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The interaction between Ir and La via O atoms as a bridge in a-IrOx/LaCO3OH boosts the acidic OER activity of a-IrOx.

Tridoped cross-linked 3D porous carbon materials as advanced trifunctional metal-free electrocatalysts for the ORR, OER and HER.

A Zn2SnO4/ZnO nanocomposite is developed for effectively catalyzing the reduction of CO2 to formic acid, owning to the potential capacity to regulate electronic structure by interfacial interaction.

For high-efficiency NH3 synthesis via ambient-condition electrohydrogenation of inert N2, it is pivotal to ingeniously design an active electrocatalyst with multiple features of abundant surfacial deficiency, good conductivity and large surface area. Here, oxygen-deficient SnO2 nanoparticles encapsulated by ultrathin carbon layer (d-SnO2@C) are developed by hydrothermal deposition coupled with annealing process, as promising catalysts for ambient electrocatalytic N2 reduction. d-SnO2@C exhibits high activity and excellent selectivity for electrocatalytic conversion of N2 to NH3 in acidic electrolytes, with Faradic efficiency as high as 12.7% at −0.15 V versus the reversible hydrogen electrode (RHE) and large NH3 yield rate of 16.68 μg h−1 mgcat−1 at −0.25 V vs. RHE in 0.1 M HCl. Benefiting from the structural superiority of enhanced charge transfer efficiency and optimized surface states, d-SnO2@C also achieves excellent long-term stability.

Exploring the transition-metal-based bifunctional electrocatalysts with high performance for efficient water-splitting and urea electrolysis is significant but challenging. This work presents the in situ preparation of holey NiFe-LDH nanosheets on Ni foam (H-NiFe-LDH/NF) via a one-step hydrothermal method in the presence of PEO-PPO-PEO as the soft template. The holey NiFe-LDH nanosheets provide a high electrochemical surface area, more edge catalytic sites, and abundant oxygen vacancies. Consequently, H-NiFe-LDH/NF exhibits excellent catalytic activity to oxygen evolution, urea oxidation, and hydrogen evolution reactions (OER, UOR, and HER) with good stability in alkaline electrolytes. This electrode requires an overpotential of 261 mV for the OER, a potential of 1.480 V for the UOR to achieve a current density of 100 mA cm

The IrO2/LiLa2IrO6 electrocatalyst achieves a win–win situation for both activity and stability towards the OER in acidic media, attributed to the in situ formed IrOx and strong interaction between IrOx and bulk LiLa2IrO6.

The prepared double mixed metal oxide BaIrO2.937/La3IrO7 with a surface of IrOx formed by Ba and La leaching exhibits excellent performance for boosting the OER in acidic media.

It is established that ultrathin layered double hydroxide nanosheets (LDHNS) and zeolitic imidazole frameworks (ZIF) are desirable electrochemical sensing modifiers owing to their large surface area and abundant catalytic sites. Integration of them is thus an effective solution to maximize their electrocatalytic activity. Herein, a novel reaction–diffusion framework (RDF) technique is applied for the in situ growth of ZIF-67 on ultrathin CoAl-LDHNS (CoAl-LDHNS@ZIF-67). In a confined space of the agar gel matrix of RDF, the coordination reaction between organic ligands and CoAl-LDHNS without an additional Co2+ source achieves the controllable growth of ZIF-67 crystals through a long vertical diffusion. The prepared composite comprises both CoAl-LDHNS and ZIF-67 components with a certain ratio and provides a large surface area and amply catalytic sites, thus realizing a rapid transfer of electron and mass. The CoAl-LDHNS@ZIF-67 modified electrode is employed for the simultaneous detection of naphthol isomers by differential pulse voltammetry. Naphthol isomers display anodic reactions with a wide peak potential difference, allowing their simultaneous detection feasible. Voltammetric responses of α-naphthol and β-naphthol follow good linearity against the concentration in a wide range from 0.3 to 150 μM with limits of detection of 54 and 82 nM, respectively. The proposed sensor also demonstrates excellent selectivity, stability, reproducibility, and practicability for the simultaneous detection of naphthol isomers.

It is imperative but challenging to develop non-noble metal-based bifunctional electrocatalysts for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Our work reports a core–shell nanostructure that is constructed by the electrodeposition of ultrathin NiFe-LDH nanosheets (NiFe-LDHNS) on Cu2Se nanowires, which are obtained by selenizing Cu(OH)2 nanowires in situ grown on Cu foam. The obtained Cu2Se@NiFe-LDHNS electrocatalyst provides more exposed edges and catalytic active sites, thus exhibiting excellent OER and HER electrocatalytic performance in alkaline electrolytes. This catalyst needs only an overpotential of 197 mV for OER at 50 mA cm−2 and 195 mV for HER at 10 mA cm−2. Besides, when employed as a bifunctional catalyst for overall water-splitting, it requires a cell voltage of 1.67 V to reach 10 mA cm−2 in alkaline media. Furthermore, the corresponding water electrolyzer demonstrates robust durability for at least 40 h. The excellent performance of Cu2Se@NiFe-LDHNS might be ascribed to the synergistic effect from the ultrathin NiFe-LDHNS, the Cu2Se nanowires anchored on the Cu foam, and the formed core–shell nanostructure, which offers large surface area, ample active sites, and sufficient channels for gas and electrolyte diffusion. This work provides an efficient strategy for the fabrication of self-supported electrocatalysts for efficient overall water-splitting.

