Search Results (18,098)

While the Ankom RF system facilitates efficient high-throughput in vitro fermentation studies, its high cost and limited flexibility constrain its broader applicability. To address these limitations, we developed and validated a low-cost, modular gas monitoring system (FerME), assembled from commercially available components. To evaluate its performance and reproducibility relative to the Ankom RF system (Ankom Technology, Macedon, NY, USA), in vitro rumen fermentation experiments were conducted under strictly controlled and identical conditions. Whole rumen contents were collected approximately 2 h post-feeding from individual mid- or late-lactation dairy cows and immediately transported to the laboratory. Each fermenter received 50 mL of processed rumen fluid, 100 mL of anaerobically prepared artificial saliva buffer, and 1.2 g of the donor cow’s diet. Bottles were sealed with the respective system’s pressure sensors, flushed with CO₂, and incubated in a 50 L water bath maintained at 39 °C. FerME (New Taipei City, Taiwan) and Ankom RF fermenters were placed side-by-side to ensure uniform thermal conditions. To assess the effect of filter bag use, an additional trial employed Ankom F57 filter bags (Ankom Technology, Macedon, NY, USA; 25 μm pore size). Trial 1 revealed no significant differences in cumulative gas production, volatile fatty acids (VFAs), NH₃-N, or pH between systems (p > 0.05). However, the use of filter bags reduced gas output and increased propionate concentrations (p < 0.05). Trial 2, which employed filter bags in both systems, confirmed comparable results, with the FerME system demonstrating improved precision (CV: 4.8% vs. 13.2%). Gas composition (CH₄ + CO₂: 76–82%) and fermentation parameters remained consistent across systems (p > 0.05). Importantly, with 12 pressure sensors, the total cost of FerME was about half that of the Ankom RF system. Collectively, these findings demonstrate that FerME is a reliable, low-cost alternative for real-time rumen fermentation monitoring and could be suitable for studies in animal nutrition, methane mitigation, and related applications. Full article

Foam cement, as a building insulation material, encounters a major problem in practical application, which is the difficulty in achieving a balance between its strength and insulation performance. To achieve multi-objective optimization of foamed cement mix design, this study first determined the optimal ranges of nano-silica aerogel (NSA), foaming agent, and polypropylene (PP) fiber dosage through single-factor experiments. Then, response surface methodology (RSM) was employed to construct a quadratic polynomial regression model, systematically investigating the influence of different NSA contents, foaming agent contents, and PP fibers contents on the thermal conductivity and compressive strength of foamed cement. Finally, the optimal mix ratio was further predicted and experimentally validated. The results demonstrate that the regression model developed using RSM exhibits high accuracy and reliability. The correlation coefficients R² of the regression models established by the response surface method are 0.9756 and 0.9684, respectively, indicating good prediction accuracy. The optimized mix ratio was determined as follows: NSA content, 9.548%; foaming agent content, 0.533%; and PP fiber content, 0.1%. Under this mix, the model predicted a thermal conductivity of 0.123 W/(m·K) and a 28-day compressive strength of 1.081 MPa. Experimental verification confirmed that the errors between predicted and measured values for all performance indicators were within 5%, demonstrating the high reliability of the predictive model. This study provides support for the practical application of foam cement as a thermal insulation material in construction projects and offers guidance for optimizing its mixture composition. Full article

LPG fuel supply systems are increasingly important for improving energy efficiency and reducing carbon emissions in the shipping industry. The primary objective of this research is to investigate the heat transfer phenomena to enhance the thermal performance of double-pipe heat exchangers (DPHEs) in LPG fuel supply systems. This study investigates the heat transfer performance of a glycol–steam double-pipe heat exchanger (DPHE) within an LPG fuel supply system under varying operating conditions. A computational model and methodology were developed and validated by comparing the numerical results with experimental data obtained from commissioning tests. Additionally, the effects of turbulence models and parametric variations were evaluated by analyzing the glycol–water mixing ratio and flow direction—both of which are critical operational parameters for DPHE systems. Numerical validation against the commissioning data showed a deviation of ±2% under parallel-flow conditions, confirming the reliability of the proposed model. With respect to the glycol–water mixing ratio and flow configuration, thermal conductance (UA) decreased by approximately 11% in parallel flow and 13% in counter flow for every 20% increase in glycol concentration. Furthermore, parallel flow exhibited approximately 0.6% higher outlet temperatures than counter flow, indicating superior heat transfer efficiency under parallel-flow conditions. Finally, the heat transfer behavior of the DPHE was further examined by considering the effects of geometric characteristics, pipe material, and fluid properties. This study offers significant contributions to the engineering design of double-pipe heat exchanger systems for LPG fuel supply applications. Full article

