Transition Metal-Gallium Intermetallic Compounds with Tailored Active Site Configurations for Electrochemical Ammonia Synthesis.

Gallium (Ga) with a low melting point can serve as a unique metallic solvent in the synthesis of intermetallic compounds (IMCs). The negative formation enthalpy of transition metal-Ga IMCs endows them with high catalytic stability. Meanwhile, their tunable crystal structures offer the possibility to tailor the configurations of active sites to meet the requirements for specific catalytic applications. Herein, we present a general method for preparing a range of transition metal-Ga IMCs, including Co-Ga, Ni-Ga, Pt-Ga, Pd-Ga, and Rh-Ga IMCs. The structurally ordered CoGa IMCs with body-centered cubic (bcc) structure are uniformly dispersed on the nitrogen-doped reduced graphene oxide substrate (O-CoGa/NG) and deliver outstanding nitrate reduction reaction (NO3RR) performance, making them excellent catalysts to construct highly efficient rechargeable Zn-NO3- battery. Operando studies and theoretical simulations demonstrate that the electron-rich environments around the Co atoms enhance the adsorption strength of *NO3 intermediate and simultaneously suppress the formation of hydrogen, thus improving the NO3RR activity and selectivity.

Learning From Human Attention for Attribute-Assisted Visual Recognition.

With prior knowledge of seen objects, humans have a remarkable ability to recognize novel objects using shared and distinct local attributes. This is significant for the challenging tasks of zero-shot learning (ZSL) and fine-grained visual classification (FGVC), where the discriminative attributes of objects have played an important role. Inspired by human visual attention, neural networks have widely exploited the attention mechanism to learn the locally discriminative attributes for challenging tasks. Though greatly promoted the development of these fields, existing works mainly focus on learning the region embeddings of different attribute features and neglect the importance of discriminative attribute localization. It is also unclear whether the learned attention truly matches the real human attention. To tackle this problem, this paper proposes to employ real human gaze data for visual recognition networks to learn from human attention. Specifically, we design a unified Attribute Attention Network (A 2 Net) that learns from human attention for both ZSL and FGVC tasks. The overall model consists of an attribute attention branch and a baseline classification network. On top of the image feature maps provided by the baseline classification network, the attribute attention branch employs attribute prototypes to produce attribute attention maps and attribute features. The attribute attention maps are converted to gaze-like attentions to be aligned with real human gaze attention. To guarantee the effectiveness of attribute feature learning, we further align the extracted attribute features with attribute-defined class embeddings. To facilitate learning from human gaze attention for the visual recognition problems, we design a bird classification game to collect real human gaze data using the CUB dataset via an eye-tracker device. Experiments on ZSL and FGVC tasks without/with real human gaze data validate the benefits and accuracy of our proposed model. This work supports the promising benefits of collecting human gaze datasets and automatic gaze estimation algorithms learning from human attention for high-level computer vision tasks.

Nitrogen-doped amorphous monolayer carbon.

Monoatomic-layered carbon materials, such as graphene1 and amorphous monolayer carbon2,3, have stimulated intense fundamental and applied research owing to their unprecedented physical properties and a wide range of promising applications4,5. So far, such materials have mainly been produced by chemical vapour deposition, which typically requires stringent reaction conditions compared to solution-phase synthesis. Herein, we demonstrate the solution preparation of free-standing nitrogen-doped amorphous monolayer carbon with mixed five-, six- and seven-membered (5-6-7-membered) rings through the polymerization of pyrrole within the confined interlayer cavity of a removable layered-double-hydroxide template. Structural characterizations and first-principles calculations suggest that the nitrogen-doped amorphous monolayer carbon was formed by radical polymerization of pyrrole at the α, ß and N sites subjected to confinement of the reaction space, which enables bond rearrangements through the Stone-Wales transformation. The spatial confinement inhibits the C-C bond rotation and chain entanglement during polymerization, resulting in an atom-thick continuous amorphous layer with an in-plane π-conjugation electronic structure. The spatially confined radical polymerization using solid templates and ion exchange strategy demonstrates potential as a universal synthesis approach for obtaining two-dimensional covalent networks, as exemplified by the successful synthesis of monolayers of polythiophene and polycarbazole.

Metallic 1T/1T' phase TMD nanosheets with enhanced chemisorption sites for ultrahigh-efficiency lead removal.

Two-dimensional (2D) materials, as adsorbents, have garnered great attention in removing heavy metal ions (HMIs) from drinking water due to their extensive exposed adsorption sites. Nevertheless, there remains a paucity of experimental research to remarkably unlock their adsorption capabilities and fully elucidate their adsorption mechanisms. In this work, exceptional lead ion (Pb2+) (a common HMI) removal capacity (up to 758 mg g-1) is achieved using our synthesized metallic 1T/1T' phase 2D transition metal dichalcogenide (TMD, including MoS2, WS2, TaS2, and TiS2) nanosheets, which hold tremendous activated S chemisorption sites. The residual Pb2+ concentration can be reduced from 2 mg L-1 to 2 µg L-1 within 0.5 min, meeting the drinking water standards following World Health Organization guideline (Pb2+ concentrations <10 µg L-1). Atomic-scale characterizations and calculations based on density functional theory unveil that Pb2+ bond to the top positions of transition metal atoms in a single-atom form through the formation of S-Pb bonds. Point-of-use (POU) devices fabricated by our reported metallic phase MoS2 nanosheets exhibit treatment capacity of 55 L-water g-1-adsorbent for feed Pb2+ concentration of 1 mg L-1, which is 1-3 orders of magnitude higher than other 2D materials and commercial activated carbon.

Surface Charge Regulation of Graphene by Fluorine and Chlorine Co-Doping for Constructing Ultra-Stable and Large Energy Density Micro-Supercapacitors.

Settling the structure stacking of graphene (G) nanosheets to maintain the high dispersity has been an intense issue to facilitate their practical application in the microelectronics-related devices. Herein, the co-doping of the highest electronegative fluorine (F) and large atomic radius chlorine (Cl) into G via a one-step electrochemical exfoliation protocol is engineered to actualize the ultralong cycling stability for flexible micro-supercapacitors (MSCs). Density functional theoretical calculations unveiled that the F into G can form the "ionic" C─F bond to increase the repulsive force between nanosheets, and the introduction of Cl can enlarge the layer spacing of G as well as increase active sites by accumulating the charge on pore defects. The co-doping of F and Cl generates the strong synergy to achieve high reversible capacitance and sturdy structure stability for G. The as-constructed aqueous gel-based MSC exhibited the superb cycling stability for 500,000 cycles with no capacitance loss and structure stacking. Furthermore, the ionic liquid gel-based MSC demonstrated a high energy density of 113.9 mW h cm-3 under high voltage of up to 3.5 V. The current work enlightens deep insights into the design and scalable preparation of high-performance co-doped G electrode candidate in the field of flexible microelectronics.

Circadian Rhythms Correlated in DNA Methylation and Gene Expression Identified in Human Blood and Implicated in Psychiatric Disorders.

Circadian rhythms modulate the biology of many human tissues and are driven by a nearly 24-h transcriptional feedback loop. Dynamic DNA methylation may play a role in driving 24-h rhythms of gene expression in the human brain. However, little is known about the degree of circadian regulation between the DNA methylation and the gene expression in the peripheral tissues, including human blood. We hypothesized that 24-h rhythms of DNA methylation play a role in driving 24-h RNA expression in human blood. To test this hypothesis, we analyzed DNA methylation levels and RNA expression in blood samples collected from eight healthy males at six-time points over 24 h. We assessed 442,703 genome-wide CpG sites in methylation and 12,364 genes in expression for 24-h rhythmicity using the cosine model. Our analysis revealed significant rhythmic patterns in 6345 CpG sites and 21 genes. Next, we investigated the relationship between methylation and expression using powerful circadian signals. We found a modest negative correlation (ρ = -0.83, p = 0.06) between the expression of gene TXNDC5 and the methylation at the nearby CpG site (cg19116172). We also observed that circadian CpGs significantly overlapped with genetic risk loci of schizophrenia and autism spectrum disorders. Notably, one gene, TXNDC5, showed a significant correlation between circadian methylation and expression and has been reported to be association with neuropsychiatric diseases.

