Progress in Ionic Liquids as Reaction Media, Monomers and Additives in High-Performance Polymers

In this chapter, we will review the progress in ionic liquids (ILs) widely used as reaction media, monomers and additives in the synthesis, chemical modification and physical processing of high-performance polymers (HPPs). Using ILs as reaction media in the syntheses of HPPs, the high-molecular-weight polymers were obtained in good yields and the shortened dehydration time compared to the conventional methods, the separation efficiency of products was improved. It is particularly noteworthy that the number of novel copolymers of HPPs with polymerisable ILs has steadily increased in recent years. In addition, ILs have been used as various types of additives such as the components of polymer materials, plasticizers and porogenic agents in the physical processing of HPPs, and the materials prepared include membranes, microcapsules, nanocomposites (NCs), electrolytes and grease

Plasmonic Thin Film Solar Cells

Thin film solar cell technology represents an alternative way to effectively solve the world’s increasing energy shortage problem. Light trapping is of critical importance. Surface plasmons (SPs), including both localized surface plasmons (LSPs) excited in the metallic nanoparticles and surface plasmon polaritons (SPPs) propagating at the metal/semiconductor interfaces, have been so far extensively investigated with great interests in designing thin film solar cells. In this chapter, plasmonic structures to improve the performance of thin film solar cell are reviewed according to their positions of the nanostructures, which can be divided into at least three ways: directly on top of thin film solar cell, embedded at the bottom or middle of the optical absorber layer, and hybrid of metallic nanostructures with nanowire of optical absorber layer

Physically Plausible Animation of Human Upper Body from a Single Image

We present a new method for generating controllable, dynamically responsive, and photorealistic human animations. Given an image of a person, our system allows the user to generate Physically plausible Upper Body Animation (PUBA) using interaction in the image space, such as dragging their hand to various locations. We formulate a reinforcement learning problem to train a dynamic model that predicts the person's next 2D state (i.e., keypoints on the image) conditioned on a 3D action (i.e., joint torque), and a policy that outputs optimal actions to control the person to achieve desired goals. The dynamic model leverages the expressiveness of 3D simulation and the visual realism of 2D videos. PUBA generates 2D keypoint sequences that achieve task goals while being responsive to forceful perturbation. The sequences of keypoints are then translated by a pose-to-image generator to produce the final photorealistic video.Comment: WACV 202

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm.Comment: The first two authors are with equal contributions. Paper accepted by ACM MM 202

Mechanism of enhancement of intumescent fire retardancy by metal acetates in polypropylene

The effects of cobalt acetate (CoAc), manganese acetate (MnAc), nickel acetate (NiAc) and zincacetate (ZnAc) as fire retardant additive in intumescent polypropylene (PP) formulations containing PP/ammonium polyphosphate (APP)/pentaerythritol (PER) are reported. The limiting oxygen index (LOI) and vertical burning (UL94) tests and cone calorimetry were used to quantify the enhancement. Environmental chamber rheometry, thermal gravimetric analysis and the morphology of the residual char were used to investigate the mechanism of enhancement. The incorporation of small quantities of metal acetates had a significant influence on the fire behaviour. As an example, 0.7 wt% MnAc improved the UL 94 rating of PP/APP+PER (mass ratio 100/25, with APP/PER=3/1) sample from V-2 to V-0, while 1 wt% MnAc reduced the peak heat release rate and the total heat release by 18% and 12% in the cone calorimeter. Rheological data, cone calorimetry, and photographs of the residual char showed how the fire retardancy of the systems were affected by the melt viscosity, which depended on the loading of metal acetate. During thermal decomposition, the metal acetates promote the crosslinking of the polymer and the fire retardant, reinforcing the protective intumescent layer. While, the effect is most potent at the optimal metal loadings. At higher MnAc loadings, the benefit of a stronger char is overwhelmed by the adverse effect of crosslinking on the transition char layer. Thus, this paper offers a new insight into the mechanism of the intumescent fire retarded PP system

A four-microRNA panel in serum may serve as potential biomarker for renal cell carcinoma diagnosis

BackgroundRenal cell carcinoma (RCC) is one out of the most universal malignant tumors globally, and its incidence is increasing annually. MicroRNA (miRNA) in serum could be considered as a non-invasive detecting biomarker for RCC diagnosis.MethodA total of 224 participants (112 RCC patients (RCCs) and 112 normal controls (NCs)) were enrolled in the three-phrase study. Reverse transcription quantitative PCR (RT-qPCR) was applied to reveal the miRNA expression levels in RCCs and NCs. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to predict the diagnostic ability of serum miRNAs for RCC. Bioinformatic analysis and survival analysis were also included in our study.ResultsCompared to NCs, the expression degree of miR-155-5p, miR-224-5p in serum was significantly upregulated in RCC patients, and miR-1-3p, miR-124-3p, miR-129-5p, and miR-200b-3p were downregulated. A four-miRNA panel was construed, and the AUC of the panel was 0.903 (95% CI: 0.847–0.944; p < 0.001; sensitivity = 75.61%, specificity = 93.67%). Results from GEPIA database indicated that CHL1, MPP5, and SORT1 could be seen as promising target genes of the four-miRNA panel. Survival analysis of candidate miRNAs manifested that miR-155-5p was associated with the survival rate of RCC significantly.ConclusionsThe four-miRNA panel in serum has a great potential to be non-invasive biomarkers for RCC sift to check