Innovative TiO2 Based Nanomaterials for Photocatalytic CO2 Reduction to Fuels and Anti-Fouling Membrane in Water Treatment

Due to fossil fuel usage, booming industry and other human activities, greenhouse gases associated with global warming and drinking water shortage severely threaten sustainable development of human society. It is emergent and critical to address and solve both of them. Greenhouse gases will trap heat and cause global warming, carbon dioxide (CO2) from fossil fuel combustion is the major contribution to greenhouse gas emission. In order to control CO2 emission, different technologies have been invented. Recently, photoreduce CO2 using solar energy with photocatalyst catches a lot of attention. Because on the one hand this technology can reduce CO2 in atmosphere, on the other hand alternative fuel can be produced with solar energy such as CO, methane, methanol, etc. For the drinking water shortage problem, membrane filtration technology has been proved as one of the most efficient and reliable methods to provide clean drinking water. However, membrane fouling caused by deposition of contaminants on membrane surface has been recognized as one of the major obstacles inhibiting the application of membrane technologies. Membrane fouling may dramatically shorten the lifetime of membrane module, deteriorate the quality of water produced and increase the operation cost. With the help of the photocatalyst, contaminates in water and on membrane can be degraded under light irradiation. Membrane fouling caused by contaminates can be significantly mitigated. Among all photocatalysts that have been investigated, TiO2 is a promising high efficient photocatalyst for both environmental and energy application, due to the low cost, high redox potential and nontoxicity. However, because of the large bandgap, fast hole/electron recombination process and limited visible light absorption, those characters significantly limit the application of TiO2. In this study, different TiO2 modification strategies were carried out to improve the efficiency of TiO2 photoactivity. One objective of this study is to demonstrate visible light functional iodine doped titanium oxide (I-TiO2) for CO2 photoreduction. I-TiO2 nanoparticles have been synthesized by hydrothermal method. I-TiO2 shows photocatalitically responsive to visible light illumination. The structure and properties of I-TiO2 nanocrystals prepared with different iodine doping levels and/or calcination temperatures were characterized by X-ray diffraction, transmission electron microscopy and diffraction, X-ray photoelectron spectroscopy, and UV–vis diffuse reflectance spectra. The three nominal iodine dopant levels (5, 10, 15 wt.%) and the two lower calcination temperatures (375, 450◦C) produced mixture of anatase and brookite nanocrystals, with small fractions of rutile found at 550◦C. The anatase phase of TiO2 increased in volume fraction with increased calcination temperature and iodine levels. A high CO2 reduction activity was observed for I-TiO2 catalysts (highest CO yield equivalent to 2.4 μmol g−1 h−1 ) under visible light, and they also had much higher CO2 photoreduction efficiency than undoped TiO2 under UV–vis irradiation. I-TiO2 calcined at 375◦C has superior activity to those calcined at higher temperatures. Optimal doping levels of iodine were identified under visible and UV–vis irradiations, respectively. Along with promising nonmetal-doped TiO2 results, our study also entails a new metal-nonmetal ion co-modified TiO2 nanoparticles fabricated through a combined hydrothermal and wet-impregnation process. Under UV–vis irradiation, the activity of the co-modified catalyst (Cu–I–TiO2) was higher than that of the single ion-modified catalysts (Cu–TiO2 or I–TiO2). Under visible light irradiation, the addition of Cu to I–TiO2 did not lead to significant improvements in CO2 reduction. Methyl chloride (CH3Cl) was detected as a reaction product when CuCl2 was used as the precursor in the synthesis, thus suggesting that methyl radicals are reaction intermediates. When CuCl2 was used as the Cu precursor, a three-fold increase in CO2 photoreduction activity was observed, as compared to when Cu(NO3)2 was used as the Cu precursor. These differences in activities were probably due to enhanced Cu dispersion and the hole-scavenging effects of the Cl ions. The water treatment with membrane filtration technology will always face membrane fouling. It is one of the major obstacles inhibiting the wide application of membrane technologies for water treatment. Membranes with surface modification of titanium dioxide (TiO2) nanoparticles or TiO2 nanowire membranes (Ti–NWM) have demonstrated reduced membrane fouling due to the photocatalytic capability of TiO2 in degrading foulants on the membrane surface. However, the wide band gap of TiO2 makes it only absorb ultraviolet light, which limits its applications under solar irradiation. In this study, our work entailed a novel membrane made of interwoven iron oxide (Fe2O3) nanowires and TiO2 nanowires (FeTi–NWM) has demonstrated superior anti-fouling capability in removing humic acid (HA) from water. Results showed that under simulated solar irradiation the FeTi–NWM achieved nearly complete HA removal during a 2 h short-term test at an initial HA concentration of 200 mg/L, compared with 89% HA removal by Ti–NWM. During a 12 h long-term test, the FeTi–NWM maintained 98% HA removal, while the Ti–NWM showed only 55% removal at the end. Without solar irradiation, the FeTi–NWM was severely contaminated and by contrast, a clean surface was maintained under solar irradiation after the 12 h test and the transmembrane pressure change was minimal. The improved HA removal by FeTi–NWM compared with Ti–NWM and its excellent anti-fouling capability under solar irradiation can be attributed to (1) the enhanced HA absorption by Fe2O3 nanowires and (2) the formed Fe2O3/TiO2 heterojunctions that increase photo-induced charge transfer and improve visible light activity. Future work includes further improvement of FeTi-NWM membrane with other materials such as graphene etc. Also design and test multi-stage FeTi-NWM membranes system for real industry application

