Search Results (870)

This study explores the self-healing properties of polyurethane nanocomposites enhanced by multiple hydrogen bonds from ureido-pyrimidinone and the incorporation of 1–3 wt.% graphene nanoparticles, based on polyol α,ω-dihydroxy[oligo(butylene-ethylene adipate)]diol, which, according to our knowledge, has not been previously used in such systems. These new materials were synthesized via a two-step process and characterized by their thermal, mechanical, chemical, and self-healing properties. The mechanical analysis revealed that all nanocomposites exhibited high self-healing efficiencies (88–91%). The PU containing 2% graphene stands out as it exhibits the highest initial mechanical strength of ~5 MPa compared to approximately 2MP for a pristine PU while maintaining excellent self-healing efficiency (88%). A cut on the PU nanocomposite with 2% graphene can be completely healed after being heated at 80 °C for 1 h, which shows that it has a fast recovery time. Moreover, 3D printing was also successfully used to assess their processability and its effect on self-healing behavior. Three-dimensional printing did not negatively affect the material regeneration properties; thus, the material can be used in a variety of applications as expected in terms of dimensions and geometry. Full article

With their superior switching speed, GaN high-electron-mobility transistors (HEMTs) enable high power density, reduce energy losses, and increase power efficiency in a wide range of applications, such as power electronics, due to their high breakdown voltage. GaN-HEMT devices are subject to long-term reliability due to the self-heating effect and lattice mismatch between the SiC substrate and the GaN. Depletion-mode GaN HEMTs are utilized for radio frequency applications, and this work investigates three wide-bandgap (WBG) GaN HEMT fixed-frequency oscillators with output buffers. The first GaN-on-SiC HEMT oscillator consists of an HEMT amplifier with an LC feedback network. With the supply voltage of 0.8 V, the single-ended GaN oscillator can generate a signal at 8.85 GHz, and it also supplies output power of 2.4 dBm with a buffer supply of 3.0 V. At 1 MHz frequency offset from the carrier, the phase noise is −124.8 dBc/Hz, and the figure of merit (FOM) of the oscillator is −199.8 dBc/Hz. After the previous study, the hot-carrier stressed RF performance of the GaN oscillator is studied, and the oscillator was subject to a drain supply of 8 V for a stressing step time equal to 30 min and measured at the supply voltage of 0.8 V after the step operation for performance benchmark. Stress study indicates the power oscillator with buffer is a good structure for a reliable structure by operating the oscillator core at low supply and the buffer at high supply. The second balanced oscillator can generate a differential signal. The feedback filter consists of a left-handed transmission-line LC network by cascading three unit cells. At a 1 MHz frequency offset from the carrier of 3.818 GHz, the phase noise is −131.73 dBc/Hz, and the FOM of the 2nd oscillator is −188.4 dBc/Hz. High supply voltage operation shows phase noise degradation. The third GaN cross-coupled VCO uses 8-shaped inductors. The VCO uses a pair of drain inductors to improve the Q-factor of the LC tank, and it uses 8-shaped inductors for magnetic coupling noise suppression. At the VCO-core supply of 1.3 V and high buffer supply, the FOM at 6.397 GHz is −190.09 dBc/Hz. This work enhances the design techniques for reliable GaN HEMT oscillators and knowledge to design high-performance circuits. Full article

Accurate heat load forecasting is essential for the efficiency of District Heating Systems (DHS). Still, it is challenged by the need to model long-term temporal dependencies and nonlinear relationships with weather and other factors. This study proposes a hybrid deep learning framework combining an attention-enhanced Neural Basis Expansion Analysis for Time Series (N-BEATS) model and eXtreme Gradient Boosting (XGBoost). The N-BEATS component, with a multi-head self-attention mechanism, captures temporal dynamics, while XGBoost models non-linear impacts of external variables. Predictions are integrated using an optimized weighted averaging strategy. Evaluated on a dataset from 103 heating units, the model outperformed 13 baselines, achieving an MSE of 0.4131, MAE of 0.3732, RMSE of 0.6427, and R² of 0.9664. This corresponds to a reduction of 32.6% in MSE, 32.0% in MAE, and 17.9% in RMSE, and an improvement of 5.1% in R² over the best baseline. Ablation studies and statistical tests confirmed the effectiveness of the attention mechanism and ensemble strategy. This model provides an efficient solution for DHS load forecasting, facilitating optimized energy dispatch and enhancing system performance. Full article

