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In this study, the performance evaluation of an adsorption chiller (AD) system with three different adsorbents—silica-gel, aluminum fumarate, and FAM-Z01—was conducted to investigate the effects of adsorption isotherms and physical properties on the system’s performance. In addition, the performance evaluation of the AD system for a low inlet hot-water temperature of 60 °C was performed to estimate the performance of the system when operated by low quality waste heat or sustainable energy sources. For the simulation work, a two-bed type AD system is considered, and silica-gel, metal organic frameworks (MOFs), and ferro-aluminophosphate (FAPO, FAM-Z01) were employed as adsorbents. The simulation results were well matched with the laboratory-scale experimental results and the maximum coefficient of performance (COP) difference was 7%. The cooling capacity and COP of the AD system were investigated at different operating conditions to discuss the influences of the adsorbents on the system performance. Through this study, the excellence of the adsorbent, which has an S-shaped isotherm graph, was presented. In addition, the influences of the physical properties of the adsorbent were also discussed with reference to the system performance. Among the three different adsorbents employed in the AD system, the FAM-Z01 shows the best performance at inlet hot water temperature of 60 °C, which can be obtained from waste heat or sustainable energy, where the cooling capacity and COP were 5.13 kW and 0.47, respectively.

Recent scientific advances have made headway in addressing pertinient issues in climate change and the sustainability of our natural environment. This study makes use of a novel approach to desalination that is environment friendly, naturally sustainable and energy efficient, meaning that it is also cost efficient. Evaporation is a key phenomenon in the natural environment and used in many industrial applications including desalination. For a liquid droplet, the vapor pressure changes due to the curved liquid–vapor interface at the droplet surface. The vapor pressure at a convex surface in a pore is, therefore, higher than that at a flat surface due to the capillary effect, and this effect is enhanced as the pore radius decreases. This concept inspired us to design a novel biporous anisotropic membrane for membrane distillation (MD), which enables to desalinate water at ambient temperature and pressure by applying only a small transmembrane temperature gradient. The novel membrane is described as a super-hydrophobic nano-porous/micro-porous composite membrane. A laboratory-made membrane with specifications determined by the theoretical model was prepared for model validation and tested for desalination at different feed inlet temperatures by direct contact MD. A water vapor flux as high as 39.94 ± 8.3 L m −2  h −1 was achieved by the novel membrane at low feed temperature (25 °C, permeate temperature = 20 °C), while the commercial PTFE membrane, which is widely used in MD research, had zero flux under the same operating conditions. As well, the fluxes of the fabricated membrane were much higher than the commercial membrane at various inlet feed temperatures.

To ascertain membrane distillation (MD) as an emerging desalination technology to meet the global water challenge, development of membranes with ideal material properties is crucial. Functionalized carbon nanotubes (CNTs) were anchored to nanofibres of electrospun membranes. Covalent modification and fluorination of CNTs improved their dispersibility and interfacial interaction with the polymer membrane, resulting in well-aligned CNTs inside crystalline fibres with superhydrophobicity. Consideration for the chemical/physical properties of the CNT composite membranes and calculation of their theoretical fluxes revealed the mechanism of MD: CNTs facilitated the repulsive force for Knudsen and molecular diffusions, reduced the boundary-layer effect in viscous flow, and assisted surface diffusion, allowing for fast vapor transport with anti-wetting. This study shows that the role of CNTs and an optimal composite ratio can be used to reduce the gap between theoretical and experimental approaches to desalination.

This is a multicenter, open-label prospective, non interventional study. We wanted to evaluate the impact of fentanyl matrix on the pain and function of patients with spinal disorder-related chronic, non-malignant pain. Patients with severe non-malignant chronic low back pain may require opioid analgesics for effective pain management. A total of 1,576 patients with severe pain (numeric rating scale = 7) were evaluated for their pain intensity at the initial visit and at weeks 4 and 8 (Visits 1, 2, and 3, respectively). Disturbances in sleep, daily living and social activities, the Oswestry Disability Index (ODI), the researchers' and patients' global assessment and the patients' treatment preference were also assessed. The pain intensity score significantly decreased from 8.1 at Visit 1 to 5.4 and 4.4 at Visits 2 and 3, respectively. Sleep disturbance also significantly decreased and the extent of disturbance of daily and social activities was also significantly improved. The ODI significantly decreased from 61.9% to 45.8% and 38.2% at Visits 1, 2, and 3, respectively. Adverse events were reported by 197 (12.5%) patients and severe adverse events were reported by 12 (0.76%) patients. Overall, 76.3% of the patients and 78.4% of the investigators rated the test drug as effective. The fentanyl matrix is believed to be effective for the treatment of pain, sleep disturbance and the impact upon daily and social activities, yet physicians should pay attention to the risks of abuse and the adverse events.

A thermodynamic based equation to predict the diffusivity of nitrogen in α-ferrite was investigated in consideration of the equilibrium nitrogen concentration. The temperature-dependent jump distance calculated from the lattice parameter of ferrite was used to derive the frequency factor as a function of temperature. The calculation accuracy for nitrogen diffusivity using the proposed thermodynamic based equation was improved by comparing the calculation results using previous empirical equations based on Arrhenius type relationship with measured diffusivity of nitrogen for α-ferrite at different temperatures.

