A submicron forest-like silicon surface promotes bone regeneration by regulating macrophage polarization.

Introduction: Silicon is a major trace element in humans and a prospective supporting biomaterial to bone regeneration. Submicron silicon pillars, as a representative surface topography of silicon-based biomaterials, can regulate macrophage and osteoblastic cell responses. However, the design of submicron silicon pillars for promoting bone regeneration still needs to be optimized. In this study, we proposed a submicron forest-like (Fore) silicon surface (Fore) based on photoetching. The smooth (Smo) silicon surface and photoetched regular (Regu) silicon pillar surface were used for comparison in the bone regeneration evaluation. Methods: Surface parameters were investigated using a field emission scanning electron microscope, atomic force microscope, and contact angle instrument. The regulatory effect of macrophage polarization and succedent osteogenesis was studied using Raw264.7, MC3T3-E1, and rBMSCs. Finally, a mouse calvarial defect model was used for evaluating the promoting effect of bone regeneration on the three surfaces. Results: The results showed that the Fore surface can increase the expression of M2-polarized markers (CD163 and CD206) and decrease the expression of inflammatory cytokines, including interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α). Fore surface can promote the osteogenesis in MC3T3-E1 cells and osteoblastic differentiation of rBMSCs. Furthermore, the volume fraction of new bone and the thickness of trabeculae on the Fore surface were significantly increased, and the expression of RANKL was downregulated. In summary, the upregulation of macrophage M2 polarization on the Fore surface contributed to enhanced osteogenesis in vitro and accelerated bone regeneration in vivo. Discussion: This study strengthens our understanding of the topographic design for developing future silicon-based biomaterials.

Retarding anion migration for alleviating concentration polarization towards stable polymer lithium-metal batteries.

Traditional dual-ion lithium salts have been widely used in solid polymer lithium-metal batteries (LMBs). Nevertheless, concentration polarization caused by uncontrolled migration of free anions has severely caused the growth of lithium dendrites. Although single-ion conductor polymers (SICP) have been developed to reduce concentration polarization, the poor ionic conductivity caused by low carrier concentration limits their application. Herein, a dual-salt quasi-solid polymer electrolyte (QSPE), containing the SICP network as a salt and traditional dual-ion lithium salt, is designed for retarding the movement of free anions and simultaneously providing sufficient effective carriers to alleviate concentration polarization. The dual salt network of this designed QSPE is prepared through in-situ crosslinking copolymerization of SICP monomer, regular ionic conductor, crosslinker with the presence of the dual-ion lithium salt, delivering a high lithium-ion transference number (0.75) and satisfactory ionic conductivity (1.16 × 10-3 S cm-1 at 30 °C). Comprehensive characterizations combined with theoretical calculation demonstrate that polyanions from SICP exerts a potential repulsive effect on the transport of free anions to reduce concentration polarization inhibiting lithium dendrites. As a consequence, the Li||LiFePO4 cell achieves a long-cycle stability for 2000 cycles and a 90% capacity retention at 30 °C. This work provides a new perspective for reducing concentration polarization and simultaneously enabling enough lithium-ions migration for high-performance polymer LMBs.

Numerical Simulation Technologies in Solar-Driven Interfacial Evaporation Processes.

Solar interfacial evaporation technology has the advantages of environmentally conscious and sustainable benefits. Recent research on light absorption, water transportation, and thermal management has improved the evaporation performance of solar interfacial evaporators. However, many studies on photothermal materials and structures only aim to improve performance, neglecting explanations for heat and mass transfer coupling or providing evidence for performance enhancement. Numerical simulation can simulate the diffusion paths and heat and water transfer processes to understand the thermal and mass transfer mechanism, thereby better achieving the design of efficient solar interfacial evaporators. Therefore, this review summarizes the latest exciting findings and tremendous advances in numerical simulation for solar interfacial evaporation. First, it presents a macroscopic summary of the application of simulation in temperature distribution, salt concentration distribution, and vapor flux distribution during evaporation. Second, the utilization of simulation in the microscopic is summed up, specifically focusing on the movement of water molecules and the mechanisms of light responses during evaporation. Finally, all simulation methods have the goal of validating the physical processes in solar interfacial evaporation. It is hoped that the use of numerical simulation can provide theoretical guidance and technical support for the application of solar-driven interfacial evaporation technology.

