Search Results (436)

Tuning structural heterogeneity in metallic glasses is key to improving their mechanical performance. Here we examine how carbon micro-alloying modulates the relaxation dynamics and creep of Fe-based amorphous ribbons. Increasing carbon content lowers the crystallization temperature, amplifies β-relaxation, and reduces hardness, consistent with enhanced atomic mobility. Nanoindentation creep, fitted with a stretched-exponential model, shows a decreasing exponent with carbon addition, indicating broader relaxation–time distributions and stronger dynamic heterogeneity. Nanoscale force-mapping further reveals a larger fraction of liquid-like regions and pronounced viscoelastic heterogeneity in carbon-rich samples. These changes facilitate the activation of shear-transformation zones and promote room-temperature creep flow. Together, the results establish a direct link between structural heterogeneity, relaxation processes, and mechanical response, providing guidance for the design of ductile metallic glasses. Full article

In this paper, Distribution of Relaxation Time (DRT) analysis is presented as a powerful tool for understanding the aging mechanisms in lithium-ion batteries, with a focus on its application to estimating the State of Health (SOH). A novel parameter, the characteristic relaxation time, derived from DRT analysis, is introduced to enhance SOH estimation. By analyzing the ratio of the central relaxation time (τ) between the charge transfer and diffusion peaks, the battery status can be determined without the need for historical data. Experimental data from lithium-ion batteries, including 18650 cells and LR2032 coin cells, were examined until the end of their life. Nyquist and DRT plots across various frequency ranges revealed consistent aging trends, particularly in the charge transfer and diffusion processes. These processes appeared as shifting and merging peaks in the DRT plots, signifying progressive degradation. A polynomial equation fitted to the τ ratio graph achieved a high accuracy (Adj. R² = 0.9994), enabling reliable battery lifespan prediction. Validation with a Samsung Galaxy S9+ battery demonstrated that the method could estimate its remaining life, predicting a total lifespan of approximately 2100 cycles (compared to 1000 cycles already completed). These results confirm that SOH estimation is feasible without prior data and highlight the potential of DRT analysis for accurate and quantitative prediction of battery longevity. Full article

This study investigated the physiological effects of seat vibration during prolonged sitting in a simulated autonomous driving environment. Eleven healthy participants (3 young adults and 8 older adults) viewed a 120-min highway driving video under two conditions: rhythmic seat vibration (2 Hz, mimicking natural respiration) and no vibration. Physiological and behavioral metrics—including Psychomotor Vigilance Task (PVT), seat pressure distribution, heart rate variability (HRV), body acceleration, and skin temperature—were assessed across three phases. Results demonstrated that seat vibration significantly enhanced parasympathetic activity, as evidenced by increased HF power and decreased LF/HF ratio (p < 0.05), suggesting reduced autonomic stress. Additionally, seated posture remained more stable under vibration, with reduced asymmetry and sway, while the no-vibration condition showed time-dependent postural degradation. Interestingly, skin surface temperature was lower in the vibration condition (p < 0.001), indicating a possible thermoregulatory mechanism. In contrast, PVT performance revealed more false starts in the vibration condition, particularly among older adults, suggesting that vibration may not enhance—and could slightly impair—cognitive alertness. These findings suggest that low-frequency seat vibration can support physiological stability and postural control during prolonged sedentary conditions, such as in autonomous vehicles. However, its effects on vigilance appear limited and age-dependent. Overall, rhythmic vibration may contribute to enhancing passenger comfort and reducing fatigue-related risks, particularly in older individuals. Future work should explore adaptive vibration strategies to balance physiological relaxation and cognitive alertness in mobility environments. Full article

Gastroesophageal reflux disease (GERD) is associated with symptoms such as heartburn, resulting from gastric content reflux. Alginate-based raft-forming gel formulations represent a non-pharmacological strategy for GERD management by forming a floating gel barrier in the stomach. This study evaluated three commercial anti-reflux oral gel systems under simulated fed-state gastric conditions, using in vitro magnetic resonance relaxometry techniques. Magnetic resonance imaging (MRI) was performed in 0.01 M hydrochloric acid (HCl) to visualize gel raft formation, spatial structure, and spatial distribution of effective T₂ relaxation time. Nuclear magnetic resonance (NMR) relaxometry in 0.01 M deuterium chloride (DCl) measured T₁ and T₂ relaxation times of the protons that were initially included in the preparation to assess its molecular mobility within the gel matrix. Two formulations formed floating, coherent gels, whereas the remaining one exhibited only polymer swelling without flotation. In one case, relaxometry data revealed a solid-like component that can be detected, indicating enhanced mechanical stability. The performance of each formulation was influenced by interactions among alginate, bicarbonates, and calcium ions, which determined gel consistency and flotation behavior. MRI and NMR relaxometry in vitro provide valuable non-invasive insights into the structural and functional behavior of alginate-based gel formulations. This approach supports the rational design of advanced gel-based therapies for GERD by linking molecular composition with in situ performance. Full article

This study investigates the elemental composition of magnetic nanoparticles (MNPs) in eleven wildland–urban interface (WUI) fire ashes, including one vegetation, six structural, and four vehicle ashes, along with three fire-impacted soil samples. The WUI fire ash samples were collected following the 2020 North Complex (NC) Fire and Sonoma–Lake–Napa unit (LNU) Lightning Complex Fire in California. Efficiency of magnetic separation was confirmed via Time-Domain Nuclear Magnetic Resonance (TD-NMR); the relaxometry showed that the transverse relaxation rate R₂ decreased from 2.02 s⁻¹ before separation to 0.29 s⁻¹ after separation (ΔR₂ = −1.73 s⁻¹; −86%), due to the removal of magnetic particles. The particle number concentrations, size distributions, and elemental compositions (and ratios) of MNPs were determined using single particle-inductively coupled plasma–time-of-flight-mass spectrometry (SP-ICP-TOF-MS). The major types of nanoparticles (NPs) detected in the magnetically separated MNPs were Fe-, Ti-, Cr-, Pb-, Mn-, and Zn-bearing NPs. The iron-bearing NPs accounted for 3.2 to 83.5% of the magnetically separated MNPs, and decreased following the order vegetation ash (77.4%) > soil (63.2–69.9%) > structural (3.2–83.5%) ash. The titanium-bearing NPs accounted for 3.3 to 66.1% of the magnetically separated MNPs, and decreased following the order vehicle (14.1–66.1%) > structural (3.5–36.4%) > vegetation (3.3%) ash. The majority of the detected NPs in the fire ashes occurred in the form of multi-metal (mm) NPs, attributed to the presence of NPs as heteroaggregates and/or due to the sorption of metals on the surfaces of NPs during combustion. However, a notable fraction (3–91%) of the detected NPs occurred as single-metal (sm) NPs, particularly smFe-bearing NPs, which accounted for 48 to 91% of all the Fe-bearing particles in the magnetically separated MNPs. The elemental ratios (e.g., Al/Fe, Ti/Fe, Cr/Fe, and Zn/Fe) in the magnetically separated MNPs from structural and vehicle ashes were higher than those in the soil samples and vegetation ashes, indicating enrichment of metals in magnetically separated NPs from vehicle and structural ashes compared to vegetation ash. Overall, this study demonstrates that the MNPs generated by WUI fire ash are associated with potentially toxic elements (e.g., Cr and Zn), exacerbating the environmental and human health risks of WUI fires. This study also highlights the need for further research into the properties, environmental fate, transport, and interactions of MNPs with biological systems during and following WUI fires. Full article

This study introduced a new low-temperature storage method that applies an additional lower strength static electric field (SEF) under the condition of a static magnetic field (SMF) to investigate the impact of magneto–electric coupling on the supercooling degree and quality of beef. The results showed that 7 mT-1 kV performs the best (−5.8 °C); the ability of SMF to maintain supercooling is less affected by SEF. Moreover, on the 15th day, magneto–electric coupling (7 mT-1 kV) outperformed SMF (7 mT) alone by reducing beef pH by 0.27, decreasing total viable counts (TVC) by 0.87 log CFU/g, maintaining TVB-N at only 12.5 mg/100 g, and limiting oxidative change, calpain activity, and shear force variation. Magnetic resonance imaging revealed that magneto–electric coupling treatment stabilized the T₂ relaxation time in meat samples, effectively inhibiting immobilized water migration and promoting more uniform moisture distribution, highlighting its application potential as a low temperature preservation method. Full article

