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The growing demand for advanced solid-state lithium metal batteries has attracted considerable attention to the development of garnet-based membranes, known for their high ionic conductivity and superior electrochemical stability. Among the fabrication methods for garnet-based membranes, the tape-casting method is recognized as a mature and widely applied process, characterized by its simplicity, low cost, and suitability for large-scale production. In this review paper, we provide a comprehensive summary of this topic, emphasizing the intricate interplay among material properties, processing parameters, and membrane performance. We discuss key challenges in creating dense and porous garnet membranes, including controlling lithium volatilization, optimizing pore size, and maintaining high mechanical strength. We also evaluate emerging strategies for interface engineering and integration with other fabrication techniques, offering insights into scalability and environmental considerations of the tape-casting process. This review is a valuable resource for researchers seeking to advance solid-state lithium metal batteries through innovative tape-casting methods.

Although RuO2is a potential electrocatalyst for Li–O2batteries (LOBs), its high price limits its large-scale application. Recent reports suggest that combining small amounts of RuO2with nonprecious supports is a wise choice for developing low-cost bifunctional electrocatalysts. Here, a series of cobalt-containing supports, including Co3O4, Co-NCNTFs, and NiCo2O4, are designed to support RuO2to explore their electrocatalytic performance in LOBs. Among them, RuO2@NiCo2O4exhibits significantly better catalytic activity and stability compared to the RuO2@Co3O4and RuO2@Co-NCNTF counterparts. Notably, RuO2@NiCo2O4achieved a low overpotential of 0.51 V and exceptional long-term reversibility exceeding 2000 h at a capacity limit of 1000 mAh g–1with a current density of 500 mA g–1, suggesting that the electronic conductance, microstructure, and electrochemical stability of the support are crucial factors in designing RuO2-based electrocatalysts for LOBs. This research provides valuable insights into designing cost-effective and efficient catalysts for advancing energy storage technologies.

Accident-tolerant fuels (ATF) have been extensively studied to reduce the production rate and total amount of heat and hydrogen generated from high-temperature steam oxidation during severe accidents. Chromium-coated zirconium alloy cladding has become one of the most promising candidates for ATF because of its excellent corrosion and oxidation resistance. This paper provides a comprehensive overview of the research progress on oxidation behaviors and degradation mechanisms in chromium-coated zirconium alloy cladding under high-temperature conditions. Potential techniques to strengthen the oxidation resistance are highlighted and compared. Finally, challenges and opportunities for various future directions are addressed.

Supported Cs-based catalysts are considered as potential candidates for methyl methacrylate (MMA) synthesis via one-step aldol condensation, but they suffer from low activity. In this paper, the acid–base properties of the Cs/SiO2catalyst were modulated by loading different amounts of the Al element. The changes in structural and physicochemical properties following Al introduction were revealed by a series of characterizations. In-depth investigation of the catalytic mechanism through in situDRIFT combined with DFT calculations unveiled the synergistic effects between the Al and Cs components on the promotion of catalytic performance. The results showed that the presence of Al species facilitated the formation of weak and medium acid and base sites, along with the dispersion of the active Cs component, leading to an improvement of MMA yield. Additionally, the kinetic studies indicated that the addition of Al efficiently reduced the activation energy in comparison to pristine Cs/SiO2.

Electrochemical reduction (ECR) of CO2to C2H4has a potential key role in realizing the carbon neutral future, which ultimately relies on the availability of an efficient electrocatalyst that can exhibit a high Faradaic efficiency (FE) for C2H4production and robust, long-term operational stability. Here, for the first time, we report that upon applying reductive potential and electrolyte to the benchmark La2CuO4catalyst, surface reconstruction occurred, i.e., the appearance of a distinctive phase evolution process over time, which was successfully monitored using ex situ powder XRD and operando Mott–Schottky (M–S) measurements of La2CuO4samples that were soaked into the electrolyte and subjected to CO2-ECR for different durations. At the end of such a reconstruction process, an outermost layer consisting of lanthanum carbonate, a thin outer layer made of an amorphous Cu+material formed over the core bulk La2CuO4, as confirmed by various characterization techniques, which resulted in the redistribution of interfacial electrons and subsequent formation of electron-rich and electron-deficient interfaces. This contributed to the enhancement in FE for C2H4, reaching as much as 58.7%. Such surface reconstruction-induced electronic structure tuning gives new explanations for the superior catalytic performance of La2CuO4perovskite and also provides a new pathway to advance CO2-ECR technology.

