Thermal Features Research Articles

Perovskite nanostructure anchored on reduced graphene (rGO) nanosheets as an efficient electrocatalyst for oxygen evolution reaction (OER)

Energy is the basic need of this modern era and water splitting is the best known renewable source of energy. Designing an effective, high performing and durable electrocatalyst has become a serious endeavour to enhance water-splitting efficiency. For this purpose, a hydrothermal method was used to produce a non-toxic, environmentally friendly and cost-effective rGO/ZnSnO3, a composite material to improve water oxidation. For this, various analytical approaches were used to investigate the structural, textural, compositional, thermal and morphological features of the reported materials. The electrochemical properties of the rGO/ZnSnO3 nanohybrid was also analyzed by a three-electrode setup in a 1 M potassium hydroxide (KOH) and the resulting nanohybrid exhibited exceptionally small overpotential (212 mV) at an ideal current density (j) of 10 mA/cm2. The larger electrochemical surface area (ECSA) value 506.25 cm2, minimum charge transfer resistance (Rct) 0.18 Ω and remarkable stability around 20 h revealed that the produced composite material exhibited excellent potential for OER. Further examination revealed a significantly low Tafel value (37 mV/dec), suggesting that the rGO/ZnSnO3 nanohybrid possesses improved electrocatalytic efficiency and fast reaction kinetics. The nanohybrid mentioned above (rGO/ZnSnO3) shows significant potential for electrolysis of water and other electrochemical processes attributed to its considerable surface area, various active sites, exceptional stability, rapid electron mobility, low resistivity and favourable electrical conductivity.

Why Observations at Mid-infrared Wavelengths Partially Mitigate M Dwarf Star Host Stellar Activity Contamination in Exoplanet Transmission Spectroscopy

Exoplanet atmosphere transmission spectroscopy for planets orbiting M dwarf stars faces significant challenges due to contamination from stellar magnetic features, i.e., spots and faculae. These features make the stellar surface inhomogeneous and introduce wavelength-dependent signals in the transmission spectrum that complicate its analysis. We identify and explain why using observations at infrared wavelengths greater than a few microns partially mitigates stellar contamination. At these wavelengths the intensity sensitivity to temperature weakens, with two significant consequences. First, the contribution of spots and faculae has a diminished effect because their flux contrast to the quiet-star regions lessens. Second, the star’s spectral features compress in magnitude, an outcome of spectral features being shaped by the star’s photospheric vertical temperature gradient. Both factors are due to the Planck function moving from exponential to linear in temperature toward mid-infrared (mid-IR) wavelengths (the “Rayleigh–Jeans tail”). In contrast to stellar spectra, the depth of the transmission spectroscopy features does not fundamentally vary with wavelength as it is primarily determined by the planet’s atmospheric scale height. The magnitude of reduction in stellar contamination is a factor of a few to several at mid-IR versus near-IR wavelengths, but whether or not this is enough to bypass stellar contamination ultimately depends on the spot coverage area. Nonetheless, the flattening of thermal emission spectral features at IR wavelengths is universal.

Life prediction and risk assessment on the aircraft cable based on thermal damage features

Aiming at the operational risk caused by melting of aircraft cable insulation layer, an aircraft cable safety analysis method based on thermal damage characteristics is proposed to accurately predict the life and dynamically assess the risk. Based on the mechanism of cable thermal damage, two types of damage characteristics including the thermal damage influence factor and the radial melting rate are defined, which characterizes the influence and degree of cable thermal damage. Use thermal damage influence factor to indicate that cable thermal damage accelerates cable failure, and a method of cable life prediction is proposed. Moreover, a dynamic risk assessment model for aircraft cable is constructed from two perspectives of thermal damage degree and failure probability. The results show that the cable life prediction method proposed owes higher accuracy and better applicability in different operating areas. The proposed risk assessment model is effective and the results are consistent with the experimental.

