科研成果

Indoor semivolatile organic compounds (SVOCs) pose a substantial threat to human health. However, identifying the sources of these emissions has been challenging owing to the scarcity of convenient and practical on-site methodologies. Herein, a novel method for source screening was proposed using aluminum silicate sampling strips to adsorb SVOCs from the surface air of indoor materials. The adsorbed SVOC levels indicate the emission intensity of these materials into indoor environments. Additionally, compact sampling strips can be readily fixed to any vertical surface using a static sticker, facilitating the characterization of various materials in practical settings. Laboratory-simulated experiments demonstrated the capability of the proposed method to differentiate between source and non-source materials within a 10-cm distance in the same space. In practical scenarios, the primary emission sources identified via this method exhibited a consistent correlation with the contents of the corresponding materials obtained from the traditional solvent-extraction method. As the adsorbed SVOCs were directly transferred to a GC–MS through thermal desorption instead of the solvent-extraction procedure, the proposed method demonstrated several-fold improvements in analytical sensitivity and efficiency. Using this versatile screening technique, some emerging and important SVOC species were identified within specific indoor materials. Eliminating these sources has been demonstrated as an effective approach to mitigate SVOC pollution. Overall, the proposed method offers a powerful tool for managing indoor pollutants and safeguarding human health.

The properties of the interface between materials have practical implications in various fields, encompassing capillary action, foam and emulsion stability, adhesion properties of materials and mass and heat transfer processes. Studying the dynamics of interfaces is also fundamental for understanding intermolecular interactions, change of molecular conformations and molecular aggregations. Pendant-drop tensiometry and its extension, the oscillating drop method, are simple, versatile methods used to measure surface tension, interfacial tension and interfacial rheological properties. These methods can, however, generate unreliable results because of inadequate material preparation, an incorrect calibration method, inappropriate selection of data for analysis, neglect of optical influences or operating the system outside the linear viscoelastic regime. In addition, many studies fail to report accurate uncertainties. This protocol addresses all these critical points and provides detailed descriptions of some operation tips relating to purifying methods for different kinds of material, the time frame for analyzing measurement data, the correction method for optical effects, implementation of the oscillating method with a common programmable pump and remedies for some common problems encountered during the measurement. Examples of interfacial tension measurements for two- and three-phase systems, as well as interfacial dilational modulus measurements for N2 and surfactant solutions, are provided to illustrate procedural details and results. A single measurement takes minutes to hours to complete, while the entire protocol, including the leak test, cleaning, repeated measurements and data analysis, may take several days.

In the context of the rapid growth of corporate green investment and the rapid dissemination of information brought about by Internet technology, it is important to explore the relationship between investor attention and corporate environmental responsibility. Unfortunately, an in-depth research on the relationship between investor attention and firms' environment, society, and governance (ESG) performance remains unexplored. The results show a mutual inhibition between investor attention and firms’ ESG performance. Each 1% increase in investor attention decreases ESG performance by 0.252%, while each 1% increase in ESG performance decreases investor attention by 2.296%. Thus, ESG performance dominates this mutual influence. Moreover, ESG performance positively affects ESG performance and investor attention of neighboring firms. Each 1% increase in ESG performance increases ESG performance and investor attention of neighboring firms by 0.371% and 0.983%, respectively. Investor attention negatively affects investor attention and ESG performance of neighboring firms. Each 1% increase in investor attention decreases ESG performance and investor attention of neighboring firms by 0.04% and 0.104%, respectively. Further research reveals significant regional and organizational heterogeneity in the relationship between investor attention and ESG performance. The findings provide theoretical and empirical insights for further improvement of the ESG system and continued strengthening of investor guidance by regulators.

This paper studies the problem of language-guided reflection separation which aims at addressing the ill-posed reflection separation problem by introducing language descriptions to provide layer content. We propose a unified framework to solve this problem which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a randomized training strategy are employed to tackle the recognizable layer ambiguity. The effectiveness of the proposed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.

Event cameras with their high temporal resolution dynamic range and low power consumption are particularly good at time-sensitive applications like deblurring and frame interpolation. However their performance is hindered by latency variability especially under low-light conditions and with fast-moving objects. This paper addresses the challenge of latency in event cameras – the temporal discrepancy between the actual occurrence of changes in the corresponding timestamp assigned by the sensor. Focusing on event-guided deblurring and frame interpolation tasks we propose a latency correction method based on a parameterized latency model. To enable data-driven learning we develop an event-based temporal fidelity to describe the sharpness of latent images reconstructed from events and the corresponding blurry images and reformulate the event-based double integral model differentiable to latency. The proposed method is validated using synthetic and real-world datasets demonstrating the benefits of latency correction for deblurring and interpolation across different lighting conditions.

In this paper, we introduce L-DiffER, a language-based diffusion model designed for the ill-posed single image reflection removal task. Although having shown impressive performance for image generation, existing language-based diffusion models struggle with precise control and faithfulness in image restoration. To overcome these limitations, we propose an iterative condition refinement strategy to resolve the problem of inaccurate control conditions. A multi-condition constraint mechanism is employed to ensure the recovery faithfulness of image color and structure while retaining the generation capability to handle low-transmitted reflections. We demonstrate the superiority of the proposed method through extensive experiments, showcasing both quantitative and qualitative improvements over existing methods.

When photographing through a piece of glass, reflections usually degrade the quality of captured images or videos. In this paper, by exploiting periodically varying light flickering, we investigate the problem of removing strong reflections from contaminated image sequences or videos with a unified capturing setup. We propose a learning-based method that utilizes short-term and long-term observations of mixture videos to exploit one-side contextual clues in fluctuant components and brightness-consistent clues in consistent components for achieving layer separation and flickering removal, respectively. A dataset containing synthetic and real mixture videos with light flickering is built for network training and testing. The effectiveness of the proposed method is demonstrated by the comprehensive evaluation on synthetic and real data, the application for video flickering removal, and the exploratory experiment on high-speed scenes.