A 2D-2D titanium dioxide nanosheet-reduced graphene oxide (TNS-rGO) composite with better electronic conductivity and hydrophilicity was prepared by the hydrothermal method. The as-obtained TNS-rGO composite was further functionalized with 3-aminopropyltriethoxysilane (APTES) to provide a large amount of –NH2 groups on the surface for anchoring ultrafine PdAg alloy nanoparticles with an average particle size of 1.69 nm by a facile wet reduction approach. Benefiting from the combined effects of well-dispersed PdAg alloy nanoparticles, facilitated electron transfer from TNS-rGO to Pd, and increased electron density of active sites, the Pd8Ag1/NH2-TNS-rGO catalyst exhibited excellent activity towards dehydrogenation of formic acid without adding any additives at 298 K, corresponding to an initial turn over frequency as high as 1090 h−1, which is much higher than that of most other state-of-the-art catalysts.

The oxygen evolution reaction (OER) is a key reaction for many electrochemical devices. To date, many OER electrocatalysts function well in alkaline media, but exhibit poor performances in neutral and acidic media, especially the acidic stability. Herein, sodium-decorated amorphous/crystalline RuO 2 with rich oxygen vacancies (a/c-RuO 2 ) was developed as a pH-universal OER electrocatalyst. The a/c-RuO 2 shows remarkable resistance to acid corrosion and oxidation during OER, which leads to an extremely high catalytic stability, as confirmed by a negligible overpotential increase after continuously catalyzing OER for 60 h at pH = 1. Besides, a/c-RuO 2 also exhibits superior OER activities to commercial RuO 2 and most reported OER catalysts under all pH conditions. Theoretical calculations indicated that the introduction of Na dopant and oxygen vacancy in RuO 2 weakens the adsorption strength of the OER intermediates by engineering the d -band center, thereby lowering the energy barrier for OER.

Purpose Accurately delineating clinical target volumes (CTV) is essential for completing radiotherapy plans but is time-consuming, labor-intensive, and prone to inter-observer variation. Automating CTV delineation has the benefits of both speeding up contouring process and improving the quality of contours. Recently, auto-segmentation approaches based on deep learning have achieved some improvements. However, unlike organ segmentation, the CTV contains potential tumor spread tissues or subclinical disease tissues, resulting in poorly defined margin interface and irregular shape. It is not reasonable to directly apply the deep learning segmentation algorithms to CTV tasks without considering the unique characteristics of shape and margin. In this work, we propose a novel automatic CTV delineation algorithm based on deep learning addressing the unique shape and margin challenges. Methods Our deep learning method, called RA-CTVNet, segments the CTV from cervical cancer CT images. RA-CTVNet denotes our automatic CTV delineation algorithm based on deep learning with Area-aware reweight strategy and Recursive refinement strategy. (1) In order to process the whole-volume CT images and delineate all CTVs in one shot, our method is built upon the popular 3D Unet architecture. We further extend it with robust residual learning and squeeze-and-excitation blocks for better feature representation. (2) We propose area-aware reweight strategy which assigns different weights for different slices. The core is adjusting model's attention to each slice. (3) In terms of the trade-off between providing performance improvements and meeting the limitations of GPU memory, we exploit a new recursive refinement strategy to address margin challenge. Results This retrospective study included 462 patients diagnosed with cervical cancer who received radiotherapy from June 2017 to May 2019. Extensive experiments were conducted to evaluate performance of RA-CTVNet. First, compared to different network architectures, RA-CTVNet achieved improvements in Dice similarity coefficient (DSC). Second, we conducted ablation study. The results showed that compared to the backbone, area-aware reweight strategy increased DSC by 3.3% on average and recursive refinement strategy further increased DSC by 1.6% on average. Then, we compared our method with three human experts. Our RA-CTVNet performed better than two experts while comparably to the third expert. Finally, a multicenter evaluation was conducted to verify the accuracy and generalizability. Conclusions Our findings show that deep learning is able to offer an efficient framework for automatic CTV delineation. The tailored RA-CTVNet can improve the quality of CTV contours, which has great potential for reducing the burden of experts and increasing the accuracy of delineation. In the future, if with more training data, further improvements are possible, bringing this approach closer to real clinical practice.

Amorphous iridium oxides (IrOx) are highly active for oxygen evolution reaction (OER) in acid media; however, it is generally unstable compared with commercial IrO2. Recently, many non-noble metal-iridium mixed oxides are prepared for catalyzing OER efficiently. Herein, we report a cubic fluorite-type praseodymium iridium oxide with the surface of IrOx (IrOx/Pr3IrO7) that shows the improved activity and stability in 0.1 mol L−1 HClO4 solution, characterized by an overpotential of 305 mV at the benchmark of 10 mA cm−2 and a small Tafel slope of 37 mV dec−1, indicating a fast reaction kinetics and a competitive activity compared with the benchmark IrO2 and most reported electrocatalysts. The initial potential increases by less than 0.07 V after continuous OER testing over 60,000 s. In contrast, IrO2 becomes nearly inactive for the OER within 20,000 s. Density functional theory calculations uncover that the optimal energy level path follows lattice oxygen mechanism (LOM). This work enlarges the family of the IrOx-type OER electrocatalyst in acid media.