Traditional Phase Research Articles

A numerical modeling of battery thermal management system using nano-enhanced phase change material in hot climate conditions

Battery thermal management system (BTMS) is of utmost importance to ensure their safe and efficient operation. In regions with hot climates, such as Saudi Arabia, batteries are particularly susceptible to overheating, leading to performance degradation, safety risks, and potential fires. Traditional phase change materials (PCMs) have limitations in terms of slow response times and limited thermal conductivity. To overcome these challenges, nano-enhanced PCMs (NePCMs) have been developed, incorporating nanoparticles into the PCM matrix to enhance their thermal properties. This study focused on the application of NePCM, specifically RT44 and SWCNT, for BTMS of a pouch battery in hot climates. The investigation primarily centered on exploring the effects of NePCM chamber thicknesses at the top and bottom. It was expected that increasing the NePCM thickness would effectively regulate battery temperature. However, the study revealed that the thickness of the NePCM had a limited impact on battery temperature due to the dominant influence of the ambient environment. In summary, the results of this study indicate that the optimal design configuration for the NePCM chamber, in terms of thickness, consists of a bottom thickness of 10 mm and a top thickness of 15 mm.

Detection of early pulmonary emphysema by multi-contrast x-ray Talbot-Lau interferometer.

Pulmonary emphysema is a part of chronic obstructive pulmonary disease, which is an irreversible chronic respiratory disease. In order to avoid further damage to lung tissue, early diagnosis and treatment of pulmonary emphysema is essential. Early pulmonary emphysema diagnosis is difficult with conventional radiographic imaging. Recently, x-ray phase contrast imaging has proved to be an effective and promising imaging strategy for soft tissue, due to its high sensitivity and multi-contrast. The aim of this study is to diagnose pulmonary emphysema early utilizing an x-ray Talbot-Lau interferometer (TLI). We successfully established the mouse model of emphysema by porcine pancreatic elastase treatment, and then used the established x-ray TLI to perform imaging experiments on the mice with different treatment time. The traditional absorption CT and phase contrast CT were obtained simultaneously through TLI. The CT results and histopathology of mice lung in different treatment time were quantitatively analyzed. By imaging mice lungs, it can be found that phase contrast has higher sensitivity than absorption contrast in early pulmonary emphysema. The results show that the phase contrast signal could distinguish the pulmonary emphysema earlier than the conventional attenuation signal, which can be consistent with histological images. Through the quantitative analysis of pathological section and phase contrast CT, it can be found that there is a strong linear correlation. In this study, we quantitatively analyze mean linear intercept of histological sections and CT values of mice. The results show that the phase contrast signal has higher imaging sensitivity than the attenuation signal. X-ray TLI multi-contrast imaging is proved as a potential diagnostic method for early pulmonary emphysema in mice.

Generation of tunable vortex beams from a side-pumped Nd:YAG laser utilizing spot defect mirrors

High-order vortex lasers are important for high angular momentum quantum entanglement and high-resolution spatial detection. However, traditional spatial phase modulation for Gaussian beams have difficulties achieving high-order and continuously adjustable optical vortex output. In this study, a high-order vortex laser output of continuously adjustable topological charges between 1–9 was obtained by adjusting the cavity length in a side-pumped Nd:YAG oscillator with circular spot defects of different sizes etched on the surface of the cavity mirror. Its chirality was also controlled by slightly tilting the cavity mirror, and the mode changes under different cavity parameters and the factors limiting the output optical-vortex order in the side-pumping structure were analyzed. In addition, the generation of optical vortex lattice modes with multiple spatially separated OAMs was obtained based on the spot defect method by displacing the cavity mirror.

