University of Shanghai for Science and Technology

Hydrogen production via seawater electrolysis is limited by chloride corrosion and slow oxygen evolution reaction (OER) kinetics. Here, we present hydroxyl network‐engineered NiFe hydroxide catalysts on stainless steel (SS‐NiFe‐X) via a rapid one‐step electrodeposition strategy. During OER, the NiFe hydroxide layer transforms into an active NiFeOOH/NiOOH phase, while in situ‐generated surface hydroxyl networks establish hydrogen‐bond‐mediated pathways that simultaneously enhance OER activity and shield against chloride attack. SS‐NiFe‐60, with a 500 nm oxide layer, sustains 400 mA cm⁻² for over 500 h in an aggressive chloride environment (1.0 M KOH + 2.0 M NaCl), while the bare SS experiences complete deactivation within 1 h. Operando studies reveal that the hydroxyl network could block chloride penetration by electrostatic repulsion and facilitate OER intermediate adsorption, validated by a membrane electrode assembly electrolyzer stably delivering 250 mA cm⁻² for over 100 h. This scalable design bridges mechanistic insights with industrial seawater electrolysis applications.

Liquid-fueled molten-salt reactors have dynamic features that distinguish them from solid-fueled reactors, such that conventional system-analysis codes are not directly applicable. In this study, a coupled dynamic model of the Molten-Salt Reactor Experiment (MSRE) is developed. The coupled model includes the neutronics and single-phase thermal-hydraulics modeling of the reactor and validated xenon-transport modeling from previous studies. The coupled dynamic model is validated against the frequency-response and transient-response data from the MSRE. The validated model is then applied to study the effects of xenon and void transport on the dynamic behaviors of the reactor. Plant responses during the unique initiating events such as off-gas system blockages and loss of circulating voids are investigated.

Matrix optimization has various applications in finance, statistics, and engineering, etc. In this paper, we derive the Lagrangian dual of the matrix optimization problem with sparse group lasso regularization, and develop an adaptive gradient/semismooth Newton algorithm for this dual. The algorithm adaptively switches between semismooth Newton and gradient descent iterations, relying on the decrease of the residuals or values of the dual objective function. Specifically, the algorithm starts with the gradient iteration and switches to the semismooth Newton iteration when the residual decreases to a given threshold value. If the trial step size for the semismooth Newton iteration has been shrunk several times or the residual does not decrease sufficiently, the algorithm switches back to the gradient iteration and reduces the threshold value for invoking the semismooth Newton iteration. Under some mild conditions, the global convergence of the proposed algorithm is proved. Moreover, local superlinear convergence is achieved under one of two scenarios: either when the constraint nondegeneracy condition is met, or when both the strict complementarity and the local error bound conditions are simultaneously satisfied. Some numerical results on synthetic and real data sets demonstrate the efficiency and robustness of our proposed algorithm.

Photovoltaic energy development has effectively mitigated energy crises and accelerated global carbon neutrality efforts. However, the increasing photovoltaic installed capacity poses significant challenges to grid scheduling systems. Photovoltaic power forecasting techniques provide crucial basis to formulate scheduling plans, thereby alleviating scheduling pressures. Yet, existing photovoltaic power prediction algorithms have shown unstable performance in complex weather conditions. Therefore, this paper proposes a short-term photovoltaic power prediction method based on the nearest clear sky day decomposition and temporal convolutional network (TCN). The method identifies the photovoltaic output on the nearest clear sky day to the target day and decomposes the photovoltaic power waveform based on the clear sky component removal. TCN combines the feature extraction capabilities of convolutional neural networks (CNNs) with the temporal information mining abilities of sequence-based neural networks like recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), making it suitable for capturing the relationships between various meteorological features and photovoltaic output. Using the Alice Springs dataset in Australia as a case study, the algorithm conducts experiments under different seasons and weather conditions, comparing its performance against other models using metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2).

Let G be a simple connected graph on n vertices. The degree of a vertex v\in V\left(G) , denoted by {d}_{v} , is the number of edges incident with v and the distance between any two vertices u,v\in V\left(G) , denoted by {d}_{uv} , is defined as the length of the shortest path from u to v . The distance matrix of G , denoted by, D\left(G) , is defined as D\left(G)={d}_{uv} . We now define and investigate the degree distance matrix of a connected graph G , defined as {M}_{DD}\left(G)={\left(\left({d}_{u}+{d}_{v}){d}_{uv})}_{u,v\in V\left(G)} . In this article, first, we derive bounds for the largest eigenvalue of the degree distance matrix. Then, we establish some bounds for the energy of the degree distance matrix of G .

This paper deals with the critical Choquard equation with a Kirchhoff type perturbation. Under suitable assumptions on parameters, we prove the existence and multiplicity of positive solutions.