Comparative experimental and theoretical study of thermal and rheological features of Cu/PAM/DW and water/PAM/SiO2 individual polynanofluids

Comparative experimental and theoretical study of thermal and rheological features of Cu/PAM/DW and water/PAM/SiO2 individual polynanofluids

Manufacturing trials of PFCs with low thermal conductivity features for limiters

In future demonstration fusion power plants, plasma facing components will face high steady state and ultra-high transient heat loads from the plasma. Introducing low thermal conductivity features to the component can reduce the peak temperatures seen by the coolant pipe during transient loads, but at the trade-off of a loss of steady-state performance. Producing low-conductivity tungsten by additive methods has been investigated elsewhere, so this trial investigates production through subtractive means i.e., conventional milling. This method preserves the original temperature limits and majority of the microstructure and is much higher Technology Readiness Level (TRL) so requires less development to reach series manufacture. In this trial, diamond drilling produced holes in monoblocks down to 0.6 mm and ligaments down to 0.7 mm, showing that this method is capable of producing small features in a controlled way. With the geometric design of lattice used in this trial, these samples achieved thermal conductivity reductions of 15% to 40%. One monoblock assembly was tested up to 1100 °C for 500 cycles in the Heating by Induction to Verify Extremes (HIVE) High Heat Flux test rig and showed no signs of ligament cracking or thermal degradation. Electromagnetic modelling has been validated against the experimental results, so future designs can be accurately modelled ahead of testing in HIVE.

Study of relativistic charged particle production using multi-source thermal model and emission feature of slowest target protons

Study of relativistic charged particle production using multi-source thermal model and emission feature of slowest target protons

Particle motion, shadows and thermodynamics of regular black hole in pure gravity

This paper investigates the thermodynamic behaviors and optical properties of regular black holes within pure gravity theories, notable for their absence of singularities. Using both analytical and numerical methods, we analyze the thermodynamics to identify stability and phase transitions, and explore the particle dynamics behavior of regular black holes. We analyze the particle dynamics properties by investigating the effective potential, energy density, angular momentum, ISCO, photon, and shadow radius. We analyze the thermal features by exploring the mass, temperature, specific heat, and Gibbs free energy. Our findings demonstrate that regular black holes exhibit distinct behaviors from traditional singular black holes, providing valuable insights into black hole physics and general relativity. This study suggests new directions for future research in fundamental gravitational theories.

A nanoscale photonic thermal transistor for sub-second heat flow switching

Control of heat flow is critical for thermal logic devices and thermal management and has been explored theoretically. However, experimental progress on active control of heat flow has been limited. Here, we describe a nanoscale radiative thermal transistor that comprises of a hot source and a cold drain (both are ~250 nm-thick silicon nitride membranes), which are analogous to the source and drain electrodes of a transistor. The source and drain are in close proximity to a vanadium oxide (VOx)-based planar gate electrode, whose dielectric properties can be adjusted by changing its temperature. We demonstrate that when the gate is located close ( < ~1 µm) to the source-drain device and undergoes a metal-insulator transition, the radiative heat transfer between the source and drain can be changed by a factor of three. More importantly, our nanomembrane-based thermal transistor features fast switching times ( ~ 500 ms as opposed to minutes for past three-terminal thermal transistors) due to its small thermal mass. Our experiments are supported by detailed calculations that highlight the mechanism of thermal modulation. We anticipate that the advances reported here will open new opportunities for designing thermal circuits or thermal logic devices for advanced thermal management.

Correlation filter based single object tracking: A review

In recent years, correlation filter-based (CF) tracking algorithms have gained momentum in the field of visual tracking. CF tracking algorithms have achieved compelling performance by addressing its limitations such as boundary effect and filter corruption during various tracking and target appearance variations. Many researchers have attempted to provide better efficiency and tracking results by extracting handcrafted and deep features either from vision sensors or specialized sensors in the CF tracking framework. Handcrafted features are integrated with deep features, thermal features, and depth features to prevent tracking failures during dense tracking challenges. To provide a detailed understanding of CF-based trackers, the tracking algorithms are categorized either as kernelized CF trackers or fusion-based CF trackers in this work. This is the first review of its kind, which categorizes various correlation-based tracking algorithms based on the key methodologies and the exploited features in the appearance model. Under each category, salient features, detailed overview, and current advancement are discussed and tabulated to provide future research directions. In addition, the performance of CF-based state-of-the-art is experimentally evaluated on multiple datasets namely, OTB100 and VOT2017 under tough tracking situations.

Artificial neural network modelling and experimental investigations of malachite green adsorption on novel carboxymethyl cellulose/β-cyclodextrin/nickel cobaltite composite

This study presents a novel polymer nanocomposite based on carboxymethyl cellulose and β-cyclodextrin crosslinked with succinic acid (CMC-SA-β-CD) containing nickel cobaltite (NCO) nano-reinforcement. Various analytical techniques have been employed to investigate the structural, thermal, and morphological features of the resulting nanocomposite. The CMC-SA-β-CD/NCO nanocomposite has been utilized as an adsorbent for the removal of bisphenol-A (BPA, R% <40 %), malachite green (MG, R% > 75 %)), and Congo red (CR, no adsorption) from the synthetic wastewater. The study systematically explored the impact of various parameters on the adsorption process, and the interactions between MG and CMC-SA-β-CD/NCO were discussed. The adsorption data were fitted to different models to elucidate the kinetics and thermodynamics of the adsorption process. An artificial neural network (ANN) analysis was employed to train the experimental dataset for predicting adsorption outcomes. Despite a low BET surface area (0.798 m2 g−1), CMC-SA-β-CD/NCO was found to exhibit high MG adsorption capacity. CMC-SA-β-CD/NCO exhibited better MG adsorption performance at pH 5.5, 40 mg L−1 MG dye concentration, 170 min equilibrium time, 20 mg CMC-SA-β-CD/NCO dose with more than 90 % removal efficiency. Moreover, the thermodynamic studies suggest that the adsorption of MG was exothermic with ΔH° value −9.93 ± 0.76 kJ mol−1. The isotherm studies revealed that the Langmuir model was the best model to describe the adsorption of MG on CMC-SA-β-CD/NCO indicating monolayer surface coverage with Langmuir adsorption capacity of 182 ± 4 mg g−1. The energy of adsorption (11.4 ± 0.8 kJ mol−1) indicated chemisorption of MG on the composite surface. The kinetics studies revealed that the pseudo-first-order model best described the adsorption kinetics with qe = 86.7 ± 2.9 mg g−1. A good removal efficiency (>70 %) was retained after five regeneration reuse cycles. The ANN-trained data showed good linearity between predicted and actual data for the adsorption capacity (R-value>0.99), indicating the reliability of the prediction model. The developed nanocomposite, composed predominantly of biodegradable material, is facile to synthesize and exhibited excellent monolayer adsorption of MG providing a new sustainable adsorbent for selective MG removal.

Rheological behavior predictions of non-Newtonian nanofluids via correlations and artificial neural network for thermal applications

Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe3O4-Ag/EG, MWCNT-alumina/water-EG, Fe3O4-Ag/water-EG, and MWCNT-SiO2/EG-water, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R2), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R2 values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe3O4-Ag/water-EG resulted in an R2 value as low as 0.72, to determine the nanofluids’ effective viscosity.

Studying the structural, thermal, mechanical, and dielectric features of PVA/iron oxide@SiO2 polymer nanocomposites for electrical applications

Fe3O4@SiO2 (FS) nanocomposite was synthesized through the co-precipitation method and utilized as a filler material in the polyvinyl alcohol (PVA) matrix to obtain PVA/iron oxide@silica Fe3O4@SiO2 (PFS) films. This study presents the synthesis and characterization of flexible polymer nanocomposites comprising polyvinyl alcohol (PVA) matrices embedded with iron oxide @ silica. Through the analysis of the composite, we observed a significant enhancement in the thermal stability of the nanocomposites with increasing Fe3O4@SiO2 content. X-ray photoelectron spectroscopy (XPS) spectra have been used to provide more information about the chemical state and electronic structure of the polymer film. Dielectric spectroscopy revealed notable improvements in electrical properties, including increased real electric modulus and permittivity. Mechanical investigation showed an improvement in the tensile strength, meanwhile, Young's modulus has improved from 341.2 to 1789.8 MPa, indicating that (PVA) and FS successfully complexed and interacted strongly. These findings underscore the potential of these nanocomposites for high-performance applications in energy storage devices and other fields requiring robust dielectric materials.

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

Radiation shielding, optical, structural, morphological, and thermal features for tellurite glass ceramics

A new series of tellurite glass-ceramic was prepared for potential utilization in the radiation shielding field. The thermal features of parent glasses were assessed to choose the best heat treatment technique. The morphological and structural properties of glass ceramics were investigated by scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR). The UV absorption was utilized to calculate the optical features of glass ceramics. The shielding properties were computed based on the Phy-X software. The transformation of glasses to glass ceramics enhanced the density of all glass ceramics. XRD results exhibited an orthorhombic BaTeO3 crystalline phase for the TMSB0 and TMSB5 samples. It is proved that the monoclinic molybdenum tellurium oxide (Mo5TeO16) crystalline phase for TMSB10, TMSB15, and TMSB20 samples. Also, the change in crystalline phase resulted from adding MoO3 instead of TeO2. SEM results affirmed the role of MoO3 in improving the structure of glass ceramics. The thermal stability and weight loss of TMSB20 samples are higher than other glasses. Also, the adding MoO3 led to a slight enhancement in the radiation shielding properties of the prepared samples. Data indicate that adding MoO3 to the glasses enhanced the glass-ceramic samples' thermal, structural, and radiation protection properties.

Characterization of microstructure, texture, grain structure, and thermal properties of Al/Ni/Al multilayered composites fabricated through cross-accumulative roll bonding

This research investigates microstructure, texture, and thermal properties of Al/Ni/Al composites fabricated by cross-accumulative roll bonding (CARB) process. According to the microstructural results, ultra-fine grain structure was observed in severely deformed layers. More grain refinement occurred when the content of Ni increased. In addition, shear texture components became stronger by increasing the CARB passes and Ni content even though the Copper-based texture components of {112}<111> existed in all deformed samples. Also, despite an improvement in thermal features of deformed aluminum and nickel layers versus temperature rise, the TD, and TC decreased as the number of passes increased. However, by increasing the rolling passes, the measured thermal capacity values modestly increased while the thermal expansion values first decreased and then increased. Regarding composites, all thermal features decreased with an increase in Ni volume fraction. Due to the restricting effect of harder reinforcement layers, the measured thermal expansion declined from 17.01 × 10−6 1/k in Al/0.2 Vf Ni to 15.4 × 10−6 1/k in Al/0.8 Vf Ni.

Data-driven estimation of battery state-of-health with formation features

Accurately estimating the state-of-health (SOH) of a battery is crucial for ensuring battery safe and efficient operation. The lifetime of lithium-ion batteries (LIBs) starts from their manufacture, and the performance of LIBs in the service period is highly related to the formation conditions in the factory. Here, we develop a deep transfer ensemble learning framework with two constructive layers to estimate battery SOH. The primary approach involves a combination of base models, a convolutional neural network to combine electrical features with spatial relationships of thermal and mechanical features from formation to subsequent cycles, and long short-term memory to extract temporal dependencies during cycling. Gaussian process regression (GPR) then handles SOH prediction based on this integrated model. The validation results demonstrate highly accurate capacity estimation, with a lowest root-mean-square error (RMSE) of 1.662% and a mean RMSE of 2.512%. Characterization on retired cells reveals the correlation between embedded formation features and their impact on the structural, morphological, and valence states evolution of electrode material, enabling reliable prediction with the corresponding interplay mechanism. Our work highlights the value of deep learning with comprehensive analysis through the relevant features, and provides guidance for optimizing battery management.

Effect of trapezoidal louvered winglets on increased heat transfer and exergy in tubular heat exchanger

The effect of inserting a trapezoidal louvered winglet tape (TLWT) into a uniformly heat-fluxed tube on its thermal effectiveness was studied experimentally. The exergy and entropy analyses for turbulent flows, as well as frictional loss and thermal features, were highlighted as key aspects of the experimental finding for the Reynolds number which measured between about 4700 and 30,000. Because fixing baffles to the curved shape of tube wall presented a challenge, the baffles were consequently positioned on double surfaces of a flat tape. Six values of the louver angle (θ1 = 0°, 25°, 30°, 45°, 60°, and 90°) and three values of the relative pitch of winglet (PR = 1.0, 1.5, and 2.0) were employed in the arrangement of TLWTs, with the V-apex oriented upstream (V-up). Each of these had only a fixed height (BR = 0.25) and angle of attack (α = 30°). The winglets were utilized to induce streamwise vortices which can hinder the boundary layer formation, while the louvered openings were adopted to lessen pressure drop without significantly impacting the primary vortices. The experiment results disclosed that the smallest θ1 and PR produced the largest relative friction factor (fR) and NuR, which were about 13.57 and 4.04 times higher, while PR = 1 and θ1 = 45° provide the greatest TEF of about 2.27. The greatest exergy efficiency (ηEx) resulting from the TLWT was reached at θ1 = 0°, but the generation of entropy (S˙g′) dropped with lowering θ1 and Re. A further examination, however, showed that the best scenario with α = 60° and staggered arrays is more desirable since it yields the largest TEF of 2.45 at θ1 = 45° and PR = 1. For the range of parameters under consideration, the Nu and f correlations were additionally established.