Advancing overall water splitting via phase-engineered amorphous/crystalline interface: A novel strategy to accelerate proton-coupled electron transfer

Traditional phase engineering enhances conductivity or activity by fully converting electrocatalytic materials into either a crystalline or an amorphous state, but this approach often faces limitations. Thus, a practical solution entails balancing the dynamic attributes of both phases to maximize an electrocatalyst’s functionality is urgently needed. Herein, in this work, Co/Co2C crystals have been assembled on the amorphous N, S co-doped porous carbon (NSPC) through hydrothermal and calcination processes. The stable biphase structure and amorphous/crystalline (A/C) interface enhance conductivity and intrinsic activity. Moreover, the adsorption ability of water molecules and intermediates is improved significantly attributed to the rich oxygen-containing groups, unsaturated bonds, and defect sites of NSPC, which accelerates proton-coupled electron transfer (PCET) and overall water splitting. Consequently, A/C-Co/Co2C/NSPC (Co/Co2C/NSPC with amorphous/crystalline interface) exhibits outstanding behavior for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), requiring the overpotential of 240.0 mV and 70.0 mV to achieve 10 mA cm−2. Moreover, an electrolyzer assembled by A/C-Co/Co2C/NSPC-3 (anode) and A/C-Co/Co2C/NSPC-2 (cathode) demonstrates a low drive voltage of 1.54 V during overall water splitting process. Overall, this work has pioneered the coexistence of crystalline/amorphous phases in electrocatalysts and provided new insights into phase engineering.

Manipulating the reliability of target-color information modulates value-driven attentional capture.

Previously rewarded stimuli slow response times (RTs) during visual search, despite being physically non-salient and no longer task-relevant or rewarding. Such value-driven attentional capture (VDAC) has been measured in a training-test paradigm. In the training phase, the search target is rendered in one of two colors (one predicting high reward and the other low reward). In this study, we modified this traditional training phase to include pre-cues that signaled reliable or unreliable information about the trial-to-trial color of the training phase search target. Reliable pre-cues indicated the upcoming target color with certainty, whereas unreliable pre-cues indicated the target was equally likely to be one of two distinct colors. Thus reliable and unreliable pre-cues provided certain and uncertain information, respectively, about the magnitude of the upcoming reward. We then tested for VDAC in a traditional test phase. We found that unreliably pre-cued distractors slowed RTs and drew more initial eye movements during search for the test-phase target, relative to reliably pre-cued distractors, thus providing novel evidence for an influence of information reliability on attentional capture. That said, our experimental manipulation also eliminated value-dependency (i.e., slowed RTs when a high-reward-predicting distractor was present relative to a low-reward-predicting distractor) for both kinds of distractors. Taken together, these results suggest that target-color uncertainty, rather than reward magnitude, played a critical role in modulating the allocation of value-driven attention in this study.

Phase denoising and unwrapping method for moiré fringes based on multiresolution analysis

Phase unwrapping for moiré fringes plays a crucial role in moiré tomography, and the precision of its outcomes deeply influences the detection results concerning high-temperature complex flow fields. In this paper, a phase denoising and unwrapping technique grounded in multiresolution analysis (MRA) is presented, and it is effectively applied to extract phase information from moiré fringes for propane-air flame and argon arc plasma flow fields. Firstly, the wrapped phase undergoes a wavelet packet decomposition for overall denoising. Subsequently, MRA is conducted to refine the denoising process and unwrap phase, involving layered wavelet packet decomposition and iterative unwrapping on different levels of approximate components. Following the hierarchical structure of MRA, the results from different levels are systematically combined and summarized. In order to ascertain the feasibility and applicability of this approach, a comparison is conducted between the results obtained from our proposed method and those obtained from three representative traditional methods. And then, the outcomes obtained from various unwrapping methods in scenarios involving noise and discontinuity, as well as the results between diverse wavelet basis functions, different decomposition layers and distinct iterations, are meticulously analyzed and compared. The findings indicate that our proposed method outperforms traditional phase unwrapping techniques in terms of continuity and smoothness, particularly when exposed to severe noise. Unlike the three classic methods, our proposed method maintains consistent results even in the presence of noise, thereby highlighting its advantages in terms of resilience against noise. In a word, this research serves as a crucial reference for the accurate detection of complex high-temperature flow fields by moiré tomography.

Numerical study of heat transfer characteristics of phase change materials reinforced with porous media based on hybrid mesh

To address the drawbacks of inadequate energy density as well as utilization effectiveness in solar energy utilization, phase change energy storage offers a possible option. However, the low thermal conductivity of traditional phase change materials (PCMs) restricts the extent to which solar energy utilization can be enhanced. In this paper, the impact of the metal foam (MF) on the solid–liquid interface, the flow field, the enhancement heat transfer mechanism during the paraffin wax (PX) melting process and thermal storage characteristics was analyzed. Besides, a three-dimensional mathematical model of MF-PX using the hybrid mesh technique was established and software ANSYS-FLUENT was used for simulation. The results indicated that, the hybrid mesh exhibited a reduction of 52.75 % in mesh count compared to the unstructured mesh, resulting in 45 % reduction in computational time. Moreover, the hybrid mesh also showcased improved mesh quality that the unstructured mesh was mainly distributed in the range of 0.48–0.6, while within the range of 0.84–1.0 for the hybrid mesh. More important, the maximum relative error between experimental and simulation was 8.67 %, indicating that the model can make a reasonable prediction. With the proportions of MF increased from 0.11 % to 0.43 %, both the Rayleigh number (Ra) and its amplification decreased. At 800 s, the Ra decreased from 1.76E+07 to 1.20E+07, while the amplification of the Ra was reduced from 1.88E+07 to 1.30E+07. In addition, the natural convection proportions of CMF decreased from 96.31 % to 80.65 %. These observations indicated that natural convection was the primary heat transfer mechanism, and the conduction was enhanced by the increasing proportion of MF. Finally, in terms of heat storage characteristics, the heat storage capacity and heat storage rate of the composite PCM slightly decreased. The heat storage values decreased from 19.30 kJ to 18.66 kJ. Similarly, the heat storage rates decreased from 19.86 J/s to 18.81 J/s.

Unveiling dynamics changes: Singular spectrum analysis-based method for detecting concept drift in industrial data streams

Industrial data streams frequently experience concept drifts. Current drift detection methods, focusing on prediction performance or data distribution, often neglect temporal dependencies and require prior distribution assumptions. These limitations have catalyzed the development of dynamic theory-based approaches that identify drifts by discerning variations in data dynamics. However, the majority of these exhibit high complexity in dynamics characterization and still underperform in industrial drift detection. To overcome these challenges, we propose a singular spectrum analysis-based drift detection method for industry data streams, comprising three modules: common dynamics extraction, noise-robust drift detection, and threshold adaptation. Our approach uses singular spectrum analysis (SSA) as the dynamics representation method instead of traditional high-dimensional phase space embedding and Fourier transform. SSA allows for the efficient description of principal dynamics by intelligently mixing the Fourier basis functions according to given data. The first two modules are specifically engineered to address the common dynamics and stochastic noise inherent in industrial data streams through a well-designed online common dynamics extraction algorithm and a noise-robust detection index respectively. Threshold adaptation module provides a more reasonable way of updating the drift threshold based on the extreme value theory, ensuring global consistency for parameter estimation of extreme value distribution. Our proposed method was tested on simulated examples and a real-world sintering process, exhibiting enhanced drift detection performance.

Extraction of time-frequency ridge line based on automatic peak search and curve fitting

Abstract Instantaneous rotational frequency (IRF) plays a crucial role in diagnosing bearing faults under variable speed conditions. However, accurately extracting the rotational frequency becomes challenging due to significant noise and strong harmonic interference. To address this issue, this paper proposes an approach for extracting Time-frequency ridge line through automatic peak search and curve fitting with a minimal number of parameters. The approach eliminates disturbance from harmonics and noise by locating peak coordinates within specified areas. Moreover, these peak coordinates were fitted as interpolated nodes to prevent sudden changes in the curve due to noise interference. It also enhances the accuracy of the extracted ridge. The efficacy of the proposed technique is confirmed through simulations and specially designed test bench data. Results show that IRF-estimation errors of our method fall by about 20% compared with those of any traditional method, approaching TKEO and Phase Demodulation without using too many parameters.

A complete thermo-poromechanical model for freeze-thaw modification of recycled coarse aggregate considering deterioration of each component

Freeze-thaw modification is a novel method that improves recycled aggregates by utilizing the expansion of pore water during phase transitions. However, there is still a lack of theoretical analysis regarding each component in recycled aggregates during freeze-thaw. In this paper, we introduce the parameter of pore radius to transform the traditional 2D phase diagram into a 3D phase diagram of pore water. This transformation makes the phase transition conditions of pore water more clear to understand. It is then combined with the pore size distribution of each component in recycled aggregate to calculate the ice content and freezing pressure. To achieve a more intuitive evaluation of frost susceptibility, this paper establishes a frost vulnerability index (FVI). Based on the FVI curves of different rocks, their frost resistance is categorized into three levels. Finally, according to the FVI of recycled aggregates with diverse material properties, some recommendations are provided for the engineering application of freeze-thaw modification.

Optical design of a novel near-infrared phase contrast imaging (NI-PCI) diagnostic on the HL-2A tokamak

The optical design of near-infrared phase contrast imaging (NI-PCI) diagnosis on HL-2A is introduced in this paper. This scheme benefits from the great progress of near-infrared laser technology and is a broadening of traditional phase contrast technology. This diagnostic can work as a keen tool to measure plasma wavenumber spectra by inferring string-integrated plasma density fluctuations. Design of both the front optical path which is the path before the laser transmitting into the tokamak plasma and the rear optics which is the path after the laser passing through the plasma is detailed. The 1550 nm laser is chosen as the probe beam and high-precision optical components are designed to fit the laser beam, in which a phase plate with a 194-nm-deep silver groove is the key. Compared with the conventional 10.6 m laser-based PCI system on HL-2A, NI-PCI significantly overcomes the unwanted phase scintillation effect and promotes the measurement capability of high-wavenumber turbulence with an increased maximal measurable wavenumber from 15 cm−1 to 32.6 cm−1.

Complex phase diagram and supercritical matter.

The supercritical region is often described as uniform with no definite transitions. The distinct behaviors of the matter therein, e.g., as liquidlike and gaslike, however, suggest "supercritical boundaries." Here we provide a mathematical description of these phenomena by revisiting the Yang-Lee theory and introducing a complex phase diagram, specifically a four-dimensional (4D) one with complex T and p. While the traditional 2D phase diagram with real temperature T and pressure p values (the physical plane) lacks Lee-Yang (LY) zeros beyond the critical point, preventing the occurrence of criticality, the off-plane zeros in this 4D scenario still induce critical anomalies in various physical properties. This relationship is evidenced by the correlation between the Widom line and LY edges in van der Waals, 2D Ising model, and water. The diverged supercritical boundaries manifest the high-dimensional feature of the phase diagram: e.g., when LY zeros of complex T or p are projected onto the physical plane, boundaries defined by isobaric heat capacity C_{p} or isothermal compression coefficient K_{T} emanates. These results demonstrate the incipient phase transition nature of the supercritical matter.

Decoding a novel green and effective antimicrobial agent: Glycerol monolaurate stable in nanosystem

Food microbiological control is the key to protecting food quality. However, glycerol monolaurate (GML), as a generally recognized safe antimicrobial agent, limits its antibacterial activity due to its poor water solubility. Ultrasound-assisted construction of essential oil nanosystems is an ingenious way to solve the above problems. In this study, essential oil-based nanoemulsions (NEs) were constructed by replacing traditional oil phase with tea tree essential oil (TTO) stabilized GML and enhanced antibacterial properties. The results demonstrated that uniform droplets could only be formed when the GML content in the nanosystem was below 50 mg/mL. NEs showed favorable optical properties with a turbidity below 0.1 and a whiteness index below 35, and they were Newtonian fluids (viscosity <0.005 Pa s). Besides, the nanosystem exhibited excellent stability even at extreme pH (1 or 11) and high salt ion concentration (500 mM), where droplet sizes of above system were still less than 100 nm after 15 d. Notably, GML alone cannot exert effective antibacterial effects against E. coli and S. aureus, whereas the encapsulation of GML into the nanosystem allowed it to exert antibacterial activity successfully. Banana was selected to validate the availability of NEs, and the treated groups maintained their attractive appearances and quality after 20 d. Overall, this work developed a new nanosystem to stabilize GML, allowing it to exert antimicrobial activity effectively.