Protein phosphatase 4 maintains the survival of primordial follicles by regulating autophagy in oocytes.

In mammalian ovary, the primordial follicle pool serves as the source of developing follicles and fertilizable ova. To maintain the normal length of female reproductive life, the primordial follicles must have adequate number and be kept in a quiescent state before menopause. However, the molecular mechanisms underlying primordial follicle survival are poorly understood. Here, we provide genetic evidence showing that lacking protein phosphatase 4 (PPP4) in oocytes, a member of PP2A-like subfamily, results in infertility in female mice. A large quantity of primordial follicles has been depleted around the primordial follicle pool formation phase and the ovarian reserve is exhausted at about 7 months old. Further investigation demonstrates that depletion of PPP4 causes the abnormal activation of mTOR, which suppresses autophagy in primordial follicle oocytes. The abnormal primordial follicle oocytes are eventually erased by pregranulosa cells in the manner of lysosome invading. These results show that autophagy prevents primordial follicles over loss and PPP4-mTOR pathway governs autophagy during the primordial follicle formation and dormant period.

Carrier-phonon decoupling in perovskite thermoelectrics via entropy engineering.

Thermoelectrics converting heat and electricity directly attract broad attentions. To enhance the thermoelectric figure of merit, zT, one of the key points is to decouple the carrier-phonon transport. Here, we propose an entropy engineering strategy to realize the carrier-phonon decoupling in the typical SrTiO3-based perovskite thermoelectrics. By high-entropy design, the lattice thermal conductivity could be reduced nearly to the amorphous limit, 1.25 W m-1 K-1. Simultaneously, entropy engineering can tune the Ti displacement, improving the weighted mobility to 65 cm2 V-1 s-1. Such carrier-phonon decoupling behaviors enable the greatly enhanced µW/κL of ~5.2 × 103 cm3 K J-1 V-1. The measured maximum zT of 0.24 at 488 K and the estimated zT of ~0.8 at 1173 K in (Sr0.2Ba0.2Ca0.2Pb0.2La0.2)TiO3 film are among the best of n-type thermoelectric oxides. These results reveal that the entropy engineering may be a promising strategy to decouple the carrier-phonon transport and achieve higher zT in thermoelectrics.

Van der Waals phase transition investigation toward high-voltage layered cathodes.

High-voltage phase transition constitutes the major barrier to accessing high energy density in layered cathodes. However, questions remain regarding the origin of phase transition, because the interlayer weak bonding features cannot get an accurate description by experiments. Here, we determined van der Waals (vdW) interaction (vdWi) in LixCoO2 via visualizing its electron density, elucidating the origin of O3─O1 phase transition. The charge around oxygen is distorted by the increasing Co─O covalency. The charge distortion causes the difference of vdW gap between O3 and O1 phases, verified by a gap corrected vdW equation. In a high charging state, excessive covalency breaks the vdW gap balance, driving the O3 phase toward a stable O1 one. This interpretation of vdWi-dominated phase transition can be applied to other layered materials, as shown by a map regarding degree of covalence. Last, we introduce the cationic potential to provide a solution for designing high-voltage layered cathodes.

Mechanistic insights into temperature-driven retention and speciation changes of heavy metals (HMs) in ash residues from Co-combustion of refuse-derived fuel (RDF) and red mud.