Metabolism and transcriptional responses to asparagine in Arabidopsis thaliana

Asparagine aminotransferase transforms asparagine into α-ketosuccinamate, which is further deamidated by an ω-amidase. Serine:glyoxylate aminotransferase, encoded by AGT1 in Arabidopsis, was identified as asparagine aminotransferase. In the roots of 10-day-old Arabidopsis seedlings treated with 20 mM asparagine, AGT1 transcript levels increased by 2-fold while ω-amidase transcripts were decreased by 30%. Recombinant AGT1 had a substrate preference for asparagine when compared with alanine and serine as amino group donors. An ω-amidase candidate gene, AT5G12040, was identified based on amino acid sequence identity with mammalian gene Nitrilase 2. RT-PCR of a T-DNA insertion mutant line showed that ω-amidase expression was abolished compared with wild-type. In the roots of 10-day-old seedlings grown without asparagine or with 2 mM asparagine, the amount of ω-amidase substrates, α-ketosuccinamate and α-hydroxysuccinamate, was higher in mutant than in wild-type under both conditions. Kinetic analysis indicated that recombinant ω-amidase has a preference for α-hydroxysuccinamate over α-ketosuccinamate and α-hydroxysuccinamate

ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly applicable due to the distinct properties of ECGs in visualization and dynamic properties. Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the properties of ECGs to design effective attacks schemes under two attacks models respectively. Our results demonstrate the blind spots of DNN-powered diagnosis systems under adversarial attacks, which calls attention to adequate countermeasures.Comment: Accepted by AAAI 202

Distributed Topology Control based on Swarm Intelligence In Unmanned Aerial Vehicles Networks

Unmanned aerial vehicles (UAVs) have shown enormous potential in both public and civil domains. Although multi-UAV systems can collaboratively accomplish missions efficiently, UAV network(UAVNET) design faces many challenging issues, such as high mobility, dynamic topology, power constraints, and varying quality of communication links. Topology control plays a key role for providing high network connectivity while conserving power in UAVNETs. In this paper, we propose a distributed topology control algorithm based on discrete particle swarm optimization with articulation points(AP-DPSO). To reduce signaling overhead and facilitate distributed control, we first identify a set of articulation points (APs) to partition the network into multiple segments. The local topology control problem for individual segments is formulated as a degree-constrained minimum spanning tree problem. Each node collects local topology information and adjusts its transmit power to minimize power consumption. We conduct simulation experiments to evaluate the performance of the proposed AP-DPSO algorithm. Numerical results show that AP-DPSO outperforms some known algorithms including LMST and LSP, in terms of network connectivity, average link length and network robustness for a dynamic UAVNET

Alive Caricature from 2D to 3D

Caricature is an art form that expresses subjects in abstract, simple and exaggerated view. While many caricatures are 2D images, this paper presents an algorithm for creating expressive 3D caricatures from 2D caricature images with a minimum of user interaction. The key idea of our approach is to introduce an intrinsic deformation representation that has a capacity of extrapolation enabling us to create a deformation space from standard face dataset, which maintains face constraints and meanwhile is sufficiently large for producing exaggerated face models. Built upon the proposed deformation representation, an optimization model is formulated to find the 3D caricature that captures the style of the 2D caricature image automatically. The experiments show that our approach has better capability in expressing caricatures than those fitting approaches directly using classical parametric face models such as 3DMM and FaceWareHouse. Moreover, our approach is based on standard face datasets and avoids constructing complicated 3D caricature training set, which provides great flexibility in real applications.Comment: Accepted to CVPR 201

FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes