A Systematic Literature Review on Safety Research Related to Chemical Industrial Parks

Low-Temperature Rapid Polymerization of Intrinsic Conducting PAD/OC Hydrogels with a Self-Adhesive and Sensitive Sensor for Outdoor Damage Repair and Detection

Intrinsic conducting hydrogels fabricated in situ at low temperatures with self-adhesive properties and excellent flexibility hold significant promise for energy applications and outdoor damage repair. However, challenges such as low polymerization rate and self adhesion, insufficient ionic conductivity, inflexibility, and poor stability under extreme cold conditions have hindered their utilization as high-performance sensors. In this study, we designed an intrinsic conducting hydrogel (PADOC) composed of acrylic acid, acryloyloxyethyltrimethylammonium chloride, N,N'-methylenebis(2-propenamide), self-fabricated oxidized curdlan (OC), and a water/glycerol binary solvent. The novel hydrogel exhibited rapid gelation (30 s) at 0 °C facilitated by the promotion of OC, without the need for external energy input. Our findings from FT-IR, NMR, XPS, XRD, EPR spectra, MS, and DSC analyses revealed that OC underwent selective oxidation via the evolved Fenton reaction at 30 °C, serving as bioaccelerators for PAD polymerization. Due to OC's reductive structure and increased solubility, the reaction activation energy of the PAD polymerization reaction significantly reduced from 103.2 to 54.4 kJ/mol. PADOC ionic hydrogels demonstrated an electrical conductivity of 1.00 S/m, 0.7% low hysteresis, 39.6 kPa self-adhesive strength, and 923% strain-at-break and kept even at -20 °C owing to dense hydrogen and ionic bonds between PAD and OC chains. Furthermore, PADOC ionic hydrogels exhibited antifatigue properties for 10 cycles (0-100%) due to electrostatic interactions and hydrogen bonding. Remarkably, using a self-designed device, the rapid polymerization of PADOC effectively repaired copper pipeline leakage under 86 kPa pressure and detected 1% strain variation as a strain sensor. This study opens a new avenue for the rapid gelation of self-adhesive and flexible intrinsic conducting hydrogels with robust sensor performance.

Development to Emergency Evacuation Decision Making in Hazardous Materials Incidents Using Machine Learning

Chemical accidents are the biggest factor that hinders the development of the chemical industry. Issuing an emergency evacuation order is one of effective ways to reduce human casualties that may occur due to chemical accidents. The present study proposes a machine learning-based decision making model for faster and more accurate decision making for the issuance of an emergency evacuation order in the event of a chemical accident. To implement the decision making model, supervised learning by the 1-Dimension Convolutional Neural Network based model was carried out using the HSEES and NTSIP data of ATSDR in the United States. An action—victim matrix was devised to determine the validity of emergency evacuation orders and the decision making model was made to learn the matrix so that the decision making model could recommend whether to execute the emergency evacuation orders or not. To make the decision making model learn the chemical accident situations, the embedding technique used in text mining was applied, and weighted learning was carried out considering the fact that learning data are asymmetric. The AUROC value for the results of the decision making by the model is 0.82, which is at a reliable level. Establishing such an emergency response decision making model using the method proposed in the present study in the mitigation stage will help the process. Among the chemical accident emergency management stages, constructing a database for the model, and using the model as a tool for quick decision making for an emergency evacuation order, is also thought to be helpful in the establishment and implementation of emergency response plans for chemical accidents.

Trends in Civic Engagement Disaster Safety Education Research: Systematic Literature Review and Keyword Network Analysis

Background: Education plays the most important role in establishing a disaster management system by creating a safety culture in the community and by engaging its members. This study explored the trends in research on disaster safety education based on the community from the perspective of lifelong education. Methods: We undertook a systematic literature review and keyword network analysis. The main search keywords were “community”, “disaster”, “safety”, and “education”. The subjects of education were adults, including disaster-vulnerable people, such as elderly and disabled people. A total of 185 articles and papers were identified and then narrowed down to 56. Results: Research related to disaster safety education has developed in a direction that reflects the characteristics of disasters that occur in the region. Currently, disaster safety education is being studied in various fields, including the humanities, social sciences, and engineering, focusing on disaster prevention. The main research methods in the reviewed literature were qualitative, especially case studies that applied narrative, storytelling, and risk scenario construction. Conclusion: The study provides a framework for the in-depth analysis of disaster risk management and risk level of communities, and lays the academic foundation for it.