With the global low-carbon energy transition, accurate prediction of thermal and physical parameters of deep rock masses is critical for geothermal resource development. To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. A data augmentation strategy combining cubic spline interpolation and Gaussian noise injection (with noise intensity set to 10% of the original data feature range) was proposed, expanding the original 47 samples to 150. Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R² increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. The BPNN and SVM models also improved, with R² reaching 0.9365 and 0.8743, respectively, on the enhanced dataset. Feature importance analysis revealed porosity (with a coefficient of variation of 0.88, much higher than the longitudinal wave velocity’s 0.27) and density as key factors, with significantly higher contributions than longitudinal wave velocity. This study provides quantitative evidence for data augmentation and machine learning in predicting rock thermophysical parameters, promoting intelligent geothermal resource development. Full article

Amidst the background of accelerated global energy transition, the safety risk of lithium-ion battery energy storage systems, especially the fire hazard, has become a key bottleneck hindering their large-scale application, and there is an urgent need to build a systematic prevention and control program. This paper focuses on the fire characteristics and thermal runaway mechanism of lithium-ion battery energy storage power stations, analyzing the current situation of their risk prevention and control technology across the dimensions of monitoring and early warning technology, thermal management technology, and fire protection technology, and comparing and analyzing the characteristics of each technology from multiple angles. Building on this analysis, this paper summarizes the limitations of the existing technologies and puts forward prospective development paths, including the development of multi-parameter coupled monitoring and warning technology, integrated and intelligent thermal management technology, clean and efficient extinguishing agents, and dynamic fire suppression strategies, aiming to provide solid theoretical support and technical guidance for the precise risk prevention and control of lithium-ion battery storage power stations. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

We report the development of highly luminescent, bovine serum albumin (BSA)-stabilized gold–silver bimetallic nanoclusters (Au-AgNCs@BSA) as a novel platform for high-sensitivity, ratiometric intracellular temperature sensing. Precise and non-invasive temperature sensing at the nanoscale is crucial for applications ranging from intracellular thermogenesis monitoring to localized hyperthermia therapies. Traditional luminescent thermometric platforms often suffer from limitations such as high cytotoxicity and low photostability. Here, we synthesized Au-AgNCs@BSA via a one-pot aqueous reaction, achieving significantly enhanced photoluminescence quantum yields (PL QYs, up to 18%) and superior thermal responsiveness compared to monometallic counterparts. The dual-emissive Au-AgNCs@BSA exhibit a linear ratiometric fluorescence response to temperature fluctuations within the physiological range (20–50 °C), enabling accurate and concentration-independent thermometry in live cells. Time-resolved PL and Arrhenius analyses reveal two distinct emissive states and a high thermal activation energy (E_a = 199 meV), indicating strong temperature dependence. Silver doping increases radiative decay rates while maintaining low non-radiative losses, thus amplifying fluorescence intensity and thermal sensitivity. Owing to their small size, excellent photostability, and low cytotoxicity, these nanoclusters were applied to non-invasive intracellular temperature mapping, presenting a promising luminescent nanothermometer for real-time cellular thermogenesis monitoring and advanced bioimaging applications. Full article