Electrical Property Improvements of Multiple Dopant ZnO/Bi2O3 Varistor By Liquid Phase Method

Although ZnO Varistor is widely used in surge protection due to its unique I-V characteristics, its key influencing factors of grain size and uniformity are difficult to be guaranteed by traditional solid phase method. In this paper, the ZnO/Bi2O3 varistor was prepared by liquid phase co-deposition method and the sintering temperature also has been optimized. For samples prepared by the liquid-phase method, as the sintering temperature increases, the grain size gradually increases, the grain distribution becomes initially more uniform and later more disordered, and the crystallinity gradually increases. In comparison to solid-phase method, samples prepared by liquid-phase method exhibit smaller grain sizes and more uniform grain size distribution. Based on this, samples sintered at 1100 °C by the liquid-phase method demonstrate optimal electrical performance: the voltage gradient is 520 V/mm, the nonlinear coefficient is 44, and the leakage current is 5.8 μA/cm2. This paper has theoretical support for the study of preparation of varistor for achieving excellent electrical performance for being used in the field of surge protection.

Gaussian wavelet basis expansion based phase noise suppression method for coherent optical OFDM systems

In this paper, we developed a phase noise suppression method based on Gaussian wavelet basis expansion (GWBE) for coherent optical orthogonal frequency-division-multiplexing (CO-OFDM) systems. Compared with traditional phase noise suppression methods, the GWBE method significantly enhances the system’s robustness to laser phase noise, providing improved phase noise suppression, especially in scenarios with high linewidth. Additionally, the method demonstrates excellent performance in 32-QAM and 64-QAM. By employing the proposed GWBE method, the impact of laser phase noise on CO-OFDM can be effectively mitigated.

Smart pillar[5]arene-based PDMAEMA/PES beads for selective dye pollutants removal: design, synthesis, chemical-physical characterization, and adsorption kinetic studies.

This article reports on the synthesis of an innovative smart polymer, P5-QPDMAEMA, opportunely developed with the aim of combining the responsiveness of PDMAEMA polymer and the host-guest properties of covalently linked pillar[5]arenes. Thanks to a traditional Non-Induced Phase Separation (NIPS) process performed at various coagulation pH, the blending of P5-QPDMAEMA with polyethersulfone gave rise to the formation of functional beads for the removal of organic dyes in water. Adsorption tests are carried out on all the produced blend-based beads by employing two representative dyes, the cationic methylene blue (MB), and the anionic methyl orange (MO). In particular, the P5-QPDMAEMA based beads, prepared at acidic pH, featured the best MO removal rate (i. e., 91.3 % after 150 minutes starting from a 20 mg ⋅ L-1 solution) and a high selectivity towards the removal of the selected anionic dye. Based on the adsorption kinetics and isotherm calculations, the pseudo-first order and Freundlich models were shown to be the most suitable to describe the MO adsorption behavior, achieving a maximum adsorption capacity of 21.54 mg ⋅ g-1 . Furthermore, zwitterionic beads are obtained by a post-functionalization of the PDMAEMA and the P5-QPDMAEMA based beads, to test their removal capability towards both anionic and cationic dyes, as shown.

Effect of Low-Temperature Plasma Carburization on Fretting Wear Behavior of AISI 316L Stainless Steel