Red mud is a promising candidate for promoting the incineration of Refuse Derived Fuel (RDF) and stabilizing the resulting incineration ash. The combustion conditions, notably temperature, significantly steers the migration and transformation of harmful metal components during combustion, and ultimately affect their retention and speciation in the ash residue. The study attempted to investigate the effect of co-combustion temperature on the enrichment and stability of Cr, Ni, Cu, Zn, Cd and Pb within bottom ashes, and to reveal the underlined promotion mechanism of red mud addition. As temperature increased, red mud's active components formed a robust matrix, helping the formation, melting, and vitrification of silicates and aluminosilicates in the bottom ashes. The process significantly contributed to the encapsulation and stabilization of heavy metals such as Ni, Cu, Zn, Cd, and Pb, with their residual fractions ascending to 71.37%, 55.75%, 74.78%, 84.24%, and 93.54%, respectively. Conversely, high temperatures led to an increase in the proportion of Cr in the extremely unstable acid-soluble fraction of the bottom ashes, reaching 31.52%, posing a heightened risk of environmental migration. Considering the stability of heavy metals in the bottom ashes and the combustion characteristics, 800 °C is identified as the optimal temperature for the co-combustion of RDF and red mud, balancing efficiency and environmental safety. The findings will provide valuable insights for the co-utilization strategy of RDF and red mud, contributing to more informed decision-making in waste-to-energy processes.

Oxophilic gallium single atoms bridged ruthenium clusters for practical anion-exchange membrane electrolyzer.

The development of highly efficient and durable alkaline hydrogen evolution reaction (HER) catalysts is crucial for achieving high-performance practical anion exchange membrane water electrolyzer (AEMWE) at ampere-level current density. Herein, we report a design concept by employing Ga single atoms as an electronic bridge to stabilize the Ru clusters for boosting alkaline HER performance in practical AEMWE. Experimental and theoretical results collectively reveal that the bridged Ga sites trigger strong metal-support interaction for the homogeneous distribution of Ru clusters with high density, as well as optimize the Ru-H bond strength due to the electron transfer between Ru and Ga for enhanced intrinsic HER activity. Moreover, the oxophilic Ga sites near the Ru clusters tend to adsorb the hydroxyl species and accelerate the water dissociation for sufficient proton supplement in an alkaline medium. The Ru-GaSA/N-C catalyst exhibits a low overpotential of 4 ± 1 mV (10 mA cm-2) and high mass activity of 9.3 ± 0.5 A mg-1Ru at -0.05 V vs RHE. In particular, the Ru-GaSA/N-C-based AEMWE in 1 M KOH delivers a voltage of only 1.74 V to reach an industrial current density of 1 A cm-2, and can steadily operate at 1 A cm-2 for more than 170 h.

A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images with Multi-Relationship Graph Learning.

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.

An orbital strategy for regulating the Jahn-Teller effect.

The Jahn-Teller effect (JTE) arising from lattice-electron coupling is a fascinating phenomenon that profoundly affects important physical properties in a number of transition-metal compounds. Controlling JT distortions and their corresponding electronic structures is highly desirable to tailor the functionalities of materials. Here, we propose a local coordinate strategy to regulate the JTE through quantifying occupancy in the [Formula: see text] and [Formula: see text] orbitals of Mn and scrutinizing the symmetries of the ligand oxygen atoms in MnO6 octahedra in LiMn2O4 and Li0.5Mn2O4. The effectiveness of such a strategy has been demonstrated by constructing P2-type NaLi x Mn1 - x O2 oxides with different Li/Mn ordering schemes. In addition, this strategy is also tenable for most 3d transition-metal compounds in spinel and perovskite frameworks, indicating the universality of local coordinate strategy and the tunability of the lattice-orbital coupling in transition-metal oxides. This work demonstrates a useful strategy to regulate JT distortion and provides useful guidelines for future design of functional materials with specific physical properties.

Electric Field-Manipulated Optical Chirality in Ferroelectric Vortex Domains.

