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3. ARGET ATRP of styrene with low catalyst usage in bio-based solvent γ-valerolactone

Author

Zhu Qianqian, Song Tianchen, Zhao Jiaxin, Gao Gang, Xiang Yixin, Gao Jiangang, and Shen Xianrong

Subjects
styrene polymerization, green solvent, arget atrp, γ-valerolactone, living radical polymerization, Polymers and polymer manufacture, TP1080-1185
Abstract

The application of bio-based solvents for living radical polymerization has been a hot topic in recent year. In this article, γ-valerolactone (GVL), a bio-based solvent as green media for ARGET atom transfer radical polymerization (ATRP) of styrene (St) were investigated. We first conducted polymerization of St in γ-valerolactone using copper(ii) bromide as the catalyst, tris(2-pyridylmethyl) amine as the ligand, and only sodium ascorbate as reducing agent. The polymerization achieved moderate conversion; however, the controllability of polymerization was not very good, providing a polymer with a broad molecular weight distribution (M w/M n > 1.30). When sodium carbonate is introduced, excellent results were obtained providing high yields and low M w/M n values under very low catalyst usage (∼5 ppm). 1H NMR spectroscopy, chain extension, and MALDI–MS experiments confirmed the final polymer chains with high fidelity. The use of GVL solvent opens a new route for the easy synthesis of PS through ARGET ATRP with low catalyst usage conditions.

Published
2024
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45. APPLICATION OF COMPUTER VISION TECHNOLOGY IN ATHLETES CLINICAL MEDICAL RECORD IMAGE BIOMETRIC EXTRACTION.

Author

Fu Shaoze, Gao Gang, and Zhao Hongmei

Subjects
COMPUTER vision, ATHLETES, ELECTRONIC health records
Abstract

With the development of clinical informatization technology, a large amount of data resources has been accumulated in the medical field, among which Athletes’ electronic medical records (EMRs) are one of the important data sources of clinical informatization and contain rich medical knowledge, how to obtain valuable biometric features from these data has become the basis of medical intelligence research. Therefore, this paper takes structured text as the entry point, and firstly, the research status of information extraction is elaborated. Moreover, in the work of e-medical biometrics recognition, medical text is added to the language model BERT for pre-training and fine-tuning, and a multi-head self-attention mechanism is introduced to incorporate a bidirectional LSTM model for feature extraction, and biometric features are extracted by using a stochastic conditional field as a classification constraint. This design bypasses the character-level image segmentation step for text line images, thus avoiding the overall accuracy degradation caused by the backward accumulation of errors in character segmentation. Finally, the experimental results show that the model can effectively accomplish the related biological feature extraction tasks. [ABSTRACT FROM AUTHOR]

Published
2024
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