Biomass torrefaction must be self-sustaining and continuous to be commercially viable, eliminating dependence on additional fuels while achieving industrial-scale production. This study presents a predictive model of a full-scale continuous biomass torrefaction process that explicitly incorporates the radiation absorption properties of torrefaction gas, with a focus on water vapor. Previous research, primarily based on lab-scale batch processes, has not adequately addressed scale-up challenges or the dynamic evolution of torrefaction gas. Industrial insights from Perpetual Next confirm that water vapor significantly impacts reactor performance by absorbing heat and reducing radiative flux to the biomass. Simulations show that neglecting water vapor absorption in reactor design can lead to throughput deviations of 10–20%, affecting process stability and efficiency. Industrial-scale validation demonstrates that the model accurately predicts this effect, ensuring realistic energy demand and throughput expectations. By explicitly incorporating water vapor absorption into the radiation balance, the model provides a validated framework for optimizing reactor design and process scale-up. It demonstrates that failing to consider this effect can lead to operational instability and deviations from the intended torrefaction severity, ultimately affecting industrial-scale performance and self-sustaining operation. Full article

Background and Objective: Intervertebral disc degeneration (IVDD) is a leading cause of lower back pain with limited regenerative treatments. Among emerging regenerative approaches, growth factor-based therapies, such as recombinant human transforming growth factor-beta (Rh-TGF-β), have shown potential for disc regeneration but are hindered by rapid degradation and uncontrolled release by direct administration. Additionally, mechanical stress elevates heat shock protein 90 (HSP-90), impairing cell function and extracellular matrix (ECM) production. This study aimed to investigate a dual self-cross-linked alginate di-aldehyde (ADA) hydrogel system for the sustained delivery of Rh-TGF-β and epigallocatechin gallate (EGCG) to enhance protein stability, regulate release, and promote disc regeneration by targeting both regenerative and stress-response pathways. Methods: ELISA and UV-Vis spectrophotometry assessed Rh-TGF-β and EGCG release profiles. A rat tail IVDD model was established with an Ilizarov-type external fixator for loading, followed by hydrogel treatment with or without bioactive agents. Disc height, tissue structure, and protein expression were evaluated via radiography, histological staining, immunohistochemistry, and Western blotting. Results: The hydrogel demonstrated a biphasic release profile with 100% Rh-TGF-β released over 60 days and complete EGCG release achieved within 15 days. Treated groups showed improved disc height, structural integrity, and proteoglycan retention revealed by histological analysis and elevated HSP-90 expression by immunohistochemistry. In contrast, Western blot analysis confirmed that EGCG effectively downregulated HSP-90 expression, suggesting a reduction in mechanical stress-induced degeneration. Conclusions: ADA hydrogel effectively delivers therapeutic agents, offering a promising strategy for IVDD treatment. Full article

Diamond is an attractive substrate candidate for GaN high-electron-mobility transistors (HEMT) to enhance heat dissipation due to its exceptional thermal conductivity. However, the thermal boundary resistance (TBR) at the GaN–diamond interface poses a significant bottleneck to heat transport, exacerbating self-heating and limiting device performance. In this work, TCAD simulations were employed to systematically investigate the effects of thermal boundary layer (TBL) thickness (d_TBL) and thermal conductivity (κ_TBL) on the electrothermal behavior of GaN-on-diamond HEMTs. Results show that increasing the TBL thickness (5–20 nm) or decreasing its thermal conductivity (0.1–1.0 W/(m·K)) leads to elevated hotspot temperatures and degraded electron mobility, resulting in a notable deterioration of I–V characteristics. The nonlinear dependence of device performance on κ_TBL is attributed to Fourier’s law, where heat flux is inversely proportional to thermal resistance. Furthermore, the co-analysis of substrate thermal conductivity and interfacial quality reveals that interface TBR has a more dominant impact on device behavior than substrate conductivity. Remarkably, devices with low thermal conductivity substrates and optimized interfaces can outperform those with high-conductivity substrates but poor interfacial conditions. These findings underscore the critical importance of interface engineering in thermal management of GaN–diamond HEMTs and provide a theoretical foundation for future work on phonon transport and defect-controlled thermal interfaces. Full article

