Crystallization kinetics of a commercial poly(lactic acid) based on characteristic crystallization time and optimal crystallization temperature

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10973-020-10081-7[Abstract]: A model is proposed to fit differential scanning calorimetry (DSC) isothermal crystallization curves obtained from the molten state at different temperatures. A commercial 3D printing polylactic acid (PLA) sample is used to test the method. All DSC curves are fitted by a mixture of two simultaneous functions, one of them being a time derivative generalized logistic accounting for the exothermic effect and the other, a generalized logistic, accounting for the baseline. There is a rate parameter, which is allowed to vary across different temperatures. The rate parameter values obtained at different temperatures were jointly explained as a result of three crystallization processes, each one defined by a characteristic crystallization time, a characteristic temperature, and a dispersion or width factor. Apart from the very good fittings obtained at all temperatures, the results agree with the existence of a few crystal forms of PLA, which were demonstrated by other authors. Thus, the main significance of this work consists in providing a new approach in order to mathematically describe the isothermal crystallization kinetics of a polymer from the melt. Such a kinetic description is needed in order to predict the extent of a crystallization process as a function of time at any isothermal temperature. The approach used here allows to understand the overall crystallization of the PLA used in this work as the sum of three crystallization processes, each of them corresponding to a different crystal form. Each experimental crystallization exotherm, which may include more than one crystal form, can be reproduced by a generalized logistic function. The overall rate factor at a given temperature is the weighted sum of the rate factors of the different crystal structures at that temperature. The rate factor of each of these three processes is described by a Gaussian function whose parameters are a crystallization time, a characteristic temperature and a temperature dispersion factor. Therefore, the crystallization rate for each crystal form can be interpreted as a relative likelihood to crystallize at a given temperature. On the other hand, the characteristic crystallization time parameter refers to the time needed for a given crystal structure to be formed at the temperature at which the relative likelihood to crystallize of that form is highestThis research has been supported by the Spanish Ministry of Science and Innovation, MINECO Grant MTM2017–82724-

Insight into the key bridge for infant’s nutrition and health: how to explore personalized utilization pathways from diverse human milk oligosaccharides

Breast milk is the preferred gold standard food for infants. Human milk oligosaccharides (HMOs) are the third most natural component in breast milk. But breast milk is often insufficient, so they rely solely on breast milk substitutes. HMOs provide nutrients to beneficial gut microbiota such as Lactobacilli and Bifidobacteria, helping to establish and maintain a balance of microbial communities in the infant gut. HMOs mimic the receptors of pathogens, preventing them from attaching to the baby’s intestinal cells, thereby preventing pathogen infection. This function is particularly crucial for newborns and infants. How to individually use HMOs is important. We focused on classification and characteristics of HMOs, their impact, intake, development/utilization mechanism on infant health, aiming to provide HMOs data support for the development. HMOs are quite different (more than 200 kinds), so it is necessary to make targeted selection, and those associated with intestinal microorganisms (Bifidobacterium), which can utilize HMOs, have the greatest application potential. Oligosaccharide-binding protein (OBPs) are an important medium for ATP-binding cassette transporter channel of intestinal HMOs transport; the influence of key OBPs of Bifidobacterium on HMOs recognition in infants from various countries has been explored, which is helpful to accelerate the establishment of precise and personalized milk powder in the future. The more important significance of the results of this review is to help consumers better choose HMOs, thereby promoting the long-term health of infants, especially the early development of their immune system

Open-Set Image Tagging with Multi-Grained Text Supervision

In this paper, we introduce the Recognize Anything Plus Model (RAM++), an open-set image tagging model effectively leveraging multi-grained text supervision. Previous approaches (e.g., CLIP) primarily utilize global text supervision paired with images, leading to sub-optimal performance in recognizing multiple individual semantic tags. In contrast, RAM++ seamlessly integrates individual tag supervision with global text supervision, all within a unified alignment framework. This integration not only ensures efficient recognition of predefined tag categories, but also enhances generalization capabilities for diverse open-set categories. Furthermore, RAM++ employs large language models (LLMs) to convert semantically constrained tag supervision into more expansive tag description supervision, thereby enriching the scope of open-set visual description concepts. Comprehensive evaluations on various image recognition benchmarks demonstrate RAM++ exceeds existing state-of-the-art (SOTA) open-set image tagging models on most aspects. Specifically, for predefined commonly used tag categories, RAM++ showcases 10.2 mAP and 15.4 mAP enhancements over CLIP on OpenImages and ImageNet. For open-set categories beyond predefined, RAM++ records improvements of 5.0 mAP and 6.4 mAP over CLIP and RAM respectively on OpenImages. For diverse human-object interaction phrases, RAM++ achieves 7.8 mAP and 4.7 mAP improvements on the HICO benchmark. Code, datasets and pre-trained models are available at \url{https://github.com/xinyu1205/recognize-anything}.Comment: Homepage: https://github.com/xinyu1205/recognize-anythin

Upstream experiments on the gigabit PON physical medium layer

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Correlation between intercalated magnetic layers and superconductivity in pressurized EuFe2(As0.81P0.19)2

We report comprehensive high pressure studies on correlation between intercalated magnetic layers and superconductivity in EuFe2(As0.81P0.19)2 single crystal through in-situ high pressure resistance, specific heat, X-ray diffraction and X-ray absorption measurements. We find that an unconfirmed magnetic order of the intercalated layers coexists with superconductivity in a narrow pressure range 0-0.5GPa, and then it converts to a ferromagnetic (FM) order at pressure above 0.5 GPa, where its superconductivity is absent. The obtained temperature-pressure phase diagram clearly demonstrates that the unconfirmed magnetic order can emerge from the superconducting state. In stark contrast, the superconductivity cannot develop from the FM state that is evolved from the unconfirmed magnetic state. High pressure X-ray absorption (XAS) measurements reveal that the pressure-induced enhancement of Eu's mean valence plays an important role in suppressing the superconductivity and tuning the transition from the unconfirmed magnetic state to a FM state. The unusual interplay among valence state of Eu ions, magnetism and superconductivity under pressure may shed new light on understanding the role of the intercalated magnetic layers in Fe-based superconductors