An Autonomous Large Language Model Agent for Chemical Literature Data Mining

Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging researchers to discern patterns and refine synthesis processes. Artificial intelligence (AI) helps by analyzing data to optimize synthesis and increase yields. However, AI faces challenges in processing literature data due to the unstructured format and diverse writing style of chemical literature. To overcome these difficulties, we introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. This AI agent employs large language models (LLMs) for prompt generation and iterative optimization. It functions as a chemistry assistant, automating data collection and analysis, thereby saving manpower and enhancing performance. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data, and we compared our method with human experts in terms of content correctness and time efficiency. The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry

Construction of photocatalytic membrane separation materials and its research progress in wastewater treatment

Membrane separation has been widely used in the field of water treatment due to its characteristics such as operation at room temperature, high separation efficiency and environmental friendliness. However, membrane pollution caused by suspended substances or soluble substances in the feed solution limits the further development of membrane technology. Photocatalytic membrane can use photocatalyst to degrade the pollutants on the membrane efficiently and improve the separation efficiency and anti-pollution performance of the membrane. This paper firstly introduced the types of photocatalysts used for the preparation of photocatalytic membrane, including metal oxides, graphite phase carbon nitride, bismuth-based materials and new two-dimensional materials. Then the construction methods of photocatalytic membranes with self-cleaning ability (surface modification and matrix blending of membrane) were reviewed, and the pollutant removal mechanism of photocatalytic membrane was summarized. The application scenarios of photocatalytic membrane for dyes, antibiotics, emerging pollutants in wastewater treatment of different industries were listed. Finally, the article gave an outlook on its future development and provided a certain reference for the development of highly anti-pollution membrane materials

Research on Protecting Information Security Based on the Method of Hierarchical Classification in the Era of Big Data

Big data is becoming increasingly important because of the enormous information generation and storage in recent years. It has become a challenge to the data mining technique and management. Based on the characteristics of geometric explosion of information in the era of big data, this paper studies the possible approaches to balance the maximum value and privacy of information, and disposes the Nine-Cells information matrix, hierarchical classification. Furthermore, the paper uses the rough sets theory to proceed from the two dimensions of value and privacy, establishes information classification method, puts forward the countermeasures for information security. Taking spam messages for example, the massive spam messages can be classified, and then targeted hierarchical management strategy was put forward. This paper proposes personal Information index system, Information management platform and possible solutions to protect information security and utilize information value in the age of big data