Nanoscaled Titanium Oxide Layer Provokes Quick Osseointegration on 3D-Printed Dental Implants: A Domino Effect Induced by Hydrophilic Surface.

Three-dimensional printing is a revolutionary strategy to fabricate dental implants. Especially, 3D-printed dental implants modified with nanoscaled titanium oxide layer (H-SLM) have impressively shown quick osseointegration, but the accurate mechanism remains elusive. Herein, we unmask a domino effect that the hydrophilic surface of the H-SLM facilitates blood wetting, enhances the blood shear rate, promotes blood clotting, and changes clot features for quick osseointegration. Combining computational fluid dynamic simulation and biological verification, we find a blood shear rate during blood wetting of the hydrophilic H-SLM 1.2-fold higher than that of the raw 3D-printed implant, which activates blood clot formation. Blood clots formed on the hydrophilic H-SLM demonstrate anti-inflammatory and pro-osteogenesis effects, leading to a 1.5-fold higher bone-to-implant contact and a 1.8-fold higher mechanical anchorage at the early stage of osseointegration. This mechanism deepens current knowledge between osseointegration speed and implant surface characteristics, which is instructive in surface nanoscaled modification of multiple 3D-printed intrabony implants.

Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation.

To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGAN-ECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at github.com/VanSWK/QCGAN_ECG.

DTQFL: A Digital Twin-Assisted Quantum Federated Learning Algorithm for Intelligent Diagnosis in 5G Mobile Network.

Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultane-ously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized train-ing, the VQNN can achieve higher accuracy than that with-out personalized training.

IoMT-based smart healthcare detection system driven by quantum blockchain and quantum neural network.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things (IoMT). The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system named QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network nameded HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.

Role of solvent in plasma-assisted peroxymonosulfate-hydrothermal process for plastic conversion.

Hydrothermal processes are considered a promising strategy for the conversion of ever-growing plastic wastes. Plasma-assisted peroxymonosulfate-hydrothermal process has attracted increasing attention in enhancing the efficiency of hydrothermal conversion. However, the role of solvent in this process is unclear and rarely researched. Herein, the conversion process with different water-based solvents was investigated based on a plasma-assisted peroxymonosulfate-hydrothermal reaction. As the ratio of the solvent effective volume in the reactor increased from 20% to 53.3%, the conversion efficiency displayed an obvious decrease from 7.1% to 4.2%. The results indicated that the increased pressure caused by the solvent greatly reduced the surface reaction and forced the hydrophilic groups to shift back to the carbon chain, thereby reducing the reaction kinetics. A further increase in the solvent effective volume ratio could promote the conversion in the inner layer of the plastics to achieve an increase of the conversion efficiency. These findings can provide valuable guidance for the design of hydrothermal conversion for plastic wastes.

Structure Design for Ultrahigh Power Density Proton Exchange Membrane Fuel Cell.

Next-generation ultrahigh power density proton exchange membrane fuel cells rely not only on high-performance membrane electrode assembly (MEA) but also on an optimal cell structure. To this end, this work comprehensively investigates the cell performance under various structures, and it is revealed that there is unexploited performance improvement in structure design because its positive effect enhancing gas supply is often inhibited by worse proton/electron conduction. Utilizing fine channel/rib or the porous flow field is feasible to eliminate the gas diffusion layer (GDL) and hence increase the power density significantly due to the decrease of cell thickness and gas/electron transfer resistances. The cell structure combining fine channel/rib, GDL elimination and double-cell structure is believed to increase the power density from 4.4 to 6.52 kW L-1 with the existing MEA, showing nearly equal importance with the new MEA development in achieving the target of 9.0 kW L-1 .

Extracellular matrix physical properties govern the diffusion of nanoparticles in tumor microenvironment.