As a densely populated area, college student dormitories consume a large amount of electricity every year to heat the domestic hot water used by students. Applying solar energy to hot water systems can effectively alleviate this situation. This paper first conducts a simulation of the hot water load and the calculation of the available area of the solar roof in a dormitory building of a certain university. Then, different solar-coupled air source heat pump systems were designed, and simulation models of the two systems were established. The thermal performance parameters and solar energy utilization of the two systems were discussed, and the energy efficiency, economy, and environmental protection of the two systems were analyzed. The results show that after coupling with the solar collector, the system operation time is shortened by 26.2%, the annual performance coefficient is 3.4, which is 0.8 higher than that of the original system, and the annual heating energy consumption is reduced by 24.4%. In contrast, the annual energy self-sufficiency rate of the photovoltaic coupled with air source heat pump system is 94.6%, achieving nearly zero energy consumption for heating. Full article

This study systematically investigates the effects of trap concentration on self-heating and terahertz (THz) responses in GaN HEMTs using Sentaurus TCAD. Traps, inherently unavoidable in semiconductors, can be strategically introduced to engineer specific energy levels that establish competitive dynamics between the electron momentum relaxation time and the carrier lifetime. A simulation-based exploration of this mechanism provides significant scientific value for enhancing device performance through self-heating mitigation and THz response optimization. An AlGaN/GaN heterojunction HEMT model was established, with trap concentrations ranging from 0 to $5\times {10}^{17} {\mathrm{c}\mathrm{m}}^{-3}$. The analysis reveals that traps significantly enhance channel current (achieving 3× gain at $1\times 10^{17} {\mathrm{c}\mathrm{m}}^{-3}$) via new energy levels that prolong carrier lifetime. However, elevated trap concentrations (>$1\times {10}^{16} {\mathrm{c}\mathrm{m}}^{-3}$) exacerbate self-heating-induced current collapse, reducing the min-to-max current ratio to 0.9158. In THz response characterization, devices exhibit a distinct DC component (${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$) under non-resonant detection ($\mathsf{\omega }\mathsf{\tau }\ll 1$). At a trap concentration of $1\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ peaks at $0.12 \mathrm{V}$ when ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-7.8 \mathrm{V}$. Compared to trap-free devices, a maximum response attenuation of 64.89% occurs at ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-4.9 \mathrm{V}$. Furthermore, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ demonstrates non-monotonic behavior with concentration, showing local maxima at $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ and $7\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, attributed to plasma wave damping and temperature-gradient-induced electric field variations. This research establishes trap engineering guidelines for GaN HEMTs: a concentration of $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ optimally enhances conductivity while minimizing adverse impacts on both self-heating and the THz response, making it particularly suitable for high-sensitivity terahertz detectors. Full article

In this study, we introduce a high-resolution, high-speed thermal imaging technique using Raman spectroscopy to simultaneously measure the temperature of a substrate and a channel. By modifying the Raman spectrometer, we achieved a measurement speed faster than commercial spectrometers. This system demonstrated a sub-micron spatial resolution and the ability to measure the temperatures of the Si substrate and GaN channel simultaneously. During high-current operation, we observed significant self-heating in the GaN channel, with hotspots 100 °C higher than the surroundings, while the Si substrate showed an even temperature distribution. The ability to detect hotspots can help secure the reliability of devices through early failure analysis and can also be used for improvement research to reduce hotspots. These findings highlight the potential of this technique for early defect inspection and device improvement research. This study provides a novel and effective method for measuring the sub-micron resolution temperature distribution in devices, which can be applied to various semiconductor devices, including SiC-based power devices. Full article