ABSTRACTMainstream environmental politics tends to measure the success of a policy through its quantitative environmental performance. Under Xi's rule, China's model of ‘Authoritarian Environmentalism’ is often assessed for its effectiveness; however, the facets of policy legitimacy have largely been overlooked. This research explores the policy legitimacy deficit of China's authoritarian environmentalism by examining the case of ‘Ban on Coal Furnace’ (BCF) project in Hebei province. Drawing on extensive fieldwork and policy discourse analysis, this article suggests that while the BCF project succeeded in environmental performance, both in policy outputs and outcomes, it failed in maintaining policy legitimacy essential for sustaining these policy outcomes over the long term. Integrating the three distinctive aspects of policy outputs, outcomes and legitimacy, this study highlights an inherent Performance-Legitimacy dilemma within China's Authoritarian Environmentalism: on the one hand, the top leader's strong political will is crucial for achieving effective performance. On the other hand, however, policy legitimacy may suffer when local officials adopt an excessively coercive campaign-style implementation approach that overlooks local needs and interests.

In recent years, gesture perception has become crucial to human–computer interaction (HCI) technologies. Among various techniques, gesture recognition based on surface electromyography (sEMG) signals has gained significant prominence, with deep-learning methods playing a pivotal role in this domain. However, as the demand for accurate gesture recognition continues to rise, there is a growing inclination toward selecting complex deep neural network architectures. This trend, however, poses challenges in terms of performance and runtime requirements for computing devices. This article introduces a novel gesture recognition method utilizing the multilayer perceptron (MLP)-Mixer framework combined with Stacking ensemble learning to address these challenges. The proposed method effectively captures the features of sEMG data by employing simple MLPs, achieving a level of accuracy comparable to complex networks while simultaneously reducing inference time. Experimental results demonstrate that the method performs a classification accuracy of 80.03% and 81.13% for 49 actions in the open-source dataset NinaPro DB2, using window lengths of 200 and 300 ms, respectively. Furthermore, the method achieves a single inference speed of 54.77 ms with a window length of 200 ms. In NinaPro DB5, with window lengths of 250 and 300 ms, the method presented in this article achieves accuracy rates of 73.39% and 74.82%, respectively, completing inference in just 11.45 ms using the 300-ms window length. Notably, the technique also demonstrates its ability to mitigate the impact of individual differences in sEMG data on recognition accuracy.

Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level annotation in medical image segmentation tasks. Consistency learning, serving as a mainstream method in semi-supervised training, suffers from low efficiency and poor stability due to inaccurate supervision and insufficient feature representation. Prototypical learning is one potential and plausible way to handle this problem due to the nature of feature aggregation in prototype calculation. However, the previous works have not fully studied how to enhance the supervision quality and feature representation using prototypical learning under the semi-supervised condition. To address this issue, we propose an implicit-explicit alignment (IEPAlign) framework to foster semi-supervised consistency training. In specific, we develop an implicit prototype alignment method based on dynamic multiple prototypes on-the-fly. And then, we design a multiple prediction voting strategy for reliable unlabeled mask generation and prototype calculation to improve the supervision quality. Afterward, to boost the intra-class consistency and inter-class separability of pixel-wise features in semi-supervised segmentation, we construct a region-aware hierarchical prototype alignment, which transmits information from labeled to unlabeled and from certain regions to uncertain regions. We evaluate IEPAlign on three medical image segmentation tasks. The extensive experimental results demonstrate that the proposed method outperforms other popular semi-supervised segmentation methods and achieves comparable performance with fully-supervised training methods.

