Phosphorus modified transition metal electrocatalysts for efficient electrolysis of water

A variety of approaches to produce hydrogen, such as electrolysis, photo-electrolysis and gasification, have been explored over the past decade. Among these methods, the electrolysis of water, which consists of cathodic hydrogen evolution reaction (HER) and anodic oxygen evolution reaction (OER), was intensively studied due to its advantages of easy-to-obtain reactants, stable high-purity outputs, and feasible large-scale production processes. However, it remains a challenge to develop high-performance, stable and cost-effective electrocatalysts for HER and OER. Here, this thesis aims at design and development of transition metal-based nanostructured catalysts for electrolysis of water. Step-by-step research was carried out by firstly investigating efficient electrocatalysts for HER in alkaline media. Then high-efficiency catalysts for HER in neutral media were designed and developed. Finally, electrocatalysts for OER in alkaline media were developed by a facile room-temperature wet chemistry method. The details of these three main PhD projects are as follows: (1) To improve the HER performance of transition metal oxides in alkaline media, Phosphorus modified monoclinic β-CoMoO4 as a low-cost, efficient and stable HER electrocatalyst for the electrolysis of water in alkaline media was fabricated. The optimized catalyst shows a small overpotential of 94 mV to reach a current density of 10 mA cm−2 for the HER with high stability in KOH electrolyte, and an overpotential of 197 mV to reach a current density of 100 mA cm−2.Combined computational and in-situ spectroscopic techniques show that P is present as a surface phosphate ion; that electron holes localise on the surface ions and both (P-O1-) and Co3+-OH- are prospective surface-active sites for HER. (2) To further improve the HER performance in neutral media, which is promising for future study of electrolysis of sea water, N-modified FeP supported on mildly oxidized carbon nanotubes electrocatalysts were fabricated. In general, metal phosphides catalysts are not stable under working condition, which would usually undergo surface reconstruction and weaken the performance. In this work, a facile one-step stabilization of metal phosphides electrocatalysts by doping of N was proposed. The optimized catalyst shows an overpotential of 256 mV at a large current density of 65 mA cm−2, which is even 10 mV lower than that of the commercial 20% Pt/C catalyst. (3) To improve the OER performance in alkaline media, Co4S3 supported on Ni foam catalysts were fabricated by a one-step room-temperature wet chemistry synthesis strategy. Excellent stability was achieved under a constant current density of 100 mA cm-2 for 100 hours and the OER performance of the catalyst was improved after 1400 cycles of linear sweep voltammetry tests in alkaline media. Furthermore, the underpinning mechanism of action was studied by measuring the change of valence states for different elements to elucidate the structural evolution and active species during the electrocatalytic process

Research on Ecological Development of Foreign Language Journals—Based on Foreign language Teaching and Research

A foreign language journal is an ecosystem because each foreign language journal has different columns. This paper takes Foreign Language Teaching and Research as an example to analyze the ecological development of foreign language journals and give some suggestions on the future development of foreign language journals

Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks

As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Researchers have begun exploring the implementation of LLMs as the driving core of social bots, enabling more efficient and user-friendly completion of tasks like profile completion, social behavior decision-making, and social content generation. However, there is currently a lack of systematic research on the behavioral characteristics of LLMs-driven social bots and their impact on social networks. We have curated data from Chirper, a Twitter-like social network populated by LLMs-driven social bots and embarked on an exploratory study. Our findings indicate that: (1) LLMs-driven social bots possess enhanced individual-level camouflage while exhibiting certain collective characteristics; (2) these bots have the ability to exert influence on online communities through toxic behaviors; (3) existing detection methods are applicable to the activity environment of LLMs-driven social bots but may be subject to certain limitations in effectiveness. Moreover, we have organized the data collected in our study into the Masquerade-23 dataset, which we have publicly released, thus addressing the data void in the subfield of LLMs-driven social bots behavior datasets. Our research outcomes provide primary insights for the research and governance of LLMs-driven social bots within the research community.Comment: 18 pages, 7 figure

Stimuli Responsive Fluidics Controls on a Paper-Based Bacterial Detection Platform