Thermal stimulation represents an effective method for enhancing reservoir permeability, thereby improving geothermal energy recovery in Enhanced Geothermal Systems (EGS). The phase-field method (PFM) has been widely adopted for its proven capability in modeling the fracture behavior of brittle solids. Consequently, a coupled thermo-mechanical phase-field model (TM-PFM) was developed in COMSOL 6.2 Multiphysics to probe thermal fracturing mechanisms in reservoir rocks. The TM-PFM was validated against the analytical solutions for the temperature and stress fields under steady-state heat conduction in a thin-walled cylinder, three-point bending tests, and thermal shock tests. Subsequently, two distinct thermal fracturing modes in reservoir rock under high-temperature conditions were investigated: (i) fracture initiation driven by sharp temperature gradients during instantaneous thermal shocks, and (ii) crack propagation resulting from heterogeneous thermal expansion of constituent minerals. The proposed TM-PFM has been validated through systematic comparison between the simulation results and the corresponding experimental data, thereby demonstrating its capability to accurately simulate thermal fracturing. These findings provide mechanistic insights for optimizing geothermal energy extraction in EGS. Full article

Pulsed electric field (PEF) and high-pressure processing (HPP) are non-thermal treatments, developed to ensure preservation of food products whilst maintaining taste and valuable nutrients. In this study, we investigated their potential for the inactivation of 3 commercial exogenous pectinases (polygalacturonase, pectin transeliminase, pectin esterase) commonly used in juice processing for clarification of juices. The inactivation of these enzymes after processing is mandatory by European law. Clear apple juice was treated with both non-thermal processing methods, as well as with thermal pasteurization as the standard method. For HPP, 3 pressures (250, 450, and 600 MPa) and different holding times (from 2 to 12 min) were tested. For PEF, 3 electric field intensities (10, 13, and 15 kV/cm) and different specific energy values (from 121 to 417 kJ/kg). Standard thermal pasteurization resulted in a complete inactivation of all tested pectinases. HPP treatment only showed marginal effects on polygalacturonase and pectin transeliminase at the highest pressure and holding times, which are beyond levels used in industrial settings. For PEF, dependence upon high electric field strength and specific energy values was evident; however, here too, the effect was only moderate at the levels attainable within the scope of this study. Assuming a continued linear relationship, usable results could be achieved in an industrial setting, albeit under more extreme conditions. Full article

This review aims to discuss how heat stress affects ovarian follicles and oocytes, steroidogenesis, and embryo development in ruminants. The literature shows that quiescent primordial follicles appear to be less susceptible to heat stress, but from the primary follicle stage onwards, they begin to suffer the consequences of heat stress. These adverse effects are exacerbated when the follicles are cultured in vitro. In antral follicles, heat stress reduces granulosa cell viability and proliferation in both in vivo and in vitro models. Oocyte maturation, both nuclear and cytoplasmic, is also compromised, and embryo quality declines under elevated thermal conditions. These effects are linked to intracellular disturbances, including oxidative imbalance, mitochondrial dysfunction, and altered hormonal signaling. The differences between in vivo and in vitro responses reflect the complexity of the biological impact of heat stress and emphasize the protective role of the physiological microenvironment. A better understanding of how heat stress alters the function of ovarian follicles, oocytes, and embryos is crucial. This knowledge is critical to devise effective strategies that mitigate damage, support fertility, and improve outcomes in assisted reproduction for livestock exposed to high environmental temperatures. Full article