The loaded acid–base bifunctional catalysts exhibit good catalytic performance in the synthesis of methyl methacrylate via aldol condensation of methyl propionate with formaldehyde, but they still suffer from deactivation caused by coke deposition. In this paper, coke deposition as well as consequential deactivation behavior of Cs/SiO2catalyst loaded with various amounts of Al was investigated under different reactant compositions by using nuclear magnetic resonance, thermogravimetric, Brunauer–Emmett–Teller, and Raman analysis. As a result, coke on the catalyst was characterized by amorphous coke deposition, which was mainly composed of polycyclic aromatic hydrocarbons and aliphatic substances. Al added to the 10Cs/SiO2catalyst significantly reduced the amount of coke deposition and inhibited the C/H ratio of coke. Appropriate amounts of Al and reactant compositions could ultimately increase the stability of the catalyst. Moreover, the kinetics of coke deposition observed on the Al–Cs/SiO2catalyst was studied. It was found that the Voorhies equation modified using the variables of reaction temperature and reactants composition could accurately forecast coke accumulation on the 3.5Al-10Cs/SiO2catalyst under specific conditions.

The semi-supervised segmentation presents a promising approach for large-scale medical image analysis, effectively reducing annotation burdens while achieving comparable performance. This methodology holds substantial potential for streamlining the segmentation process and enhancing its feasibility within clinical settings for translational investigations. While cross-supervised training, based on distinct co-training subnetworks, has become a prevalent paradigm for this task, addressing critical issues, such as predication disagreement and label-noise suppression requires further attention and progress in cross-supervised training. In this article, we introduce an evidential tri-branch consistency learning framework (ETC-Net) for semi-supervised medical image segmentation. ETC-Net employs three branches: an evidential conservative branch (ECB), an evidential progressive branch (EPB), and an evidential fusion branch (EFB). The first two branches exhibit complementary characteristics, allowing them to address prediction diversity and enhance training stability. We also integrate uncertainty estimation from the evidential learning into cross-supervised training, mitigating the negative impact of erroneous supervision signals. In addition, the EFB capitalizes on the complementary attributes of the first two branches and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by more reliable and accurate pseudolabels of unlabeled data. Extensive experiments conducted on LA, Pancreas-CT, and automated cardiac diagnosis challenge (ACDC) datasets demonstrate that ETC-Net surpasses other state-of-the-art methods for semi-supervised segmentation. The code will be made available in the near future at: https://github.com/Medsemiseg.

For solving large inconsistent linear systems, we research a novel format to enhance the numerical stability and control the complexity of the model. Based on the idea of two subspace iterations, we propose the max-residual two subspace coordinate descent (M2CD) method. To accelerate the convergence rate, we further present the cyclic block coordinate descent (CBCD) method. The convergence properties of these methods are analyzed, and their effectiveness is illustrated by numerical examples.

Organic matter in soft coal (SC) and hard coal (HC) from the same mine was extracted by sequential thermal dissolution (TD) followed by oxidative depolymerisation (OD) to obtain soluble components, which were studied using both gas chromatography/mass spectrometry (GC/MS) and Orbitrap-MS to acquire detailed molecular structure information. Toluene (TOL), tetrahydrofuran (THF), and methanol (ME) were the three solvents used in sequential TD. TOL relaxes the macromolecular structure in coals and exhibits a good solubilisation effect on arenes, THF further destroys non-covalent bonds through swelling, and ME weakens hydrogen bonds among macromolecules and attacks the weak -C-O- bridged bonds. With a higher coalification degree, soluble components from HC show more aromatised structure compared to SC. After the OD treatment, aromatic compounds with a higher degree of condensation were detected in the OD products, and HC contains more fused aromatic rings. [Received: 13 March 2023; Accepted: 28 August 2023]