Nanoparticles (NPs) are confronted with limited and disappointing delivery efficiency in tumors clinically. The tumor extracellular matrix (ECM), whose physical traits have recently been recognized as new hallmarks of cancer, forms a main steric obstacle for NP diffusion, yet the role of tumor ECM physical traits in NP diffusion remains largely unexplored. Here, we characterized the physical properties of clinical gastric tumor samples and observed limited distribution of NPs in decellularized tumor tissues. We also performed molecular dynamics simulations and in vitro hydrogel experiments through single-particle tracking to investigate the diffusion mechanism of NPs and understand the influence of tumor ECM physical properties on NP diffusion both individually and collectively. Furthermore, we developed an estimation matrix model with evaluation scores of NP diffusion efficiency through comprehensive analyses of the data. Thus, beyond finding that loose and soft ECM with aligned structure contribute to efficient diffusion, we now have a systemic model to predict NP diffusion efficiency based on ECM physical traits and provide critical guidance for personalized tumor diagnosis and treatment.

Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges.

Porous flow fields distribute fuel and oxygen for the electrochemical reactions of proton exchange membrane (PEM) fuel cells through their pore network instead of conventional flow channels. This type of flow fields has showed great promises in enhancing reactant supply, heat removal, and electrical conduction, reducing the concentration performance loss and improving operational stability for fuel cells. This review presents the research and development progress of porous flow fields with insights for next-generation PEM fuel cells of high power density (e.g., ∼9.0 kW L-1). Materials, fabrication methods, fundamentals, and fuel cell performance associated with porous flow fields are discussed in depth. Major challenges are described and explained, along with several future directions, including separated gas/liquid flow configurations, integrated porous structure, full morphology modeling, data-driven methods, and artificial intelligence-assisted design/optimization.

A prediction model for nanoparticle diffusion behavior in fibrous materials considering steric and hydrodynamic resistances.

Precise prediction of the hindered diffusion behavior of electroneutral particles in fibrous media plays a critical role in the development of drugs, polymer membranes, and porous electrodes. However, the random microstructure and unknown coupling relationship of multiple resistance mechanisms lead to the lack of a universal prediction model. In this work, a dual-resistance model is proposed by reconstructed pore-scale simulations, which presents the coexistence of steric and hydrodynamic resistances in the multiplication form. The simulation results show that the relationship between steric resistance and structural parameters (porosity, fiber radius, and particle radius) is exponential, while that for hydrodynamic resistance is polynomial. The dominant diffusion resistance is found to change from hydrodynamic to steric resistance with a decrease in porosity. The fluorescent polystyrene microsphere diffusivity in a series of SiO2 fibrous media is determined by single-particle tracking experiments, quantitatively confirming the dual-resistance model. The present model can be used for rapid diffusivity prediction and fibrous membrane and drug design.

Electronic-Level Insight into Interfacial Effects and Their Induced Anisotropic Ion Diffusion and Ion Selectivity in Nanochannels.

Osmotic energy conversion features directional ion migration in selective nanochannels, dominated by interfacial effects, temperature, and concentration. Current efforts emphasize membrane modification for superior reliability and durability, whereas the origin and implication of interfacial effects are unclear. This work performs ab initio molecular dynamics simulations for hydrated ion-graphene oxide interfaces by regulating the temperature and concentration. The interfacial effects associated with their induced anisotropic ion diffusion and ion selectivity are revealed. The scientific essence of the interfacial effects is an electron transfer triggered by hydrated ion-functional group interactions. The interfacial effects are clarified to include dynamic solvation structures, interfacial H-bonds, and chemical reactions. Ions possess incomplete hydration shells, and their arrangements vary from ordered to disordered to overlapped. Interfacial H-bonds restrict hydrated ions by constraining water molecules, whereas continuous reactions provide lateral pathways to generate anisotropy. Cation selectivity is further clarified by negative surface charges from hydroxyl deprotonation. Besides, temperature rise induces disordered hydrated ions as well as frequent and violent reactions, enhancing ion diffusion, selectivity, and anisotropy; excessive concentrations produce overlapped hydrated ions, more H-bonds, and inferior reactions, weakening ion diffusion, selectivity, and anisotropy. Finally, the bottom-up concept for osmotic energy conversion is summarized, and elevated temperature combined with low concentration is found to boost ion diffusion and ion selectivity synergistically. This work provides an in-depth understanding of interfacial phenomena and ion behaviors in nanochannels.

