring circuit scheme that can be easily adapted for the desired voltage is particularly appealing. Furthermore, in energy-harvesting-powered applications, special care must be paid to power consumption minimization, in order to ensure self-sustainability of the system, and to area occupation, thus enabling a small form factor and low cost. To address these requirements, this paper proposes a novel, highly versatile voltage-monitoring circuit for energy-harvesting-powered applications that minimizes power consumption and area occupation. Indeed, the proposed voltage monitor implementation, relying on cascaded PMOS-based and NMOS-based voltage detectors, can be easily adapted to any desired voltage level, also achieving high voltage levels to be detected by adding (multiple) diode-connected transistors in the first stage while maintaining the voltage monitor output rail-to-rail and avoiding static power consumption from the cascaded digital gates. The proposed solution, targeting a 800 mV voltage level to be detected, was designed in a 180 nm CMOS triple-well technology and extensively validated through simulations in Cadence Virtuoso. Furthermore, it was bench-marked with an implementation in the same process based on the standard voltage monitor scheme (including the necessary cascaded logic gates for achieving a rail-to-rail output) available in literature, showcasing a reduction up to about 1700× in power consumption and 3.87× in area occupation, considering a preliminary area estimation, when triple-well devices are employed, whereas, when relying only on standard devices, although no significat area benefit is obtained, a reduction of up to about 400× in power consumption is achieved. Full article

Background: Reserve Officer Training Corps (ROTC) Cadets undergo biannual Field Training Exercises (FTX) that impose substantial physiological demands, necessitating adequate nutritional intake to support performance and recovery. Methods: Energy Expenditure (EE) measured by actigraphy and self-reported nutritional intake (NI) of ROTC Cadets during a Fall FTX were obtained and compared to Military Dietary Reference Intake (MDRI) guidelines. Energy balance and nutrient adequacy were assessed using paired sample t-tests. Results: Cadets demonstrated significant caloric deficits, consuming fewer kilocalories than both their active metabolic rate (t = −12.07, df = 42, p < 0.001) and Low Energy Availability thresholds (t = 6.47, df = 57.54, p < 0.001). Macronutrient analysis revealed widespread deficiencies. Neither male nor female cadets met minimum carbohydrate gram recommendations. Protein intake in grams was significantly below MDRI guidelines for 94% of males (t = −10.03, p < 0.001) and 90% of females (t = −4.62, p = 0.001). Fat intake was generally adequate for all cadets, with 94% of males (t = 6.50, p < 0.001) and 90% of females (t = 4.19, p = 0.002) meeting or exceeding recommended fat intake. Conclusions: These findings underscore the prevalence of under-fueling during FTX and highlight the need for improved nutritional strategies to mitigate energy deficits and support cadet performance and health. Full article

Conventional alligator farming, characterized by reliance on chilled fish meat, faces significant challenges, including risks of bacterial contamination and nutritional imbalances. These issues heighten increasing disease susceptibility and threaten industry sustainability, underscoring the critical need for developing nutrient-dense, low-pathogenicity compound feeds. This study conducted a comparative analysis of newborn Siamese crocodiles fed either chilled fish meat or compound feed formulation. Intestinal microbial samples from both cohorts underwent 16S rRNA gene high-throughput sequencing to evaluate differences in microbial composition, diversity, and predicted functionality. The compound feed, specifically formulated for this investigation, possessed the following nutritional composition: crude protein 52.42%; digestible crude protein/digestible energy 16 mg/kcal; crude fat 12.31%; ash 17.42%; crude fiber 0.45%; starch 7.69%; digestible energy 3450 kcal/kg; lysine 3.66%; threonine 1.92%; methionine 1.27%; arginine 3.07%; total essential amino acids 22.97%; calcium 2.51%; total phosphorus 1.8%; available phosphorus 0.98%. Bioinformatics analysis revealed that the compound feed group exhibited numerically higher richness and alpha diversity indices within the intestinal microbiota compared to the chilled fish group. The microbial communities in both groups were dominated by the phyla Proteobacteria, Bacteroidetes, Fusobacteriota, and Firmicutes, collectively representing over 50% of the relative abundance. Functional prediction indicated that the compound feed group possessed the highest relative abundance in metabolic pathways associated with cofactor and vitamin metabolism, carbohydrate metabolism, amino acid metabolism, terpenoid and polyketide metabolism, lipid metabolism, and replication and repair. In contrast, the chilled fish group exhibited significant functional alterations in glycan biosynthesis and metabolism, translation, nucleotide metabolism, transcription, and biosynthesis of other secondary metabolites. Histomorphological analysis demonstrated greater villus height and crypt depth in the compound diet group compared to chilled fish group, although no significant differences were observed in crypt depth or the villus-to-crypt depth ratio. Collectively, these findings indicate that the compound feed enhances intestinal microbial diversity and optimizes its functional structure. Furthermore, while no statistically significant difference in small intestinal villus height was detected, the results suggest a potential positive influence on intestinal development. This investigation provides a scientific foundation for compound feed development, supporting sustainable breeding practices for Siamese crocodiles. Full article