Effect of the addition of diblock copolymer nanoparticles on the evaporation kinetics and final particle morphology for drying aqueous aerosol droplets

A deeper understanding of the key processes that determine the particle morphologies generated during aerosol droplet drying is highly desirable for spray-drying of powdered pharmaceuticals and foods, predicting the properties of atmospheric particles, and monitoring disease transmission. Particle morphologies are affected by the drying kinetics of the evaporating droplets, which are in turn influenced by the composition of the initial droplet as well as the drying conditions. Herein, we use polymerization-induced self-assembly (PISA) to prepare three types of sterically stabilized diblock copolymer nanoparticles comprising the same steric stabilizer block and differing core blocks with z-average diameters ranging from 32 to 238 nm. These well-defined nanoparticles enable a systematic investigation of the effect of the nanoparticle size and composition on the drying kinetics of aqueous aerosol droplets (20-28 μm radius) and the final morphology of the resulting microparticles. A comparative kinetics electrodynamic balance was used to obtain evaporation profiles for 10 examples of nanoparticles at a relative humidity (RH) of 0, 45, or 65%. Nanoparticles comprising the same core block with mean diameters of 32, 79, and 214 nm were used to produce microparticles, which were dried under different RH conditions in a falling droplet column. Scanning electron microscopy was used to examine how the drying kinetics influenced the final microparticle morphology. For dilute droplets, the chemical composition of the nanoparticles had no effect on the evaporation rate. However, employing smaller nanoparticles led to the formation of dried microparticles with a greater degree of buckling

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press