Background: Information on childhood body composition is critical to understanding children’s growth, development, and long-term health outcomes. BMI metrics, however, have several limitations for assessing and understanding changes in BMI. Therefore, understanding the influence of various body composition factors (covariates) that are linked to, and influence, BMI over time in growing children is important. This study aims to determine sex differences in longitudinal changes in covariates of BMI from 6 to 13 years. Methods: Participants (N = 332, 160 boys 172 girls) from North West Province in South Africa were assessed longitudinally at the following three time-points during their primary years of schooling: Grade 1 (6–7 years); Grade 4 (9–10 years); and Grade 7 (12–13 years). Covariates included: stature (cm); body weight (kg); sub-scapular-, calf-, and triceps skinfolds (mm); body fat percentage (%), relaxed forearm, waist and mid-upper arm circumferences; percentage fat weight; and percentage muscle weight. Correlational analysis and multiple stepwise regression analysis in SPSS analyzed the significance of the contributions of the different covariates to changes in BMI from 6 to 12 years. Results: Different covariates influence BMI in boys and girls at different ages and the covariates also change over time in boys and girls. Weight had the strongest influence on the BMI of boys and girls, although the prediction value decreased over time. Weight and stature were consistently the strongest BMI predictors across all ages in boys. In girls, a broader range of variables influences BMI from a younger age, where slightly higher BMI correlations with fat-related variables emerged, and the percentage of fat weight distribution was a strong influential factor. These findings indicate a more in-depth analysis of BMI to determine sound intervention strategies. Full article

A macroscopic model for nonhomogeneous traffic is introduced that incorporates relaxation time and lateral time headway, and accounts for driver reaction time. Driver reaction is based on models of real-world nonhomogeneous traffic flow in Pakistan and Iran. A lateral time headway model is obtained using lane change data to characterize traffic during lane changes. The performance of the proposed model is compared with the well-known Payne–Whitham (PW) model on a 3000 m circular road using the FORCE numerical scheme. The results show that the initial multi-cluster density distribution evolves more realistically and accurately with the proposed model. Thus, it can be used to aid in traffic congestion mitigation for on-ramps and off-ramps. Full article

Human mitochondrial DNA (mtDNA) is a relevant marker in evolutionary and population genetics, including ancient DNA (aDNA) research, due to inherent characteristics. However, aDNA is prone to damage and sequencing artefacts, potentially confounding evolutionary interpretations. To assess evolutionary patterns in ancient and modern mtDNA, we built a phylogeny comprising 63,965 modern and 3757 ancient public mitogenomes, classified mutations by genomic region and functional effect, and analysed distribution, frequency, and predicted pathogenicity of private and pre-terminal mutations, investigating purifying selection. We compared mutation class ratios (non-synonymous, rRNA, tRNA, nonsense vs. synonymous) across ancient and modern terminal branches and pre-terminal nodes. The predicted pathogenicity of non-synonymous mutations was evaluated across major European haplogroups using three tools. Ancient variants exhibited higher ratios of potentially deleterious mutations and significantly elevated pathogenicity scores compared to modern and pre-terminal branches, highlighting a mutation load likely inflated by damage-related artefacts. Remarkably, nonsense mutations—largely incompatible with life—were over 70 times more frequent in aDNA. The correlation between mutation ratios and predicted deleteriousness across haplogroups suggests a pattern incompatible with biological persistence or relaxed selection alone. These findings highlight the importance of rigorous quality control for ancient data in evolutionary inference, molecular clock calibration, and pathogenic variant identification. Full article

In the present paper, impedance spectroscopy was employed to study the corrosion and anodic oxidation of stainless steel (AISI 316L at 280 °C/9 MPa) in contact with the boron-free primary coolant of a small modular reactor at two levels of KOH concentration. Analysis of impedance spectra with a distribution of relaxation times revealed contributions from the oxide layer and its interface with the coolant. Glow-Discharge Optical Emission Spectroscopy (GDOES) was used to estimate the thickness and elemental composition of the formed oxides. A quantitative interpretation of the impedance data using the Mixed-Conduction Model allowed us to estimate the kinetic and transport parameters of oxide growth and dissolution, as well as iron dissolution through oxide. The film thicknesses following exposure agreed with ex-situ analyses. The obtained corrosion and release rates were used for comparison with laboratory and industrial data in nominal pressurized water reactor primary coolants. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