High-purity, monochiral single-walled carbon nanotubes (SWCNTs) are essential for advancing high-performance nano-optoelectronic devices. Nonetheless, conventional methods for chirality separation of SWCNTs face challenges, including low yield, poor solution stability, and inefficient film formation processes. This study revealed that the yield of monochiral (6,5) SWCNTs can be significantly enhanced by increasing solution viscosity within a low-polar solvent. Employing tetralin, a low-polarity and high-viscosity solvent, the concentration of the dispersion was enhanced by a factor of 19 relative to that in toluene, attaining the highest reported extraction yield of (6,5) SWCNTs with PFO-BPy. Additionally, a mixed-solvent strategy combining toluene and tetralin has been employed to achieve a balance between concentration and purity for (6,5) SWCNTs. Furthermore, the use of a high-viscosity solvent bolstered the stability duration of chiral SWCNT dispersions, as well as the deposition rate of SWCNT films, leading to superior chiral (6,5) SWCNT films. Field-effect transistors (FETs) fabricated using these films exhibited excellent performance, highlighting their substantial potential for use in high-performance electronic devices.

Due to non-structural factors, such as reflections on the surfaces of metal objects, fragmented depth images are often generated, posing significant challenges to 6D pose estimation tasks. To address these challenges, we propose the first cascade framework specifically designed for 6D pose estimation of texture-less and highly reflective metal parts. The proposed framework consists of three main stages: instance segmentation, point cloud shape recovery, and pose estimation. During the training phase, a synthetic dataset of metal parts is constructed as the training set. During the testing phase, real scene metal part data is collected as the testing dataset. To enhance pose estimation accuracy, a multi-scale residual self-attention point cloud shape recovery network is designed to address the issue of fragmented point clouds in metal parts. Finally, a late-stage feature fusion network based on RGB-D data is developed for 6D pose estimation. By removing erroneous point cloud information and restoring the accurate 3D structure of the point cloud, the proposed method achieves better alignment of point cloud and image features during the bidirectional fusion process, effectively reducing pose estimation errors for metal parts. Code and video are available at https://github.com/xiao-wang-han/PCR-PoseNet.

In this paper, we consider the modified Schrödinger equation where $V: {{\mathbb {R}}}^N\rightarrow {{\mathbb {R}}}$ is a continuous potential allowed to change sign and $\gamma >0$ is a real parameter. This equation is related to standing wave solutions of a class of quasilinear Schrödinger equations which is used for describing the superfluid film equations in plasma physics and has attracted much attention in recent years. It follows from recent many studies that $p\ge 4$ plays a key role to apply mountain pass theorem. By truncation technique and variational methods, our study establish the existence of multiple solutions for $p\in (2,\frac{2N}{N-2})$ with $N\ge 4$ and $p\in (2,4)$ with N=3.

Developing a highly efficient and earth-abundant electrocatalyst was crucial for enhancing the alkaline hydrogen evolution reaction (HER) and accelerating the sluggish hydrolysis kinetics. In this study, a novel self-supporting Co/MoC/Mo2C/SCG@SA electrocatalyst derived from biomass carbon was successfully synthesized via a simple hydrothermal and calcination process. The Co/MoC/Mo2C composite exhibited outstanding electrocatalytic performance, achieving a current density of 10 mA cm⁻² at a low overpotential of 70 mV for HER. The synergistic effect between the MoC/Mo2C and MoC/Co heterojunctions played a crucial role in facilitating charge transfer and promoting the reaction. This work provided a feasible strategy for the rational design of self-supporting biomass-based carbon electrodes combined with transition metal materials. Graphical abstract

Laser powder bed fusion (LPBF) is a widely used and well-developed approach in additive manufacturing. To meet the high material performance requirements of fourth-generation nuclear power reactors, the combination of LPBF processing with oxide dispersion strengthening (ODS) is currently of interest for the design and development of new materials. In this approach, nanoscale $\hbox {Y}_{2}\hbox {O}_{3}$ particles are dispersed into the feeding powders to produce LPBF-ODS materials. Oxygen exposure and the introduction of oxygen into the solvation cell during LPBF are usually considered as detrimental processes that are impossible to eliminate completely. However, our understanding of these unavoidable processes is still limited. In this study, we developed a new LPBF-ODS design approach based on in situ oxygen content regulation during the LPBF process. The oxygen content of the environmental chamber was artificially adjusted using an online monitoring system to activate reactions between oxygen and the metallic elements for the in situ formation of dispersed oxide particles. Four batches of LPBF 304 L stainless steel samples were successfully processed under different oxygen levels to investigate the reinforcement effect of in situ chemical alloying. The results show that dispersed oxide particles were formed with an average nanoscale size of approximately $46 \mathrm {~nm}$ through the LPBF in situ alloying approach. The increase in the number density of oxide particles to 11.4 particles / \upmu \textrm{m}^{2} as the oxygen content increased played a role in refining and stabilizing the cellular structure. The yield strength of the in situ alloyed ODS material was enhanced (to up to $\sim 675~\textrm{MPa}$) while its ductility was not significantly degraded (elongation of up to $\sim 39 \%$). These tensile properties are competitive within the ranges reported for ODS alloys prepared by mechanical alloying. The main mechanisms for yield strength enhancement through interactions between nanoscale oxide particles and dislocation entanglement cells were analyzed. This study provides a new approach for the future preparation of high-performance LPBF-ODS alloys.