A Real-Time Self-Adaptive Thermal Metasurface.

Emerging metamaterials have served as an efficient strategy for the realization of unconventional heat control and management using structural thermal properties, and many functional thermal metadevices have been investigated. However, thermal functions are usually fixed or limited in the switching range. Thus far, real-time thermal regulation is elusive for thermal metamaterials because of deterministic artificial metastructures and uncontrollable phase transitions, coupled with the absence of dynamic adaptability. Here, a self-adaptive metasurface platform to implement programmable thermal functions via the automatic evolution of thermoelectric heat sources and real-time control of the driven voltage is reported. The proof-of-concept smart platform experimentally demonstrates arbitrary switching between elaborate thermal patterns consolidated into an active thermoelectric element matrix. Further, thermal pixels and feedback control systems are integrated into printed circuit boards, resulting in self-adaptability to any thermal requirements. This study sets up a new paradigm for arbitrary transitions between exquisite thermal patterns and is expected to pave the way for real-time thermal management in a programming formation.

Passive Ultra-Conductive Thermal Metamaterials.

Ultra-conductive heat transport showcases significant potentials in popular thermal managements with convection, phase change, and heat source. However, it is captivated impossible for passive thermal manipulation, usually bounded by intrinsic thermal conductivities of natural materials, to outperform these active recipes in need of extra energy payload. Here, a robust recipe to create passive ultra-conductive thermal metamaterials consisting of nothing but bulk natural materials is reported. Thanks to the local thermal resistance regulation by vertical thermal transport channel, the proof-of-concept thermal metamaterials experimentally demonstrate extreme effective conductivity (1915 W m-1  K-1 ) solely with naturally occurring materials. The purely conductive modulation without any external energy is comparable to active counterparts, and further reveals its robustness and unexpected convenience. The findings construct a high-efficient paradigm of passive thermal management with ultra-conductive heat transport, and further hint potentials in other Laplace fields, e.g., DC and magnetostatics.

Collective Enhancements on Thermal-Electrical and Mechanical Properties of Graphite-Based Composite Bipolar Plates through the Coupled Manipulations of Molding and Impregnation Pressures.

The performance and durability of proton exchange fuel cells (PEMFCs) are greatly affected by the bipolar plate (BP). In this paper, the thermal and electrical conductivities and mechanical property of graphite filled with resin composite BPs were collectively enhanced through the effectively coupled manipulations of molding pressure and impregnation pressure. The microstructures show that the resin tends to distribute at the top region of the rib under high impregnation pressure. The thermal and electrical conductivities of the pure expanded graphite BP is well reserved in the composite BPs under high molding pressure, which can facilitate the heat transfer and electron conduction in the PEMFCs. The relative density and compressive strength of composite BPs were greatly enhanced by the impregnation of resin compared to the expanded graphite under high molding pressure without the impregnation of resin (HU-BP). The maximum thermal conductivity, compressive strength, and minimum interfacial contact resistance (ICR) are collectively achieved in the HL-BP. The enhanced thermal-electrical and mechanical properties could be mainly attributed to the well-reserved continuous networks of graphite in the composite BPs. The findings in this paper are expected to synergetically improve the thermal, electrical, and mechanical properties of composite BPs through coupled manipulations of the molding and impregnation pressures, which in turn enhances the power density and durability of PEMFCs.

PerAE: An Effective Personalized AutoEncoder for ECG-Based Biometric in Augmented Reality System.

With the development of the Augmented and Virtual Reality (AR/VR) technologies, massive biometric data are collected by different organizations. These data have great significance but also worsen the privacy risks. Electro-CardioGram (ECG)-based Identity Recognition (EIR) is a popular Biometric technology. An ECG record is an internal Biology feature of a person and has time continuity. Thus, compared with traditional Biometric methods like face recognition, EIR may be less vulnerable to attack. We propose an Autoencoder-based EIR system, called Personalized AutoEncoder (PerAE). PerAE maintains a small autoencoder model (called Attention-MemAE) for each registered user of a system. The Attention-MemAE enhances the autoencoder by using a memory module and two attention mechanisms. A user's Attention-MemAE classifies the hearbeats of other users as anomalies. An Attention-MemAE can be updated when the distribution of the user's ECG data is changed. By using personalized autoencoder, PerAE can improve the time efficiency and reduce the memory overhead. It improves the adaptability, scalability, and maintainability of EIR systems. Experiment results show that to train an Attention-MemAE with 90 % identification accuracy for a user, we can just take five minutes to collect the user's ECG data (around 500 heartbeat samples).

Thermal Management for Hydrogen Charging and Discharging in a Screened Metal-Organic Framework Particle Tank.

Thermal management of H2 gas storage in a tank is crucial for determining the H2 gas deliverable capacity. In this study, a strategy for the design of an excellent comprehensive performance fuel storage tank from the screening of microscopic materials to the design of macroscopic particle adsorption tank performance is proposed. The best metal-organic framework (MOF) for H2 deliverable capacity in a computation-ready experimental MOF database is first screened using a grand canonical Monte Carlo (GCMC) method. An upscale model that combines the finite volume method with GCMC is then established to investigate the H2 charging and discharging processes in a screened best MOF-filled adsorption particle tank that is integrated with a phase-change material (PCM) jacket. The process of the heat and mass transfer in the screened best MOF particle adsorption tank with and without the PCM jacket-inserted metal foam is studied. The results show that the prescreened XAWVUN has the highest gravimetric and considerable volumetric deliverable capacity among 503 MOFs, which can reach up to 23.1 mol·kg-1 and 20.8 kg·m-3 at 298 K and pressures between 35 000 kPa (adsorption pressure) and 160 kPa (desorption pressure), respectively. The H2 deliverable capacity can be maximized by 3.2 and 12.1% for PCM jackets inserted with metal foam in the H2 charging and discharging processes when it is compared with the case without the PCM jacket, respectively. The above study will facilitate the development of new equipment for hydrogen storage.

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.

Thermococcus aciditolerans sp. nov., a piezotolerant, hyperthermophilic archaeon isolated from a deep-sea hydrothermal vent chimney in the Southwest Indian Ridge.

A hyperthermophilic, strictly anaerobic archaeon, designated strain SY113T, was isolated from a deep-sea hydrothermal vent chimney on the Southwest Indian Ridge at a water depth of 2770 m. Enrichment and isolation of strain SY113T were performed at 85 °C at 0.1 MPa. Cells of strain SY113T were irregular motile cocci with peritrichous flagella and generally 0.8-2.4 µm in diameter. Growth was observed at temperatures between 50 and 90 °C (optimum at 85 °C) and under hydrostatic pressures of 0.1-60 MPa (optimum, 27 MPa). Cells of SY113T grew at pH 4.0-9.0 (optimum, pH 5.5) and a NaCl concentration of 0.5-5.5 % (w/v; optimum concentration, 3.0 % NaCl). Strain SY113T was an anaerobic chemoorganoheterotroph and grew on complex proteinaceous substrates such as yeast extract and tryptone, as well as on maltose and starch. Elemental sulphur stimulated growth, but not obligatory for its growth. The G+C content of the genomic DNA was 55.0 mol%. Phylogenetic analysis of the 16S rRNA sequence of strain SY113T showed that the novel isolate belonged to the genus Thermococcus. On the basis of physiological characteristics, average nucleotide identity values and in silico DNA-DNA hybridization results, we propose a novel species, named Thermococcus aciditolerans sp. nov. The type strain is SY113T (=MCCC 1K04190T=JCM 39083T).