AISI 316L stainless steel has received considerable attention as a common material for key ball valve components; however, its properties cannot be improved through traditional phase transformation, and fretting wears the contact interface between valve parts. A carburized layer was prepared on the surface of AISI 316L stainless steel by using double-glow low-temperature plasma carburization technology. This study reveals the effect of double-glow low-temperature plasma carburization technology on the fretting wear mechanism of AISI 316L steel under different normal loads and displacements. The fretting wear behavior and energy dissipation of the AISI 316L steel and the carburized layer were studied on an SRV-V fretting friction and wear machine with ball–plane contact. The wear mark morphology was analyzed by using scanning electron microscopy (SEM), the phase structure of the carburized layer was characterized with X-ray diffractometry (XRD), and the wear profile and wear volume were evaluated with laser confocal microscopy. The carburized layer contains a single Sc phase, a uniform and dense structure, and a metallurgically combined matrix. After plasma carburizing, the sample exhibited a maximum surface hardness of 897 ± 18 HV0.2, which is approximately four times higher than that of the matrix (273 ± 33 HV0.2). Moreover, the surface roughness was approximately doubled. The wear depth, wear rate, and frictional dissipation energy coefficient of the carburized layer were significantly reduced by up to approximately an order of magnitude compared with the matrix, while the wear resistance and fretting wear stability of the carburized layer were significantly improved. Under different load conditions, the wear mechanism of the AISI 316L steel changed from adhesive wear and abrasive wear to adhesive wear, fatigue delamination, and abrasive wear. Meanwhile, the wear mechanism of the carburized layer changed from adhesive wear to adhesive wear and fatigue delamination, accompanied by a furrowing effect. Under variable displacement conditions, both the AISI 316L steel and carburized layer mainly exhibited adhesive wear and fatigue peeling. Oxygen elements accumulated in the wear marks of the AISI 316L steel and carburized layer, indicating oxidative wear. The fretting wear properties of the AISI 316L steel and carburized layer were determined using the coupled competition between mechanical factors and thermochemical factors. Low-temperature plasma carburization technology improved the stability of the fretting wear process and changed the fretting regime of the AISI 316L steel and could be considered as anti-wearing coatings of ball valves.

Using Diffraction Deep Neural Networks for Indirect Phase Recovery Based on Zernike Polynomials.

The phase recovery module is dedicated to acquiring phase distribution information within imaging systems, enabling the monitoring and adjustment of a system's performance. Traditional phase inversion techniques exhibit limitations, such as the speed of the sensor and complexity of the system. Therefore, we propose an indirect phase retrieval approach based on a diffraction neural network. By utilizing non-source diffraction through multiple layers of diffraction units, this approach reconstructs coefficients based on Zernike polynomials from incident beams with distorted phases, thereby indirectly synthesizing interference phases. Through network training and simulation testing, we validate the effectiveness of this approach, showcasing the trained network's capacity for single-order phase recognition and multi-order composite phase inversion. We conduct an analysis of the network's generalization and evaluate the impact of the network depth on the restoration accuracy. The test results reveal an average root mean square error of 0.086λ for phase inversion. This research provides new insights and methodologies for the development of the phase recovery component in adaptive optics systems.

An Anti-Noise-Designed Residual Phase Unwrapping Neural Network for Digital Speckle Pattern Interferometry

DSPI (Digital Speckle Pattern Interferometry) is a non-destructive optical measurement technique that obtains phase information of an object through phase unwrapping. Traditional phase unwrapping algorithms depend on the quality of the images, which demands preprocessing such as filtering and denoising. Moreover, the unwrapping time is highly influenced by the size of the images. In this study, we proposed a new deep learning-based phase unwrapping algorithm combining the residual network and U-Net network. Additionally, we incorporated an improved SSIM function as the loss function based on camera characteristics. The experimental results demonstrated that the proposed method achieved higher quality in highly noisy phase unwrapping maps compared to traditional algorithms, with SSIM values consistently above 0.98. In addition, we applied image stitching to the network to process maps of various sizes and the unwrapping time remained around 1 s even for larger images. In conclusion, our proposed network is able to achieve efficient and accurate phase unwrapping.

Quantum random number generation based on phase reconstruction.

Quantum random number generator (QRNG) utilizes the intrinsic randomness of quantum systems to generate completely unpredictable and genuine random numbers, finding wide applications across many fields. QRNGs relying on the phase noise of a laser have attracted considerable attention due to their straightforward system architecture and high random number generation rates. However, traditional phase noise QRNGs suffer from a 50% loss of quantum entropy during the randomness extraction process. In this paper, we propose a phase-reconstruction quantum random number generation scheme, in which the phase noise of a laser is reconstructed by simultaneously measuring the orthogonal quadratures of the light field using balanced detectors. This enables direct discretization of uniform phase noise, and the min-entropy can achieve a value of 1. Furthermore, our approach exhibits inherent robustness against the classical phase fluctuations of the unbalanced interferometer, eliminating the need for active compensation. Finally, we conducted experimental validation using commercial optical hybrid and balanced detectors, achieving a random number generation rate of 1.96 Gbps at a sampling rate of 200 MSa/s.