Due to the limitations of the sowing machine performance and rice seed germination rates, missing seedlings inevitably occur after rice is sown in large fields. This phenomenon has a direct impact on the rice yield. In the field environment, the existing methods for detecting missing seedlings based on unmanned aerial vehicle (UAV) remote sensing images often have unsatisfactory effects. Therefore, to enable the fast and accurate detection of missing rice seedlings and facilitate subsequent reseeding, this study proposes a UAV remote-sensing-based method for detecting missing rice seedlings in large fields. The proposed method uses an improved PCERT-DETR model to detect rice seedlings and missing seedlings in UAV remote sensing images of large fields. The experimental results show that PCERT-DETR achieves an optimal performance on the self-constructed dataset, with an mean average precision (mAP) of 81.2%, precision (P) of 82.8%, recall (R) of 78.3%, and F₁-score (F₁) of 80.5%. The model’s parameter count is only 21.4 M and its FLOPs reach 66.6 G, meeting real-time detection requirements. Compared to the baseline network models, PCERT-DETR improves the P, R, F₁, and mAP by 15.0, 1.2, 8.5, and 6.8 percentage points, respectively. Furthermore, the performance evaluation experiments were carried out through ablation experiments, comparative detection model experiments and heat map visualization analysis, indicating that the model has a strong detection performance on the test set. The results confirm that the proposed model can accurately detect the number of missing rice seedlings. This study provides accurate information on the number of missing seedlings for subsequent reseeding operations, thus contributing to the improvement of precision farming practices. Full article

Postoperative adhesions are a prevalent complication following abdominal surgeries, often leading to significant clinical challenges. This study introduces an innovative solution utilizing a polyethylene glycol (PEG)-based triblock copolymer to form an injectable, self-healing hydrogel aimed at preventing these adhesions. The hydrogel, formulated with temperature-responsive and self-healing properties through the incorporation of poly (N-isopropyl acrylamide) (PNIPAM) and anion–pi interactions, was synthesized using reversible addition–fragmentation chain transfer (RAFT) polymerization. The hydrogel’s physical properties, biocompatibility, hemostatic effect, and anti-adhesive capabilities were rigorously tested through in vitro and in vivo experiments involving rat models. It demonstrated excellent biocompatibility, effective tissue adhesion, and robust hemostatic properties. Most notably, it exhibited significant anti-adhesive effects in a rat abdominal wall–cecum model, reducing adhesion formation effectively compared to controls. The PEG-based injectable hydrogel presents a promising approach for postoperative adhesion prevention. Its ability to gel in situ triggered by body heat, coupled with its self-healing properties, provides a substantial advantage in clinical settings, indicating its potential utility as a novel anti-adhesion material. Full article

Polyamide 6,6 (PA6,6) fabrics are widely used in textiles due to their high mechanical strength and chemical stability. However, their inherent flammability and melting behavior under fire pose significant safety challenges. In this study, a dual-layer flame-retardant system was developed by integrating atomic layer deposition (ALD) of ZnO with a phosphorus–silane-based flame retardant (DOPO-ETES). ALD allowed precise control of ZnO layer thickness (50, 84, and 199 nm), ensuring uniform coating. Thermal analysis (TGA) and microscale combustion calorimetry (MCC) revealed that ZnO altered the degradation pathway of PA6,6 through catalytic effects, promoting char formation and reducing heat release. The combination of ZnO and DOPO-ETES resulted in further reductions in heat release rates. However, direct flame tests showed that self-extinguishing behavior was not achieved, emphasizing the limitations related to the melting of PA6,6. TG-IR and cone calorimetry confirmed that ZnO coatings suppressed the release of smoke-related volatiles and incomplete combustion products. These findings highlight the potential of combining metal-based catalytic flame retardants like ZnO with phosphorus-based coatings to improve flame retardancy while addressing the specific challenges of polyamide textiles. This approach may also be adapted to other fabric types and integrated with additional flame retardants, broadening its relevance for textile applications. Full article