Novel complex based on [Co(SCN)4] and piperazine derivate: Synthesis, characterization, theoretical study, thermal features, optical studies and electronic investigations

The crystal structure of the synthesized compound, [Co(SCN)4] (C10H14FN2)2, comprises separate complexes in which the Co2+ cations are fourfold coordinated by four N- bonded thiocyanate motifs to induce distorted CoN4 tetrahedra and two 1-(2-fluorophenyl) piperazinium cations. The entities are further linked by N H···S H-bonds in a 3-D network generated along the c-axis direction establishing chains. Interestingly, single crystal diffraction has occurred, in addition to the X-ray powder diffraction to punctuate the purity of the synthesized complex, adding to the optical behavior study using FT-IR. Intermolecular interaction and stabilization of dimer complexes in the crystal packing are explored using Hirshfeld surface analysis to highlight the role of molecular interaction in the crystal packing. The di-electrical investigations show interesting electrical behavior of the tilted compound. The thermal stability analyses occurred and it appeared that the dissociation of the entities starts at about 500 K. In addition, density functional theory (DFT) calculations were used to study the electronic properties and the optical properties of the studied system.

Millefeuille-inspired biomass alternate multilayer composite, for excellent absorption-dominated, broadband EMI shielding and joule heating

The development of biomass electromagnetic interference (EMI) shielding materials with low cost, low reflection(R-value), and high shielding efficiency is promising but also challenging. Inspired by the alternate structure of a millefeuille, we propose an alternating assembly approach for conductive and magnetic layers. Employing sustainable bamboo fibers (BF) and biodegradable polylactic acid (PLA) as raw matrix, the magnetic and conductive layers were fabricated by compositing copper-plated BF (Cu@BF) and iron-plated BF (Fe@BF) with PLA, respectively. By alternately stacking magnetic and conductive layers and followed by hot pressing, the high EMI SE and low R-value biomass multilayer composite with “multi-(absorption-reflection-reabsorption)” structures were obtained. The performance of different alternating layers (3/5/7/9 layers) was studied, and a linear correlation between layer number, SE, and R-value was established. The results demonstrate that increasing the alternate layer number could readily tune the SE in the X-band from 45.02 dB (3-layer) to 80.2 dB (9-layer) and reduce Rmin from 0.40 to 0.25. Furthermore, the 9-layer composite exhibits approximately 75 dB SE in 1–18 GHz, simultaneously realizing high efficiency, low reflectivity, and broadband shielding. Notably, its excellent conductivity also provides reliable Joule heating performance. The shielding and thermal features of the composite highlight its potential in construction and smart housing heating applications.

Enhanced Dichroism of Polarizing Composite Films by Embedding Sepiolite.

The present study investigated the use of fibrous nanoparticle-filled polarizing films. Sepiolites were selected as nanoparticles and incorporated into a PVA-iodine complex. The resulting nanocomposite film was elongated and dyed with iodine. Various properties of the nanocomposite polarizing films, including thermal, morphological, optical, and rheological features, were experimentally analyzed. The study demonstrated that an increase in sepiolite loading was accompanied by an enhancement in both the mechanical and viscoelastic properties. In particular, the incorporation of nanoparticles led to an increase in birefringence and the degree of polarization. This was attributed to the alteration of the internal structure of the PVA film caused by the embedded sepiolites. The thermal analysis showed that the composite film with a higher content of sepiolites exhibited higher crystallinity and a higher melting temperature.