Kiwifruit (Actinidia spp.) is a globally important economic fruit with high nutritional value. Fruit peelability, defined as the mechanical ease of separating the peel from the fruit flesh, is a critical quality trait influencing consumer experience and market competitiveness and has emerged as a critical breeding target in fruit crop improvement programs. The present review systematically synthesized existing studies on kiwifruit peelability, and focused on its evolutionary trajectory, genotypic divergence, quantitative evaluation, possible underlying mechanisms, and artificial manipulation strategies. Kiwifruit peelability research has advanced from early exploratory studies in New Zealand (2010s) to systematic investigations in China (2020s), with milestones including the development of evaluation metrics and the identification of genetic resources. Genotypic variation exists among kiwifruit genera. Several Actinidia eriantha accessions and the novel Actinidia longicarpa cultivar ‘Guifei’ exhibit superior peelability, whereas most commercial Actinidia chinensis and Actinidia deliciosa cultivars exhibit poor peelability. Quantitative evaluation highlights the need for standardized metrics, with “skin-flesh adhesion force” and “peel toughness” proposed as robust, instrument-quantifiable indicators to minimize operational variability. Mechanistically, peelability is speculated to be governed by cell wall polysaccharide metabolism and phytohormone signaling networks. Pectin degradation and differential distribution during fruit development form critical “peeling zones”, whereas ethylene, abscisic acid, and indoleacetic acid may regulate cell wall remodeling and softening, collectively influencing skin-flesh adhesion. Owing to the scarcity of easy-to-peel kiwifruit cultivars, artificial manipulation methods, including manual peeling benchmarking, lye treatment, and thermal peeling, can be employed to further optimize kiwifruit peelability. Currently, shortcomings include incomplete genotype-phenotype characterization, limited availability of easy-peeling germplasms, and a fragmented understanding of the underlying mechanisms. Future research should focus on methodological innovation, germplasm development, and the elucidation of relevant mechanisms. Full article

The junction temperature (T_j) is a key parameter reflecting the thermal behavior of Silicon carbide (SiC) MOSFETs and is essential for condition monitoring and reliability assessment in power electronic systems. However, the limited temperature sensitivity of switching characteristics makes it difficult for traditional single temperature-sensitive electrical parameters (TSEPs) to achieve accurate estimation. To address this challenge and enable practical thermal sensing applications, this study proposes an accurate, application-oriented T_j estimation method based on multivariate linear regression (MLR) using turn-off current fall time (t_fi) and fall loss (E_fi) as complementary TSEPs. First, the feasibility of using current fall time and current fall energy loss as TSEPs is demonstrated. Then, a coupled junction temperature prediction model is developed based on multivariate linear regression using t_fi and E_fi. The proposed method is experimentally validated through comparative analysis. Experimental results demonstrate that the proposed method achieves high prediction accuracy, highlighting its effectiveness and superiority in MLR approach based on the current fall phase characteristics of SiC MOSFETs. This method offers promising prospects for enhancing the condition monitoring, reliability assessment, and intelligent sensing capabilities of power electronics systems. Full article

Background/Objectives: The rapid emergence of multidrug-resistant bacterial infections demands innovative non-antibiotic therapeutic strategies. Dual-modal photoresponse therapy integrating photodynamic (PDT) and photothermal (PTT) effects offers a promising rapid antibacterial approach, yet designing single-material systems with synergistic enhancement remains challenging. This study aims to develop uniform Cu-based metal–organic framework micrometer cubes (Cu-BN) for efficient PDT/PTT synergy. Methods: Cu-BN cubes were synthesized via a one-step hydrothermal method using Cu(NO₃)₂ and 2-amino-p-benzoic acid. The material’s dual-mode responsiveness to visible light (420 nm) and near-infrared light (808 nm) was characterized through UV–Vis spectroscopy, photothermal profiling, and reactive oxygen species (ROS) generation assays. Antibacterial efficacy against multidrug-resistant Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) was quantified via colony counting under dual-light irradiation. Results: Under synergistic 420 + 808 nm irradiation for 15 min, Cu-BN (200 μg/mL) achieved rapid eradication of multidrug-resistant E. coli (99.94%) and S. aureus (99.83%). The material reached 58.6 °C under dual-light exposure, significantly exceeding single-light performance. Photodynamic analysis confirmed a 78.7% singlet oxygen (¹O₂) conversion rate. This enhancement stems from PTT-induced membrane permeabilization accelerating ROS diffusion, while PDT-generated ROS sensitized bacteria to thermal damage. Conclusions: This integrated design enables spatiotemporal PDT/PTT synergy within a single Cu-BN system, establishing a new paradigm for rapid-acting, broad-spectrum non-antibiotic antimicrobials. The work provides critical insights for developing light-responsive biomaterials against drug-resistant infections. Full article

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article