Tungsten (W), as a plasma-facing material, is subjected to high fluence plasma, resulting in both radiation damage and deuterium (D) retention. This study explores the blistering behavior and D supersaturated layers in both pre-damaged and undamaged W samples. To achieve a uniform damage profile similar to neutron irradiation, carbon (C) ions with multiple energies and angles were employed. The samples were further irradiated with low-energy D ions under two fluences at temperatures from ~ 380 to 640 K. Using various experimental techniques, we found that pre-damaged W samples can effectively suppress the formation of blistering and lower the threshold temperature at which the surface remains free of blistering. We also observed the prominent D supersaturated peaks within the depth of 20 nm in both pre-damaged and undamaged W. Furthermore, a close correlation between blistering and the D supersaturation was investigated. The results suggest that neutron irradiation in the fusion reactors could increase the critical D concentration for blistering, while a greater depth of damage facilitated the inward diffusion of D, thereby reducing the D concentration gradient and alleviating surface blistering.

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

• Bi-doped SFM double perovskite oxides are first proposed as SOEC cathodes. • Bi-doping can greatly improve CO 2 adsorption ability and oxygen ionic conduction. • BSFM01 cathode exhibits excellent CO 2 reduction performance. • BSFM01 cathode demonstrates good operational stability within 200 h. Electrochemical conversion of CO 2 to CO is an economically feasible method for mitigating greenhouse gas emissions. Among various electrochemical approaches, solid oxide electrolysis cells (SOECs) show high potential for CO 2 reduction reaction (CO 2 -RR) due to their ability to operate at high temperatures, resulting in fast reaction kinetics and increased efficiency. Considering their main energy loss is still associated with the large overpotential at the fuel electrode, the development of the highly efficient and durable cathode for SOECs has been extensively searched after. Here, we propose an A-site doping strategy to enhance the properties of Sr 2 Fe 1.5 Mo 0.5 O 6− δ (SFM), which improve its performance as a cathode in SOECs for CO 2 -RR, demonstrating favorable activity and durability. The structural and physiochemical characterizations, together with DFT calculations, show that the partial replacement of Sr by Bi in the SFM double perovskite not only improves CO 2 adsorption capability at the catalyst surface but also enhances oxygen ionic conduction inside the bulk oxide, resulting in enhanced CO 2 electrocatalysis performance in SOECs. Specifically, a La 0.8 Sr 0.2 Ga 0.8 Mg 0.2 O 3− δ (LSGM) electrolyte-supported single cell with the new Bi-doped SFM cathode demonstrates a large current density of 1620 mA cm−2 at a cell potential of 1.6 V at 850 °C with good operational stability up to 200 h. Bi-doped SFM thus represents a highly promising cathode for ceramic CO 2 electrolyzers and could accelerate our transition towards a carbon-neutral society. [Display omitted] [ABSTRACT FROM AUTHOR]

To quantify colloidal filtration, a quartz crystal microbalance (QCM) with a silicon dioxide surface is embedded on the inner surface of a microfluidic channel to monitor the real-time particle deposition. Potassium chloride solution with micrometer-size polystyrene particles simulating bacterial strains flows down the channel. In the presence of intrinsic Derjaguin–Landau–Verwey–Overbeek (DLVO) intersurface forces, particles are trapped by the quartz surfaces, and the increased mass shifts the QCM resonance frequency. The method provides an alternative way to measure filtration efficiency in an optically opaque channel and its dependence on the ionic concentration.

