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De novo design of macrocycles
Predicting factors associated with anxiety by patients undergoing treatment for infectious diseases using a random-forest machine learning approach
Neoantigen-targeting vaccine treats melanoma
A de novo variant of RERE was identified in a patient with neurodevelopmental disorder, enuresis and scoliosis
PDE5 inhibitor restores dendritic cell migration
Deep learning model using squeezenet and promoted ideal gas molecular motion for music genre classification from audio spectrograms
Engineering PdAu/CeO <sub>2</sub> Alloy/Oxide Interfaces for Selective Methane‐to‐Methanol Conversion with Water
Abstract The direct conversion of methane‐to‐methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO 2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd 0.3 Au 0.7 /CeO 2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (∼80%) at 500 K with a 1:1 methane‐to‐water ratio. While Pd/CeO 2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd with Au on ceria mitigates this over‐reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO 2 composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine‐tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO 2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis.
Event-triggered smart dual hormone artificial pancreas for patient-specific drug delivery
Research on train wheel point cloud registration algorithm based on key points by fusing Super-4PCS and ICP
Comparative study of advanced hydrogen liquefaction using triple cascade mixed refrigerant cycles with integrated energy exergy economic and environmental analysis
Abstract Hydrogen, as a clean energy source, is recognized as a pivotal energy carrier in the global transition to sustainable energy systems and serves as a crucial pathway for energy storage and efficient utilization within cryogenic systems. Hydrogen liquefaction is one of the most promising methods for increasing its energy density, enabling more efficient storage, transportation, and utilization in large-scale energy systems. However, substantial challenges persist, particularly regarding the high energy consumption associated with the liquefaction process. This study addresses these challenges by proposing two designs for a triple-cascade mixed refrigerant cycle aimed explicitly at reducing energy consumption for high-density hydrogen storage: 66.7 kg/m 3 at − 245 °C (Case 1) and 76 kg/m 3 at − 249 °C (Case 2). The proposed systems utilize two mixed refrigerant cycles for the precooling and cryogenic stages. In Case 1, pure nitrogen is employed as the third refrigerant in the precooling stage, whereas Case 2 incorporates a regenerative cryogenic hydrogen cycle as the third refrigerant throughout the entire system, coupled with a carbon dioxide cycle for compressor cooling. Simulations were conducted using Aspen HYSYS, with optimization through the Aspen Optimizer algorithm. The results indicate that Case 1 achieves a specific energy consumption (SEC) of 6.98 kWh/kgH₂, representing a 17.4% reduction from the baseline, while Case 2 reduces SEC to 6.19 kWh/kgH₂, a 14.5% decrease. The exergy analysis of the heat exchangers shows a 37% reduction in exergy destruction in Case 2 compared to Case 1. Additionally, Case 2 demonstrates a 5.8% reduction in capital expenditure and a 22% reduction in carbon footprint (CFP). These findings highlight the potential of the proposed triple-cascade process to enhance energy efficiency, improve both thermodynamic and economic performance, and reduce environmental impact.
Biased adrenergic receptor agonist tackles metabolic disease
Qian Zhao
Reversible Structural Oscillation Mediates Stable Oxygen Evolution Reaction
Abstract The dynamic dissolution of active species of electrocatalysts suffers severe durability issues, thus limiting practical sustainable electrochemical application despite the enormous strides in the activity. An atomistic understanding of the dynamic pattern is a fundamental prerequisite for realizing prolonged stability. Herein, modeling on NiFe LDHs, multiple operando spectroscopies revealed the structural oscillation of the local [Ni–O 2 –Fe] unit identified a strong dependence on the alternant Fe dissolution and redeposition during the oxygen evolution reaction (OER) process, thus mediating the dynamic stability. At this point, a proof‐of‐concept strategy with S, Co co‐doping was demonstrated to tune structural oscillations. In situ S leaching that alleviates the lattice mismatch suppresses Fe dissolution, while the electron‐withdrawing Co as a deposition site promotes Fe redeposition, thus achieving the reversible oscillation of local [Ni/Co–O 2 –Fe] units and dynamic stability. The implementation of the modified NiFe LDH in industrial water electrolysis equipment operated steadily over 800 h (5000‐h lifetime obtained by epitaxial method with 10% attenuation) with an energy consumption of 4.05 kWh Nm −3 H 2 @ 4000 A m −2 . The levelized cost of hydrogen of US$ 2.315 per kg H2 overmatches the European Commission's target for the coming decade (<US$ 2.5 per kg H2 ).
