Human safety and well-being is threatened by microbes causing numerous infectious diseases resulting in a large number of deaths every year. Despite substantial progress in antimicrobial drugs, many infectious diseases remain difficult to treat. Antimicrobial polymers offer a promising antimicrobial strategy for fighting pathogens and have received considerable attention in both academic and industrial research. This mini-review presents the advances made in antimicrobial polymers since 2013. Antimicrobial mechanisms exhibiting either passive or active action and polymer material types containing bound or leaching antimicrobials are introduced. This article also addresses the applications of these antimicrobial polymers in the medical, food, and textile industries.

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

A vast majority of alginate particles exist as spheres in most practical uses, and both the particle shape and size are the key factors dominating the applications and performance of alginate gels. Therefore, it becomes an issue of great interest to investigate the aspheric alginate particles. As the first step, various shaped alginate particles were formed due to various pH values in gelation solutions. It was experimentally demonstrated that a low pH brought about an oblate shape, and particularly lower concentrations of both alginate and divalent cations resulted in a flattened oblate shape. Ba2+acting as a cross-linker had a less impact on the particle shape than Ca2+due to a higher affinity in alginate intermolecular cross-linking. With a larger surface area, an oblate particle offered a higher release rate than a spheric one.

A great number of scientific papers are published every year in the field of battery research, which forms a huge textual data source. However, it is difficult to explore and retrieve useful information efficiently from these large unstructured sets of text. The Bidirectional Encoder Representations from Transformers (BERT) model, trained on a large data set in an unsupervised way, provides a route to process the scientific text automatically with minimal human effort. To this end, we realized six battery-related BERT models, namely, BatteryBERT, Batter-yOnlyBERT, and BatterySciBERT, each of which consists of both cased and uncased models. They have been trained specifically on a corpus of battery research papers. The pretrained BatteryBERT models were then fine-tuned on downstream tasks, including battery paper classification and extractive question-answering for battery device component classification that distinguishes anode, cathode, and electrolyte materials. Our BatteryBERT models were found to outperform the original BERT models on the specific battery tasks. The fine-tuned BatteryBERT was then used to perform battery database enhancement. We also provide a website application for its interactive use and visualization.

BatteryDataExtractor is the first property-specific text-mining tool for auto-generating databases of materials and their property, device, and associated characteristics. The software has been constructed by embedding the BatteryBERT model.