Two copper(I)-sulfide clusters were synergistically modified by alkylthiols and conjugated N,S-heterocyclic ligands, i.e., Cu6(tBuS)2(MBIZ)4(designated as Cu6) and Cu8(tBuS)4(MBIZ)4·CH3OH·2H2O (designated as Cu8) (MBIZ = 2-mercaptobenzimidazole; tBuS = tert-butyl mercaptan). Introducing more thiol ligands led to further growth of the cluster core, resulting in the transformation from an octahedral to a di-tetrahedral configuration, accompanied by a slight increase in the Cu···Cu distance. Notably, both clusters demonstrate high chemical stability and superhydrophobicity as a result of the modification with surface ligands, being stable at least 1 week in a wide range of pH (1–14 for Cu6and 3–11 for Cu8). Moreover, Cu8exhibited near-infrared (NIR) photoluminescence (PL) emission and better third-order nonlinear optical (NLO) performance, with nonlinear absorption coefficients (β) of 3.5 × 10–10m/W, 3.5 times than that of Cu6and superior to the majority of crystalline chalcogenide semiconductors. Theoretical calculations indicate that the organic ligands and cluster core jointly optimize the electronic configuration in a manner that regulates the optical properties. This work provides a new perspective into understanding the relationship between cluster core/surface compositions and optical properties at an atomically precise level, which is instrumental in the accurate design and synthesis of stable and efficient third-order NLO materials.

This article reviews the important advances in one- and zero-dimensional halide perovskites as new-generation passivators for high-efficiency and stable perovskite solar cells.

Remelting rejected electrolytic manganese metal (EMM) scrap was investigated by electroslag remelting (ESR) process through industrial experiment. The results indicated that the ANF-6 slag (70 wt.% CaF2+ 30 wt.% Al2O3) and deoxidizer could promote the desulfurization of ESR manganese in an air atmosphere. Under an air atmosphere, the sulfur in the ingot decreased to 0.0534 wt.% with a desulfurization ratio of ESR manganese of 53.2% by using ANF-6 slag and water-cooled copper electrode electroslag remelting rejected EMM scrap, suggesting its efficient removal. The electroslag ingots exhibited uneven chemical composition in an air atmosphere and cooling condition of the ESR process. The metal manganese could be oxidized by electroslag remelting of rejected EMM scrap in an air atmosphere with MnO content in the final slag of 21.9 wt.%. Besides, the activity of MnO in slag increased with increasing remelting temperature, resulting in a reduction in the slag–manganese sulfur partition ratio and desulfurization ratio. Moreover, with the accumulation of sulfur in slag and the oxidation of metallic manganese liquid, the slag showed a lower cleanliness and more oxidation, leading to an increase in sulfur and oxygen content in the electroslag ingot with the increase in ingot height.

The organic matter in Xiaolongtan lignite was thermally dissolved with cyclohexane, methanol and isopropanol (2-propanol) in sequence to obtain thermal dissolution (TD) extracts. Co-Mo/?-Al2O3was used as the catalyst for the catalytic hydrogenation of TD extracts to remove oxygen. Both TD extracts and the corresponding catalytic products were analyzed by the combination of gas chromatography/mass spectrometry (GC/MS) and Orbitrap MS to reveal more molecular details. GC/MS analysis shows that cyclohexane can extract low polar organic matter in coal, while methanol and isopropanol as nucleophilic reagents can break C-O bonds in coal, thus removing oxygen. The catalyst can trigger the fracture of alkyl chains and the activation of H2, thus promoting the removal of oxygen atom and the generation of aromatics. Double bond equivalent (DBE) value and carbon number (CN) were obtained through Orbitrap MS data. The decrease of both DBE and CN after catalytic conversion indicates the breaking of bridged bonds. The introduction of various MS systems provides an effective analytical tool for studying the catalytic deoxidation of lignite, thereby guiding the conversion of lignite to clean liquid fuels and high value-added chemicals.Co-Mo/?-Al2O3catalysts activates H2to H…H, which further splits into H+and H-. H…H deoxygenates by hydrogenating C=C bond, attacking C-O bond or directly attacking oxygen atom. H+will preferentially attack oxygen atom with high electronegativity, and break Cal-O bridge bond to achieve deoxygenation, thus reducing the relative abundance of oxygen containing compounds.

