The decomposition of volatile organic compounds by photocatalytic oxidation (PCO) has been widely studied. However, the technological development of this oxidative technique has to address how to handle the formation of transformation products. The work presented here investigates the original combination of a dense membrane separation process and PCO to intensify the trapping and reduction of PCO transformation products. Specific monitoring of toluene PCO transformation products, such as benzene and formaldehyde, in the outflow of both permeate and retentate compartments was proposed. The influence of operating parameters on the process, i.e., light intensity, pressure, membrane type, and catalyst mass, was also studied. The results reveal that membrane separation-PCO hybridization is particularly effective for reducing the presence of benzene and formaldehyde in the effluent treated. The benzene concentration in the outflow of the hybrid module can be reduced by a factor of 120 compared to that observed during the PCO of toluene alone.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502665 | PMC |
| http://dx.doi.org/10.3390/membranes12090900 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Plant Cell Culture-Derived Saponin Adjuvant Enhances Immune Response Against a Stabilized Human Metapneumovirus Pre-Fusion Vaccine Candidate.
Vaccines (Basel)
December 2024
Johnson & Johnson, Janssen Vaccines & Prevention, 2333 CN Leiden, The Netherlands.
Unlabelled: Human metapneumovirus (HMPV) is a significant respiratory pathogen, particularly in vulnerable populations.
Background: No vaccine for the prevention of HMPV is currently licensed, although several subunit vaccines are in development. Saponin-based adjuvant systems (AS), including QS-21, have transformed the field of subunit vaccines by dramatically increasing their potency and efficacy, leading to the development of several licensed vaccines.
Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation.
Sensors (Basel)
December 2024
Division of Computer Science & Artificial Intelligence, Dongguk University, Seoul 04620, Republic of Korea.
Anomaly detection is critical in safety-sensitive fields, but faces challenges from scarce abnormal data and costly expert labeling. Time series anomaly detection is relatively challenging due to its reliance on sequential data, which imposes high computational and memory costs. In particular, it is often composed of real-time collected data that tends to be noisy, making preprocessing an essential step.
View Article and Find Full Text PDFJoint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer.
Sensors (Basel)
December 2024
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230002, China.
LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter's ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability.
View Article and Find Full Text PDFVoice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies.
Sensors (Basel)
December 2024
Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland.
Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis.
View Article and Find Full Text PDFRobust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation.
Sensors (Basel)
December 2024
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!