Two-dimensional van der Waals materials have demonstrated fascinating optical and electrical characteristics. However, reports on magnetic properties and spintronic applications of van der Waals materials are scarce by comparison. Here, we report anomalous Hall effect measurements on single crystalline metallic Fe 3 GeTe 2 nanoflakes with different thicknesses. These nanoflakes exhibit a single hard magnetic phase with a near square-shaped magnetic loop, large coercivity (up to 550 mT at 2 K), a Curie temperature near 200 K and strong perpendicular magnetic anisotropy. Using criticality analysis, the coupling length between van der Waals atomic layers in Fe 3 GeTe 2 is estimated to be ~5 van der Waals layers. Furthermore, the hard magnetic behaviour of Fe 3 GeTe 2 can be well described by a proposed model. The magnetic properties of Fe 3 GeTe 2 highlight its potential for integration into van der Waals magnetic heterostructures, paving the way for spintronic research and applications based on these devices. Exploring the magnetism in the van der Waals materials facilitates two dimensional spintronic devices. Here the authors demonstrate the evolution of magnetic behavior, strong perpendicular magnetic anisotropy and existence of magnetic coupling between atomic layers in Fe 3 GeTe 2 nanoflakes by varying the layer thickness.

High-entropy alloy (HEA) superconductors—a new class of functional materials—can be utilized stably under extreme conditions, such as in space environments, owing to their high mechanical hardness and excellent irradiation tolerance. However, the feasibility of practical applications of HEA superconductors has not yet been demonstrated because the critical current density ( J c ) for HEA superconductors has not yet been adequately characterized. Here, we report the fabrication of high-quality superconducting (SC) thin films of Ta–Nb–Hf–Zr–Ti HEAs via a pulsed laser deposition. The thin films exhibit a large J c of >1 MA cm −2 at 4.2 K and are therefore favorable for SC devices as well as large-scale applications. In addition, they show extremely robust superconductivity to irradiation-induced disorder controlled by the dose of Kr-ion irradiation. The superconductivity of the HEA films is more than 1000 times more resistant to displacement damage than that of other promising superconductors with technological applications, such as MgB 2 , Nb 3 Sn, Fe-based superconductors, and high- T c cuprate superconductors. These results demonstrate that HEA superconductors have considerable potential for use under extreme conditions, such as in aerospace applications, nuclear fusion reactors, and high-field SC magnets. Thin-film high-entropy alloy (HEA) superconductors have recently attracted a lot of attention, but their critical current density and potential usefulness in engineering applications has remained unclear. Here, the authors fabricate HEA films with remarkably high critical current density and resistance to radiation damage.

Generally, studies of the critical current I c are necessary if superconductors are to be of practical use, because I c sets the current limit below which there is a zero-resistance state. Here, we report a peak in the pressure dependence of the zero-field I c , I c (0), at a hidden quantum critical point (QCP), where a continuous antiferromagnetic transition temperature is suppressed by pressure toward 0 K in CeRhIn 5 and 4.4% Sn-doped CeRhIn 5 . The I c (0)s of these Ce-based compounds under pressure exhibit a universal temperature dependence, underlining that the peak in zero-field I c ( P ) is determined predominantly by critical fluctuations associated with the hidden QCP. The dc conductivity σ dc is a minimum at the QCP, showing anti-correlation with I c (0). These discoveries demonstrate that a quantum critical point hidden inside the superconducting phase in strongly correlated materials can be exposed by the zero-field I c , therefore providing a direct link between a QCP and unconventional superconductivity. Knowledge of critical current may provide important information to understand unconventional superconductivity and quantum critical behavior. Here, Jung et al. observe a peak in the pressure dependence of the zero-field critical current at a hidden quantum critical point in pure and Sn-doped heavy fermion superconductor CeRhIn 5 .

Nurr1, a transcription factor belonging to the orphan nuclear receptor, has an essential role in the generation and maintenance of dopaminergic neurons and is important in the pathogenesis of Parkinson’ disease (PD). In addition, Nurr1 has a non-neuronal function, and it is especially well known that Nurr1 has an anti-inflammatory function in the Parkinson’s disease model. However, the molecular mechanisms of Nurr1 have not been elucidated. In this study, we describe a novel mechanism of Nurr1 function. To provide new insights into the molecular mechanisms of Nurr1 in the inflammatory response, we performed Chromatin immunoprecipitation sequencing (ChIP-Seq) on LPS-induced inflammation in BV2 cells and finally identified the RasGRP1 gene as a novel target of Nurr1. Here, we show that Nurr1 directly binds to the RasGRP1 intron to regulate its expression. Moreover, we also identified that RasGRP1 regulates the Ras-Raf-MEK-ERK signaling cascade in LPS-induced inflammation signaling. Finally, we conclude that RasGRP1 is a novel regulator of Nurr1’s mediated inflammation signaling.

This work proposes a data-driven design framework for high-pressure die-cast (HPDC) aluminum alloys that integrates robust data refinement, machine learning (ML) modeling, explainability, and inverse design. A total of 1237 tensile-test records from T5-aged HPDC alloys were aggregated into a curated dataset of 382 unique composition–heat-treatment combinations. Four regression models—Ridge regression, Random Forest (RF), XGBoost (XGB), and a multilayer perceptron (MLP)—were trained to predict yield strength (YS), ultimate tensile strength (UTS), and elongation (EL). Tree-based ensemble models (XGB and RF) achieved the highest accuracy and stability, capturing nonlinear interactions inherent to industrial HPDC data. In particular, the XGB model exhibited the best predictive performance, achieving test R2 values of 0.819 for UTS and 0.936 for EL, with corresponding RMSE values of 15.23 MPa and 1.112%, respectively. Feature-importance and SHapley Additive exPlanations (SHAP) analyses identified Mn, Si, Mg, Zn, and T5 aging temperature as the most influential variables, consistent with metallurgical considerations such as microstructural stabilization and precipitation strengthening. Finally, RF-based inverse design suggested new composition–process candidates satisfying UTS > 300 MPa and EL > 8%, a region scarcely represented in the experimental dataset. These results illustrate how interpretable ML can expand the feasible design space of HPDC aluminum alloys and support composition–process optimization in industrial applications.