For the purpose of searching for new biological fluorophore, we assess the photophysical properties of two indole derivatives, 4-cyano-7-azaindole (4CN7AI) and 1-methyl-4-cyano-7-azaindole (1M4CN7AI), in a series of solvents. We find that (1) the absorption spectra of both derivatives are insensitive to solvents and are red-shifted from that of indole, having a maximum absorption wavelength of ca. 318 nm and a broad profile that extends beyond 370 nm; (2) both derivatives emit in the blue to green spectral range with a large Stokes shift, for example, in H2O, the maximum emission wavelength of 4CN7AI (1M4CN7AI) is at ca. 455 nm (470 nm); (3) 4CN7AI has a higher fluorescence quantum yield (QY) and a longer fluorescence lifetime (τF) in aprotic solvents than in protic solvents, for example, QY (τF) = 0.72 ± 0.04 (7.6 ± 0.8 ns) in tetrahydrofuran and QY (τF) = 0.29 ± 0.03 (6.2 ± 0.6 ns) in H2O; (4) in all of the solvents used except H2O, the fluorescence QY (τF) of 1M4CN7AI is equal to or higher (longer) than 0.69 ± 0.03 (11.2 ± 0.7 ns). Taken together, these results suggest that the corresponding non-natural amino acids, 4-cyano-7-azatryptophan and 1-methyl-4-cyano-7-azatryptophan, could be useful as biological fluorophores.

Micron-size spherical polystyrene colloidal particles are mechanically stretched to a prolate geometry with desirable aspect ratios. The particles in an aqueous medium with specific ionic concentration are then introduced into a microchannel and allowed to settle on a glass substrate. In the presence of unidirectional flow, the loosely adhered particles in the secondary minimum of surface interaction potential are easily washed off, but the remnant in the strong primary minimum preferentially aligns with the flow direction and exercises in-plane rotation. A rigorous theoretical model is constructed to account for filtration efficiency in terms of hydrodynamic drag, intersurface forces, reorientation of prolate particles, and their dependence on flowrate and ionic concentration.

Heavy metal ion pollution of water resources is becoming increasingly serious, and adsorption is one of the most effective strategies for removing heavy metal ions. In the paper, hydrated hydrogen sodium vanadium oxide (HNaV6O16⋅4H2O) film developed for heavy metal ion adsorption was prepared directly via a low-temperature liquid-phase deposition approach. The prepared film shows an interesting porous flower-like morphology and has large spacing (d= 10.87 Å). The highest adsorption capacity of the obtained HNaV6O16⋅4H2O film for Pb2+, Cu2+, Cd2+and Mn2+is 513 mg/g (2565 mg/m2), 430 mg/g (2150 mg/m2), 134 mg/g (875 mg/m2) and 175 mg/g (670 mg/m2), respectively. The adsorption percentage of the sample decreased from 92.2 to 86.3% after 4 cycles. The adsorption process follows the Langmuir adsorption isotherm and the pseudo second-order dynamic model, indicating that heavy metal ion adsorption by the film is a single molecular layer chemical adsorption. In combination with various characterizations and comparison tests of samples after adsorption, the adsorption mechanisms include surface electrostatic attraction, complexation, and cation exchange. The results indicate that the film is a potential material to remove heavy metal ions from the aqueous solution.

Recognizing facial expressions is crucial for adaptive social interaction. Prior empirical research on facial expression processing has primarily focused on isolated faces; however, facial expressions appear embedded in surrounding scenes in everyday life. In this study, we attempted to demonstrate how the online car‐hailing scene affects the processing of facial expression. This study examined the processing of drivers' facial expressions in scenes by recording event‐related potentials, in which neutral or happy faces embedded in online car‐hailing orders were constructed (with type of vehicle, driver rating, driver surname, and level of reputation controlled). A total of 35 female volunteers participated in this experiment and were asked to judge which facial expressions that emerged in scenes of online car‐hailing were more trustworthy. The results revealed an interaction between facial expression scenes, brain areas, and electrode sites in the late positive potential, which indicated that happy faces elicited larger amplitudes than did neutral ones in the parietal areas and that scenes with happy facial expressions had shorter latencies than did those with neutral ones. As expected, the late positive potential evoked by happy facial expressions in a scene was larger than that evoked by neutral ones, which reflected motivated attention and motivational response processes. This study highlights the importance of scenes as context in the study of facial expression processing.