Redox Flow Batteries: Fundamentals and Applications

A redox flow battery is an electrochemical energy storage device that converts chemical energy into electrical energy through reversible oxidation and reduction of working fluids. The concept was initially conceived in 1970s. Clean and sustainable energy supplied from renewable sources in future requires efficient, reliable and cost‐effective energy storage systems. Due to the flexibility in system design and competence in scaling cost, redox flow batteries are promising in stationary storage of energy from intermittent sources such as solar and wind. This chapter covers basic principles of electrochemistry in redox flow batteries and provides an overview of status and future challenges. Recent progress in redox couples, membranes and electrode materials will be discussed. New demonstration and commercial development will be addressed

Effects of Seller Certificates on Buyer’s Order Cancellation in the E-marketplace

E-marketplaces are implementing various policies to reduce the information asymmetry between sellers and buyers. One popular way is to issue different kinds of certificates (or seals) for sellers, e.g., a quality certificate for sellers who have a lower product return rate than others or a quick certificate for sellers who dispatch products faster than others. Despite a plethora of previous studies on the role of certificates in the e-marketplace, we have a limited understanding of certificate effects in the post-order stage, where buyers can reverse their purchase decision. Based on the psychological contract violation theory and other related literature, we first explain why seller certificates can take a role in buyers’ order cancellation decision. Then, we empirically examine the effects of seller certificates using the large transaction data from a leading e-marketplace in Korea. Our findings are as follows. Given the time elapsed from the order, buyers are less likely to cancel the order when the seller has a quality certificate (for sellers who have lower product return rate than others) or a quantity certificate (for experienced sellers who sold a larger amount of products than others). When the seller has a quick certificate (for sellers who dispatch products faster than others), on the other hand, buyers are more likely to cancel the order. Further, the effects of seller certificates on order cancellation are largely varying across purchase channels (Smartphone vs. PC) and product types (convenience goods, shopping goods, vs. specialty goods)

Vanadium Redox Flow Batteries: Electrochemical Engineering

The importance of reliable energy storage system in large scale is increasing to replace fossil fuel power and nuclear power with renewable energy completely because of the fluctuation nature of renewable energy generation. The vanadium redox flow battery (VRFB) is one promising candidate in large-scale stationary energy storage system, which stores electric energy by changing the oxidation numbers of anolyte and catholyte through redox reaction. This chapter covers the basic principles of vanadium redox flow batteries, component technologies, flow configurations, operation strategies, and cost analysis. The thermodynamic analysis of the electrochemical reactions and the electrode reaction mechanisms in VRFB systems have been explained, and the analysis of VRFB performance according to the flow field and flow rate has been described. It is shown that the limiting current density of “flow-by” design is more than two times greater than that of “flow-through” design. In the cost analysis of 10 kW/120 kWh VRFB system, stack and electrolyte account for 40 and 32% of total cost, respectively

Cross-Modal Learning with 3D Deformable Attention for Action Recognition

An important challenge in vision-based action recognition is the embedding of spatiotemporal features with two or more heterogeneous modalities into a single feature. In this study, we propose a new 3D deformable transformer for action recognition with adaptive spatiotemporal receptive fields and a cross-modal learning scheme. The 3D deformable transformer consists of three attention modules: 3D deformability, local joint stride, and temporal stride attention. The two cross-modal tokens are input into the 3D deformable attention module to create a cross-attention token with a reflected spatiotemporal correlation. Local joint stride attention is applied to spatially combine attention and pose tokens. Temporal stride attention temporally reduces the number of input tokens in the attention module and supports temporal expression learning without the simultaneous use of all tokens. The deformable transformer iterates L times and combines the last cross-modal token for classification. The proposed 3D deformable transformer was tested on the NTU60, NTU120, FineGYM, and Penn Action datasets, and showed results better than or similar to pre-trained state-of-the-art methods even without a pre-training process. In addition, by visualizing important joints and correlations during action recognition through spatial joint and temporal stride attention, the possibility of achieving an explainable potential for action recognition is presented.Comment: 10 pages, 8 figure

Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network

Along with generative AI, interest in scene graph generation (SGG), which comprehensively captures the relationships and interactions between objects in an image and creates a structured graph-based representation, has significantly increased in recent years. However, relying on object-centric and dichotomous relationships, existing SGG methods have a limited ability to accurately predict detailed relationships. To solve these problems, a new approach to the modeling multiobject relationships, called edge dual scene graph generation (EdgeSGG), is proposed herein. EdgeSGG is based on a edge dual scene graph and Dual Message Passing Neural Network (DualMPNN), which can capture rich contextual interactions between unconstrained objects. To facilitate the learning of edge dual scene graphs with a symmetric graph structure, the proposed DualMPNN learns both object- and relation-centric features for more accurately predicting relation-aware contexts and allows fine-grained relational updates between objects. A comparative experiment with state-of-the-art (SoTA) methods was conducted using two public datasets for SGG operations and six metrics for three subtasks. Compared with SoTA approaches, the proposed model exhibited substantial performance improvements across all SGG subtasks. Furthermore, experiment on long-tail distributions revealed that incorporating the relationships between objects effectively mitigates existing long-tail problems

Quasiclassical theory of non-adiabatic tunneling in nanocontacts induced by phase-controlled ultrashort light pulses

We theoretically investigate tunneling through free-space or dielectric nanogaps between metallic nanocontacts driven by ultrashort ultrabroadband light pulses. For this purpose we develop a time-dependent quasiclassical theory being especially suitable to describe the tunneling process in the non-adiabatic regime, when this process can be significantly influenced by the photon absorption as the electron moves in the classically forbidden region. Firstly, the case of driving by an ideal half-cycle pulse is studied. For different distances between the contacts, we analyze the main solutions having the form of a quasiclassical wave packet of the tunneling electron and an evanescent wave of the electron density. For each of these solutions the resulting tunneling probability is determined with the exponential accuracy inherent to the method. We identify a crossover between two tunneling regimes corresponding to both solutions in dependence on the field strength and intercontact distance that can be observed in the corresponding behaviour of the tunneling probability. Secondly, considering realistic temporal profiles of few-femtosecond pulses, we demonstrate that the preferred direction of the electron transport through the nanogap can be controlled by changing the carrier-envelope phase of the pulse, in agreement with recent experimental findings and numerical simulations. We find analytical expressions for the tunneling probability, determining the resulting charge transfer in dependence on the pulse parameters. Further, we determine temporal shifts of the outgoing electron trajectories with respect to the peaks of the laser field in dependence on the pulse phase and illustrate when the non-adiabatical character of the tunneling process is particularly important.Comment: 38 pages, 13 figure