Spectroscopically clean Au nanoparticles for catalytic decomposition of hydrogen peroxide

Cytotoxicity evaluation of organophosphorus flame retardants using electrochemical biosensors and elucidation of associated toxic mechanisms

In recent years, organophosphorus flame retardants (OPFRs) have been widely used as substitutes for brominated flame retardants with excellent properties, and their initial toxicological effects on the water ecosystem and human health have gradually emerged. However, to date, research on the cytotoxicity and health risks of OPFRs is still limited. Therefore, this study aims to systematically explore the cytotoxic effects and toxic mechanisms of OPFRs on cells. Human liver cancer (HepG2) cells were adopted as an ideal model for toxicity evaluation due to their rapid growth and metabolism. This study proposes a sensitive electrochemical cell-based sensor constructed on a graphitized multi-walled carbon nanotube/ionic liquid/gold nanoparticle-modified electrode. The sensor was used to detect the cytotoxicity of tri(2-butylxyethyl) phosphate (TBEP), tributyl phosphate (TnBP), triphenyl phosphate (TPhP), tri(1,3-dichloro-2-propyl) phosphate (TDCIPP), tri(2-chloropropyl) phosphate (TCPP) and tri(2-chloroethyl) phosphate (TCEP) in the liquid medium, providing insight into their toxicity in water environments. The half-maximal inhibitory concentration (IC50) of TBEP, TnBP, TPhP, TDCIPP, TCPP and TCEP on HepG2 cells were 179.4, 194.9, 219.8, 339.4, 511.8 and 859.0 μM, respectively. Additionally, the cytotoxic mechanism of six OPFRs was discussed from the perspective of oxidative stress and apoptosis, and four indexes were correlated with toxicity. Furthermore, transcriptome sequencing was conducted, followed by a thorough analysis of the obtained sequencing results. This analysis demonstrated a significant enrichment of the p53 and PPAR pathways, both of which are closely associated with oxidative stress and apoptosis. This study presents a simplified and efficient technique for conducting in vitro toxicity studies on organophosphorus flame retardants in a water environment. Moreover, it establishes a scientific foundation for further investigation into the mechanisms of cytotoxicity associated with these compounds.

Hydrogen on Colloidal Gold Nanoparticles

Colloidal gold nanoparticles (AuNPs) have myriad scientific and technological applications, but their fundamental redox chemistry is underexplored. Reported here are titration studies of oxidation and reduction reactions of aqueous AuNP colloids, which show that the AuNPs bind substantial hydrogen (electrons + protons) under mild conditions. The 5 nm AuNPs are reduced to a similar extent with reductants from borohydrides to H2 and are reoxidized back essentially to their original state by oxidants, including O2. The reactions were monitored via surface plasmon resonance (SPR) optical absorption, which was shown to be much more sensitive to surface H than to changes in solution conditions. Reductions with H2 occurred without pH changes, demonstrating that hydrogenation forms surface H rather than releasing H+. Computational studies suggested that an SPR blueshift was expected for H atom addition, while just electron addition likely would have caused a redshift. Titrations consistently showed a maximum redox change of the 5 nm NPs, independent of the reagent, corresponding to 9% of the total gold or ∼30% hydrogen surface coverage (∼370 H per AuNP). Larger AuNPs showed smaller maximum fractional surface coverages. We conclude that H binds to the edge, corner, and defect sites of the AuNPs, which explains the stoichiometric limitation and the size effect. The finding of substantial and stable hydrogen on the AuNP surface under mild reducing conditions has potential implications for various applications of AuNPs in reducing environments, from catalysis to biomedicine. This finding contrasts with the behavior of bulk gold and with the typical electron-focused perspective in this field.