Infectious diseases are the leading causes of death around the world. Point-of-care devices using nucleic acid amplification are sensitive enough to diagnose these diseases, however, often require complex and time-intensive sample preparation steps that are not integrated with the detection process. A rapid, sensitive, and integrated sample-to-result diagnostic device will permit disease treatment planning at the point-of-care. Paper-based detection assays are a promising platform to integrate the sample preparation and detection, with minimal infrastructure, equipment, and user involvement. To integrate sample preparation with detection on paper-based assays, timing and delivery of sample fluid flow needs to be controlled. Here we use thermally responsive materials (e.g. wax) to create a micro valve on a nitrocellulose membrane in order to automate fluid flow and minimize user involvement. The ease-of-fabrication, lot-to-lot variability and consistency of the dispensing methods are compared. After multiple trials, dispensing the wax material, PureTemp 68X, using a stamp made of polydimethylsiloxane (PDMS) is able to direct the sample flow with the highest consistency. Thermally responsive valves fabricated by stamping PureTemp 68X are found to block the sample fluid to flow for a sustained time when cool. When heated above the melting temperature (68°C), the valve opens and allows fluid flow without interfering with downstream assay binding reactions. These valves can be actuated multiple times simply by heating and cooling again. As a proof of concept, we use the valve to control sample delivery time of nucleic acid amplicons in a lateral flow immunoassay. Amplicons dispensed onto one side of the assay are incubated and bind to gold nanoparticles. They are then released through the valve by applying heat to open the valve. Nanoparticle nucleic acid amplicons will bind to recognition antibodies at the far side of the valve and become visible bands for detection

On The Concept of a True Judge And Judging A Good Artwork

David Hume introduced the concept of a true judge in his paper Of the Standard of Taste. Hume suggested that a true judge would judge an artwork to be good if he or she experiences the pleasure produced by certain qualities in an artwork. His theory discussed the criteria of an ideal true judge but failed to recognize that a true judge is also an ordinary person. In order to fix the irrealism of Hume’s theory, I first classified pleasure into what I will call aesthetic pleasure and hasty pleasure. I revised Hume’s theory by limiting the meaning of pleasure to aesthetic pleasure, which is the pleasure produced by certain qualities in an artwork. To further help a true judge distinguish the effect of aesthetic pleasure from the effect of hasty pleasure, I proposed a candidate for Hume’s principles.Bachelor of Art

Dynamic characteristic of spur gear with flexible support of gearbox

In this study, a nonlinear translation-torsion model of spur gear pair with flexible support of gearbox is proposed. The time-varying meshing stiffness, transmission error and backlash are considered in this model. Lagrange’s equations are used for establishing the mathematic model. The numerical method is presented for solutions of nonlinear differential equations. The effect of rotating speed and support stiffness of gearbox is analyzed. The numerical results show that the flexibility of the support of gearbox has a significant effect on the amplitude-frequency characteristic of the spur gear pair at low rotating speeds. The response shows flexibility while the support stiffness is smaller than the bearings and rigidity while the support stiffness is larger than the bearings. The maximum deformation of the driving gear bearings under the flexible support is generally greater than the one under rigid support

Unsupervised Object-Centric Voxelization for Dynamic Scene Understanding

Understanding the compositional dynamics of multiple objects in unsupervised visual environments is challenging, and existing object-centric representation learning methods often ignore 3D consistency in scene decomposition. We propose DynaVol, an inverse graphics approach that learns object-centric volumetric representations in a neural rendering framework. DynaVol maintains time-varying 3D voxel grids that explicitly represent the probability of each spatial location belonging to different objects, and decouple temporal dynamics and spatial information by learning a canonical-space deformation field. To optimize the volumetric features, we embed them into a fully differentiable neural network, binding them to object-centric global features and then driving a compositional NeRF for scene reconstruction. DynaVol outperforms existing methods in novel view synthesis and unsupervised scene decomposition and allows for the editing of dynamic scenes, such as adding, deleting, replacing objects, and modifying their trajectories

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201