Nacre has become the golden standard for the structural design of high‐performance composites due to extraordinary fracture toughness, which exceeds the mixing principle of traditional composites by two orders of magnitude. Surprisingly, the unique biomaterials are formed under ambient temperature and pressure conditions, resulting in low energy consumption and no pollution. It is an effective approach to obtain inspiration from structure‐activity relationships of biomaterials for developing the next‐generation of high‐performance composites. Furthermore, 2D carbon nanomaterials, such as graphene and MXene, having exceptional mechanical and electrical properties, are ideal candidates for fabricating new generation high‐performance composites that would replace carbon fiber (CF) composites. This review systematically summarizes relevant works for high‐performance 2D carbon nanocomposites (TDCNs) inspired by nacre. The review first explores structural insights from the nacre. Next, the fabrication strategies of TDCNs are systematically summarized, with an emphasis on achieving highly aligned 2D carbon nanosheets through advanced assembly techniques. Subsequently, the critical role of void defects, which is a key factor governing the mechanical properties of TDCNs, is addressed by analyzing their formation mechanisms, characterization methodologies, and elimination strategies. Finally, the applications and challenges of high‐performance TDCNs obtained through highly aligned assembly and densification processes are discussed.

Owing to sensitive nonlinear plasmonic responses, anisotropic metal nanoparticles are effective nanoprobes for optical imaging. However, the photo-thermal instability issues have hindered their further nanophotonics application potentials. In this letter, we reported the super-resolution imaging and optical memory by suppressing the plasmonic scattering signal of core-shell gold nanorods (GNRs). Good thermal stability and conductivity from GNRs coated with 20 nm silica shell supported the super-resolution imaging with a lateral feature size of 114 nm (λ/5.6) via a very low suppressing laser power (0.9 mW). The GNRs were then employed for achieving super-resolved bioimaging with a feature size of 128 nm (λ/5). More importantly, we were able to realize the super-resolution optical recording (feature size: 173 nm) in a GNRs-polyvinyl alcohol sample. This work further extends our understanding of the nanophotonics application of core-shell anisotropic metal nanoparticles based on nonlinear plasmonic scattering.

Background As life expectancy rises, age-related decline in mobility and physical function poses challenges for older adults. While traditional exercise can help, limitations and injury risks persist. This study explores low-frequency vibration training as a potential alternative to improve walking ability and body composition in older adults. Methods A lottery was used to randomly assign 50 participants (mean age 80.08 years) to either a vibration group ( n = 25, 10 males, 15 females) or a control group ( n = 25, 11 males, 14 females). While the control group continued their regular daily schedule, the vibration group completed 8 weeks of low-frequency vibration training (frequency: 4–13 Hz; amplitude: two mm), three sessions per week, with each session lasting 20–30 minutes. The walk ability was assessed using the 30-second Chair Stand Test (30-s CST), Timed Up and Go (TUG), and six-meter (six m) walk speed, while body composition was measured via body mass index (BMI), body fat percentage, and waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Results Low-frequency vibration training significantly increased walking speed in the six m walk speed ( F (1,36) = 4.50, p = 0.04, η p ² = 0.11) and TUG ( z = − 2.72, p = 0.007), compared with the control group. Observed improvements on the 30-s CST were not statistically significant ( F (1,36) = 0.05, p = 0.81, η p ² = 0.002). In the WC, the effect of time ( F (1,36) = 7.19, p = 0.01, η p ² = 0.16) was significant. The main effect of the group for HC ( F (1,36) = 0.06, p = 0.80, η p ² = 0.002) and WHR ( F (1,36) = 2.00, p = 0.16, η p ² = 0.05) were not significant, but the interaction effects for HC ( F (1,36) = 6.37, p = 0.01, η p ² = 0.15) and WHR ( F (1,36) = 9.08, p = 0.005, η p ² = 0.20) were significant. However, the intervention showed no statistically significant effects on BMI and body fat percentage. Conclusion Low-frequency vibration training significantly enhanced walking speed and WHR in older adults. This low-intensity intervention is especially beneficial for those with exercise limitations or a high risk of injury. Although its effects on BMI and body fat percentage were limited, the study offers valuable insights for developing personalized vibration training programs.