Motion sensitive network for action recognition in control and decision-making of autonomous systems

Spatial-temporal modeling is crucial for action recognition in videos within the field of artificial intelligence. However, robustly extracting motion information remains a primary challenge due to temporal deformations of appearances and variations in motion frequencies between different actions. In order to address these issues, we propose an innovative and effective method called the Motion Sensitive Network (MSN), incorporating the theories of artificial neural networks and key concepts of autonomous system control and decision-making. Specifically, we employ an approach known as Spatial-Temporal Pyramid Motion Extraction (STP-ME) module, adjusting convolution kernel sizes and time intervals synchronously to gather motion information at different temporal scales, aligning with the learning and prediction characteristics of artificial neural networks. Additionally, we introduce a new module called Variable Scale Motion Excitation (DS-ME), utilizing a differential model to capture motion information in resonance with the flexibility of autonomous system control. Particularly, we employ a multi-scale deformable convolutional network to alter the motion scale of the target object before computing temporal differences across consecutive frames, providing theoretical support for the flexibility of autonomous systems. Temporal modeling is a crucial step in understanding environmental changes and actions within autonomous systems, and MSN, by integrating the advantages of Artificial Neural Networks (ANN) in this task, provides an effective framework for the future utilization of artificial neural networks in autonomous systems. We evaluate our proposed method on three challenging action recognition datasets (Kinetics-400, Something-Something V1, and Something-Something V2). The results indicate an improvement in accuracy ranging from 1.1% to 2.2% on the test set. When compared with state-of-the-art (SOTA) methods, the proposed approach achieves a maximum performance of 89.90%. In ablation experiments, the performance gain of this module also shows an increase ranging from 2% to 5.3%. The introduced Motion Sensitive Network (MSN) demonstrates significant potential in various challenging scenarios, providing an initial exploration into integrating artificial neural networks into the domain of autonomous systems.

Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning

The development of education robots has brought tremendous potential and opportunities to the field of education. These intelligent machines can interact with students in classrooms and learning environments, providing personalized educational support. To enable education robots to fulfill their roles, they require accurate object detection capabilities to perceive and understand the surrounding environment of students, identify targets, and interact with them. Object detection in complex environments remains challenging, as classrooms or learning scenarios involve various objects, backgrounds, and lighting conditions. Improving the accuracy and efficiency of object detection is crucial for the development of education robots. This paper introduces the progress of an education robot's object detection based on a brain-inspired heuristic method, which integrates Faster R-CNN, YOLOv3, and semi-supervised learning. By combining the strengths of these three techniques, we can improve the accuracy and efficiency of object detection in education robot systems. In this work, we integrate two popular object detection algorithms: Faster R-CNN and YOLOv3. We conduct a series of experiments on the task of education robot object detection. The experimental results demonstrate that our proposed optimization algorithm significantly outperforms individual algorithms in terms of accuracy and real-time performance. Moreover, through semi-supervised learning, we achieve better performance with fewer labeled samples. This will provide education robots with more accurate perception capabilities, enabling better interaction with students and delivering personalized educational experiences. It will drive the development of the field of education robots, offering innovative and personalized solutions for education.

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.

Au-on-Ag nanostructure forin-situSERS monitoring of catalytic reactions

Dual-functionality Au-on-Ag nanostructures (AOA) were fabricated on a silicon substrate by first immobilizing citrate-reduced Ag nanoparticles (Ag NPs, ∼43 nm in diameter), followed by depositing ∼7 nm Au nanofilms (Au NFs) via thermal evaporation. Au NFs were introduced for their catalytic activity in concave-convex nano-configuration. Ag NPs underneath were used for their significant enhancement factor (EF) in surface-enhanced Raman scattering (SERS)-based measurements of analytes of interest. Rhodamine 6G (R6G) was utilized as the Raman-probe to evaluate the SERS sensitivity of AOA. The SERS EF of AOA is ∼37 times than that of Au NPs. Using reduction of 4-nitrothiophenol (4-NTP) by sodium borohydride (NaBH₄) as a model reaction, we demonstrated the robust catalytic activity of AOA as well as its capacity to continuously monitor via SERS the disappearance of reactant 4-NTP, emergence and disappearance of intermediate 4,4'-DMAB, and the appearance of product 4-ATP throughout the reduction process in real-time andin situ.

The enzymatic performance derived from the lattice planes of Ir nanoparticles

DFT calculations reveal that the versatile enzyme-like properties of IrNPs are directly related to their crystal planes. The catalase-like activity originates from the Ir(111) plane, while the oxidase-like activity is intrinsic to the Ir(220) plane.