The Pennes bioheat equation is the most widely used model for describing heat transfer in living tissue during thermal exposure. It is derived from the classical Fourier law of heat conduction and assumes energy exchange between blood vessels and surrounding tissues. The literature presents various numerical methods for solving the bioheat equation, with exact solutions developed for different boundary conditions and geometries. However, analytical models based on this framework are rarely reported. This study aims to develop an analytical three-dimensional model using MATHEMATICA software, with subsequent mathematical validation performed through COMSOL simulations, to characterize heat transfer in biological tissues induced by laser irradiation under various therapeutic conditions. The objective is to refine the conventional bioheat equation by introducing three key improvements: (a) incorporating a non-Fourier framework for the Pennes equation, thereby accounting for the relaxation time in thermal response; (b) integrating Dirac functions and the telegraph equation into the bioheat model to simulate localized point heating of diseased tissue; and (c) deriving a closed-form analytical solution for the Pennes equation in both its classical (Fourier-based) and improved (non-Fourier-based) formulations. This paper investigates the nuanced relationship between the relaxation time parameter in the telegraph equation and the thermal relaxation time employed in the bioheat transfer equation. Considering all these aspects, the optimal thermal relaxation time determined for these simulations was 1.16 s, while the investigated thermal exposure time ranged from 0.01 s to 120 s. This study introduces a generalized version of the model, providing a more realistic representation of heat exchange between biological tissue and blood flow by accounting for non-uniform temperature distribution. It is important to note that a reasonable agreement was observed between the two modeling approaches: analytical (MATHEMATICA) and numerical (COMSOL) simulations. As a result, this research paves the way for advancements in laser-based medical treatments and thermal therapies, ultimately contributing to more optimized therapeutic outcomes. Full article

The in situ emulsification synergistic self-profile control system has wide application prospects for efficient development on offshore oil reservoirs. During water flooding in Bohai heavy oil reservoirs, random emulsification occurs with superimposed Jamin effects. Effectively utilizing this phenomenon can enhance the efficient development of offshore oilfields. This study addresses the challenges hindering water flooding development in offshore oilfields by investigating the emulsification mechanism and key influencing factors based on oil–water emulsion characteristics, thereby proposing a novel in situ emulsification flooding method. Based on a fundamental analysis of oil–water properties, key factors affecting emulsion stability were examined. Core flooding experiments clarified the impact of spontaneous oil–water emulsification on water flooding recovery. Two-dimensional T1–T2 NMR spectroscopy was employed to detect pure fluid components, innovating the method for distinguishing oil–water distribution during flooding and revealing the characteristics of in situ emulsification interactions. The results indicate that emulsions formed between crude oil and formation water under varying rheometer rotational speeds (500–2500 r/min), water cuts (30–80%), and emulsification temperatures (40–85 °C) are all water-in-oil (W/O) type. Emulsion viscosity exhibits a positive correlation with shear rate, with droplet sizes primarily ranging between 2 and 7 μm and a viscosity amplification factor up to 25.8. Emulsion stability deteriorates with increasing water cut and temperature. Prolonged shearing initially increases viscosity until stabilization. In low-permeability cores, spontaneous oil–water emulsification occurs, yielding a recovery factor of only 30%. For medium- and high-permeability cores (water cuts of 80% and 50%, respectively), recovery factors increased by 9.7% and 12%. The in situ generation of micron-scale emulsions in porous media achieved a recovery factor of approximately 50%, demonstrating significantly enhanced oil recovery (EOR) potential. During emulsification flooding, the system emulsifies oil at pore walls, intensifying water–wall interactions and stripping wall-adhered oil, leading to increased T2 signal intensity and reduced relaxation time. Oil–wall interactions and collision frequencies are lower than those of water, which appears in high-relaxation regions (T1/T2 > 5). The two-dimensional NMR spectrum clearly distinguishes oil and water distributions. Full article

The titanium matrix composites (TMCs) fabricated via Directed Energy Deposition (DED) effectively overcome the issue of coarse columnar grains typically observed in additively manufactured titanium alloys. In this study, systematic annealing heat treatments were applied to in situ (TiB + TiC)/Ti-6Al-4V composites to refine the microstructure and tailor mechanical properties. The results reveal that the plate-like α phase in the as-deposited composites gradually transforms into an equiaxed morphology with increasing annealing temperature and holding time. Notably, when the annealing temperature exceeds 1000 °C, significant coarsening of the TiC phase is observed, while the TiB phase remains morphologically stable. Annealing promotes decomposition of acicular martensite and stress relaxation, leading to a reduction in hardness compared to the as-deposited state. However, the reticulated distribution of the TiB and TiC reinforcement phases contributes to enhanced tensile performance. Specifically, the as-deposited composite achieves a tensile strength of 1109 MPa in the XOY direction, representing a 21.6% improvement over the as-cast counterpart, while maintaining a ductility of 2.47%. These findings demonstrate that post-deposition annealing is an effective strategy to regulate microstructure and achieve a desirable balance between strength and ductility in DED-fabricated titanium matrix composites. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article