Computer vision based efficient segmentation and classification of multi brain tumor using computed tomography images
Kueselia aquadivae gen. nov., sp. nov., the first member of the family Isosphaeraceae isolated from subsurface percolates
Abstract Subsurface habitats, found under various geological conditions, exhibit diverse microbial communities. The vadose zone, a previously unexplored subsurface compartment, connects the surface to phreatic groundwater. Drilling into the subsurface allows access to these habitats for microbial diversity study. Due to nutrient limitation, subsurface microbiomes adapt, potentially producing biotechnologically important biomolecules. Planctomycetota, known for possessing about 20 to 45% of protein-coding genes of unknown function, may be relevant in this context. A percolate water sample from the weathered bedrock of the Hainich Critical Zone Exploratory (CZE; Thuringia, Germany) was processed to enrich planctomycetes, leading to the isolation of an uncharacterized Isosphaeraceae member, strain EP7T. Strain EP7T forms round, pink colonies, and spherical, non-motile cells that divide asymmetrically by budding. It grows between 10 and 24 °C and over a range of pH 5 to pH 10. Its genome size is 7.2 Mbp, and its DNA G + C content is 66.7%. Polyphasic characterization justifies the assignment of strain EP7T to a novel species within a novel genus. We introduce the name Kueselia aquadivae for the novel taxon with strain EP7T as the type strain of the novel species. Strain EP7T represents the first Isosphaeraceae member isolated from vadose zone percolate water.
The ConPET Mechanism Remains Veiled: Reply to “Unlocking the ConPeT Mechanism”
Abstract In this journal, a Correspondence by Ventura, Cozzi, and Ceroni reported time‐resolved absorption spectroscopy studies in the electron transfer process from cyanoarene photocatalyst 3,4,5,6‐tetrakis(diphenyl amino)phthalonitrile (4DPAPN) in the presence of tetrabutylammonium oxalate (TBAOx). This was used as a model reaction to investigate the mechanism of consecutive photoinduced electron transfer (ConPET) in our previously reported asymmetric [3+2] photocycloaddition. They proposed a new electron transfer pathway in which the electron from the excited state of the radical anion 4DPAPN* •− solvated in acetonitrile. This article replies to their Correspondence, including: the experimental and theoretical analysis on the driving force of electron transfer and a series of new experiments conducted with purer reagents under more stringent conditions, which suggest that the process of proton‐coupled electron transfer (PCET) followed by ConPET cannot be excluded, as proposed in our previous publication. Yet, Ceroni et al.’s efforts to study organic photochemical reactions using time‐resolved spectroscopy remain worthwhile, and their proposed mechanism also led us to consider other possible pathways for the reaction. This article concludes with a series of constructive suggestions for further studying the ConPET mechanism using time‐resolved absorption spectroscopy and other techniques.
A repetitive amplitude encoding method for enhancing the mapping ability of quantum neural networks
Abstract With the rapid development of quantum machine learning, quantum neural networks (QNNs) have become a research hotspot. However, the quantum gates used to implement feature mapping in this model are all linear transformations, which directly affects the mapping ability of the model. Therefore, how to enhance the mapping capability of QNN is an important issue that has not yet been effectively addressed. This paper proposes a repetitive amplitude encoding method that encodes the probability amplitudes of multiple qubit blocks by repeatedly using the same set of classical data, effectively improving the mapping capability of QNN. Taking the MNIST dataset as an example, the experimental results comparing the repetitive amplitude encoding method with several existing encoding methods show that, firstly, when the number of classes is fixed, the repetitive amplitude encoding is superior to other methods. Secondly, when the number of hidden layers in QNN is fixed, as the number of classes increases, the performance of repetitive amplitude encoding not only consistently outperforms other methods, but this advantage becomes increasingly apparent. Finally, the repetitive amplitude encoding-based QNN was applied to reservoir lithology identification in the field of oil and gas exploration, IRIS and WINe classification datasets. By comparing with classical neural networks, the proposed method was validated for its adaptability to different classification problems and superior classification performance compared to classical neural networks.
Multi-objective hybrid optimized coil design for enhanced efficiency, improved voltage gain, and compactness for inductive power transfer
Abstract With a rising global population and vehicle usage, electric vehicles (EVs) have emerged as a sustainable solution for carbon neutrality. The paper’s main objective is to test the performance of the optimized coil design for the Inductive power transfer (IPT) prototype designed for 48 V light EV (LEV) applications operating at 86 kHz. The coils are optimized for objectives such as compactness and power transfer efficiency, using a hybrid multi-objective optimization algorithm combining Taylor-series tuning and Dove Swarm (DSO) optimization. The optimized coil design achieved power transfer efficiency (PTE) of 94% with a voltage gain of 0.93 and current gain of 0.9. A minimum drop of approximately 0.7 V (1.5%) is observed between the primary to secondary coils. Simulation and hardware tests showed minimal voltage loss and strong coupling, validated for variable frequency operation. This shows the robustness of the LC optimized coil compact IPT system designed in this paper. Compact coil design in this research aids in high power transfer efficiency while reducing size, making it well-suited for LEV wireless charging.