Due to their self-adaptability, low friction, low loss, and high-speed stability, bump foil aerodynamic journal bearings are widely used in high-speed rotating equipment such as turbomachinery and flywheel energy storage. In the process of high-speed operation, the heat generated leads to changes in air parameters (such as viscosity, density, etc.), thus affecting the overall performance of air bearings. In this paper, combined with the compressible Reynolds equation, a fluid–solid coupling model was established to analyze the steady-state characteristics and key influencing factors of bearings. Through the energy equation, the air viscosity–temperature effect was considered, and different boundary conditions were set. The internal temperature distribution of the air bearing and the influence of the temperature on the bearing characteristics were systematically analyzed. It was found that the bearing capacity increased when the temperature was considered. In a certain range, with the increase in ambient temperature, the increase in bearing capacity is reduced. This paper provides a theoretical design basis for the design of high-stability bearings and promotes the design of next-generation air bearings with higher speed, lower loss, and stronger adaptability, which has very important theoretical and engineering significance. Full article

Fluids that exhibit self-rewetting properties, such as aqueous long-chain alcohol solutions, display a unique quadratic relationship between surface tension and temperature and are marked by a positive gradient. This characteristic leads to distinctive patterns of thermocapillary convection and associated interfacial dynamics, setting self-rewetting fluids apart from normal fluids (NFs). The potential to improve heat transfer using self-rewetting fluids (SRFs) is garnering interest for use in various technologies, including low-gravity conditions and microfluidic systems. Our research aims to shed light on the contrasting behaviors of SRFs in comparison to NFs regarding interfacial transport phenomena. This study focuses on the thermocapillary convection in SRF layers with a deformable interface enclosed inside a closed container modeled as a square cavity, which is subject to nonuniform heating, represented using a Gaussian profile for the heat flux variation on one of its sides, in the absence of gravity. To achieve this, we have enhanced a central-moment-based lattice Boltzmann method (LBM) utilizing three distribution functions for tracking interfaces, computing two-fluid motions with temperature-dependent surface tension and energy transport, respectively. Through numerical simulations, the impacts of several characteristic parameters, including the viscosity and thermal conductivity ratios, as well as the surface tension–temperature sensitivity parameters, on the distribution and magnitude of the thermocapillary-driven motion are examined. In contrast to that in NFs, the counter-rotating pair of vortices generated in the SRF layers, due to the surface tension gradient at the interface, is found to be directed toward the SRF layers’ hotter zones. Significant interfacial deformations are observed, especially when there are contrasts in the viscosities of the SRF layers. The thermocapillary convection is found to be enhanced if the bottom SRF layer has a higher thermal conductivity or viscosity than that of the top layer or when distributed, rather than localized, heating is applied. Furthermore, the higher the magnitude of the effect of the dimensionless quadratic surface tension sensitivity coefficient on the temperature, or of the effect of the imposed heat flux, the greater the peak interfacial velocity current generated due to the Marangoni stresses. In addition, an examination of the Nusselt number profiles reveals significant redistribution of the heat transfer rates in the SRF layers due to concomitant nonlinear thermocapillary effects. Full article

In this study, we present a comprehensive numerical investigation of alternating-current electrothermal (ACET) pumping strategies tailored for energy-efficient microfluidic applications. Using coupled electrokinetic and thermal multiphysics simulations in narrow microchannels, we systematically explore the effects of channel geometry, electrode asymmetry and external heating on flow performance and thermal management. A rigorous mesh convergence study confirms velocity deviations below ±0.006 µm/s across the entire operating envelope, ensuring reliable prediction of ACET-driven flows. We demonstrate that increasing channel height from 100 µm to 500 µm reduces peak temperatures by up to 79 K at a constant 2 W heat input, highlighting the critical role of channel dimensions in convective heat dissipation. Introducing a localized external heat source beneath asymmetric electrode pairs enhances convective circulations, while doubling the fluid’s electrical conductivity yields a ~29% increase in net flow rate. From these results, we derive practical design guidelines—combining asymmetric electrode layouts, tailored channel heights, and external heat bias—to realize self-regulating, low-power microfluidic pumps. Such devices hold significant promises for on-chip semiconductor cooling, lab-on-a-chip assays and real-time thermal control in high-performance microelectronic and analytical systems. Full article