Manipulating optical chirality via electric fields has garnered considerable attention in the realm of both fundamental physics and practical applications. Chiral ferroelectrics, characterized by their inherent optical chirality and switchable spontaneous polarization, are emerging as a promising platform for electronic-photonic integrated circuits applications. Unlike organics with chiral carbon centers, integrating chirality into technologically mature inorganic ferroelectrics has posed a long-standing challenge. Here, the successful introduction of chirality is reported into self-assembly La-doped BiFeO3 nanoislands, which exhibit ferroelectric vortex domains. By employing synergistic experimental techniques with piezoresponse force microscopy and nonlinear optical second-harmonic generation probes, a clear correlation between chirality and polarization configuration within these ferroelectric nanoislands is established. Furthermore, the deterministic control of ferroelectric vortex domains and chirality is demonstrated by applying electric fields, enabling reversible and nonvolatile generation and elimination of optically chiral signals. These findings significantly expand the repertoire of field-controllable chiral systems and lay the groundwork for the development of innovative ferroelectric optoelectronic devices.

Omnidirectional anisotropic embedded 3D bioprinting.

Anisotropic microstructures resulting from a well-ordered arrangement of filamentous extracellular matrix (ECM) components or cells can be found throughout the human body, including skeletal muscle, corneal stroma, and meniscus, which play a crucial role in carrying out specialized physiological functions. At present, due to the isotropic characteristics of conventional hydrogels, the construction of freeform cell-laden anisotropic structures with high-bioactive hydrogels is still a great challenge. Here, we proposed a method for direct embedded 3D cell-printing of freeform anisotropic structure with shear-oriented bioink (GelMA/PEO). This study focuses on the establishment of an anisotropic embedded 3D bioprinting system, which effectively utilizes the shear stress generated during the extrusion process to create cells encapsulating tissues with distinct anisotropy. In conjunction with the water-solubility of PEO and the in-situ encapsulation effect provided by the carrageenan support bath, high-precise cell-laden bioprinting of intricate anisotropic and porous bionic artificial tissues can be effectively implemented in one-step. Additionally, anisotropic permeable blood vessel has been taken as a representation to validate the effectiveness of the shear-oriented bioink system in fabricating intricate structures with distinct directional characteristics. Lastly, the successful preparation of muscle patches with anisotropic properties and their guiding role for cell cytoskeleton extension have provided a significant research foundation for the application of the anisotropic embedded 3D bioprinting system in the ex-vivo production and in-vivo application of anisotropic artificial tissues.

Frequency-aware Feature Fusion for Dense Image Prediction.

Dense image prediction tasks demand features with strong category information and precise spatial boundary details at high resolution. To achieve this, modern hierarchical models often utilize feature fusion, directly adding upsampled coarse features from deep layers and high-resolution features from lower levels. In this paper, we observe rapid variations in fused feature values within objects, resulting in intra-category inconsistency due to disturbed high-frequency features. Additionally, blurred boundaries in fused features lack accurate high frequency, leading to boundary displacement. Building upon these observations, we propose Frequency-Aware Feature Fusion (FreqFusion), integrating an Adaptive Low-Pass Filter (ALPF) generator, an offset generator, and an Adaptive High-Pass Filter (AHPF) generator. The ALPF generator predicts spatially-variant low-pass filters to attenuate high-frequency components within objects, reducing intra-class inconsistency during upsampling. The offset generator refines large inconsistent features and thin boundaries by replacing inconsistent features with more consistent ones through resampling, while the AHPF generator enhances high-frequency detailed boundary information lost during downsampling. Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries. Extensive experiments across various dense prediction tasks confirm its effectiveness. The code is made publicly available at https://github.com/Linwei-Chen/FreqFusion.

Rethinking masked image modelling for medical image representation.

Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success in computer vision by improving image representations using unannotated data. Traditional MIMs typically employ a strategy of random sampling across the image. However, this random masking technique may not be ideally suited for medical imaging, which possesses distinct characteristics divergent from natural images. In medical imaging, particularly in pathology, disease-related features are often exceedingly sparse and localized, while the remaining regions appear normal and undifferentiated. Additionally, medical images frequently accompany reports, directly pinpointing pathological changes' location. Inspired by this, we propose Masked medical Image Modelling (MedIM), a novel approach, to our knowledge, the first research that employs radiological reports to guide the masking and restore the informative areas of images, encouraging the network to explore the stronger semantic representations from medical images. We introduce two mutual comprehensive masking strategies, knowledge-driven masking (KDM), and sentence-driven masking (SDM). KDM uses Medical Subject Headings (MeSH) words unique to radiology reports to identify symptom clues mapped to MeSH words (e.g., cardiac, edema, vascular, pulmonary) and guide the mask generation. Recognizing that radiological reports often comprise several sentences detailing varied findings, SDM integrates sentence-level information to identify key regions for masking. MedIM reconstructs images informed by this masking from the KDM and SDM modules, promoting a comprehensive and enriched medical image representation. Our extensive experiments on seven downstream tasks covering multi-label/class image classification, pneumothorax segmentation, and medical image-report analysis, demonstrate that MedIM with report-guided masking achieves competitive performance. Our method substantially outperforms ImageNet pre-training, MIM-based pre-training, and medical image-report pre-training counterparts. Codes are available at https://github.com/YtongXie/MedIM.

Trace Small Molecular/Nano-Colloidal Multiscale Electrolyte Additives Enable Ultra-Long Lifespan of Zinc Metal Anodes.

Electrolyte additives are efficient to improve the performance of aqueous zinc-ion batteries (AZIBs), yet the current electrolyte additives are limited to fully water-soluble additives (FWAs) and water-insoluble additives (WIAs). Herein, trace slightly water-soluble additives (SWAs) of zinc acetylacetonate (ZAA) were introduced to aqueous ZnSO4 electrolytes. The SWA system of ZAA is composed of a FWA part and a WIA part in a dynamic manner of dissolution equilibrium. The FWA part exists as soluble small molecules, which efficiently regulate Zn2+ ion solvation structure, while the WIA part exists as insoluble nano-colloids, which in-situ form a thick and robust solid electrolyte interface film on zinc metal anodes (ZMAs). Such small molecular/nano-colloidal multiscale electrolyte additives of ZAA are capable to not only improve ionic conductivity and transference number but also inhibit corrosion, hydrogen evolution, and Zn dendrite on ZMAs. The SWA-based Zn∥Zn half battery delivers a superb cumulative plating capacity of 15 Ah cm-2 under 1 mAh cm-2 and 20 mA cm-2, and the SWA-based NH4V4O10∥Zn pouch cell obtains a capacity retention of 67.8% within 4000 cycles under 4 A g-1. The study provides innovative insights for rational design of electrolyte additives, which may pave the way for the practicality of AZIBs.

Versatile Method of Engineering the Band Alignment and the Electron Wavefunction Hybridization of Hybrid Quantum Devices.

Hybrid devices that combine superconductors (S) and semiconductors (Sm) have attracted great attention due to the integration of the properties of both materials, which relies on the interface details and the resulting coupling strength and wavefunction hybridization. However, until now, none of the experiments have reported good control of the band alignment of the interface, as well as its tunability to the coupling and hybridization. Here, the interface is modified by inducing specific argon milling while maintaining its high quality, e.g., atomic connection, which results in a large induced superconducting gap and ballistic transport. By comparing with Schrödinger-Poisson calculations, it is proven that this method can vary the band bending/coupling strength and the electronic spatial distribution. In the strong coupling regime, the coexistence and tunability of crossed Andreev reflection and elastic co-tunneling-key ingredients for the Kitaev chain-are confirmed. This method is also generic for other materials and achieves a hard and huge superconducting gap in lead and indium antimonide nanowire (Pb-InSb) devices. Such a versatile method, compatible with the standard fabrication process and accompanied by the well-controlled modification of the interface, will definitely boost the creation of more sophisticated hybrid devices for exploring physics in solid-state systems.