Spatioselective Occlusion of Copolymer Nanoparticles within Calcite Crystals Generates Organic-Inorganic Hybrid Materials with Controlled Internal Structures.

Efficient occlusion of particulate additives into a single crystal has garnered an ever-increasing attention in materials science because it offers a counter-intuitive yet powerful platform to make crystalline nanocomposite materials with emerging properties. However, precisely controlling the spatial distribution of the guest additives within a host crystal remains highly challenging. We herein demonstrate a unique, straightforward method to engineer the spatial distribution of copolymer nanoparticles within calcite (CaCO3) single crystals by judiciously adjusting initial [Ca2+] concentration used for the calcite precipitation. More specifically, polymerization-induced self-assembly is employed to synthesize well-defined and highly anionic poly(3-sulfopropyl methacrylate potassium)41-block-poly(benzyl methacrylate)500 [PSPMA41-PBzMA500] diblock copolymer nanoparticles, which are subsequently used as model additives during the growth of calcite crystals. Impressively, such guest nanoparticles are preferentially occluded into specific regions of calcite depending on the initial [Ca2+] concentration. These unprecedented phenomena are most probably caused by dynamic change in electrostatic interaction between Ca2+ ions and PSPMA41 chains based on systematic investigations. This study not only showcases a significant advancement in controlling the spatial distribution of guest nanoparticles within host crystals, enabling the internal structure of composite crystals to be rationally tailored via a spatioselective occlusion strategy, but also provides new insights into biomineralization.

Magnetically Reusable and Well-dispersed Nanoparticles for Oxygen Detection in Water.

In this study, we aimed to synthesize magnetically well-dispersed nanosensors for detecting dissolved oxygen (DO) in water, and explore their biological applications. Firstly, we synthesized two kinds of magnetic nanoparticle with average sizes of approximately 82 nm by one-step emulsion polymerization: polystyrene magnetic nanoparticles (Fe3O4@Os1-PS) and polymethylmethacrylate magnetic nanoparticles (Fe3O4@Os1-PMMA). Both types of nanoparticle present good dispersibility and fluorescence stability. The nanoparticles could be used as oxygen sensors that exhibited a high DO-sensitivity response in the range 0-39.30 mg/L, with a strong linear relationship. The nanoparticles have good magnetic properties, and so they could be recycled by magnet for further use. Recovered Fe3O4@Os1-PS still presented high stability after continued use in oxygen sensing for one month. Furthermore, Fe3O4@Os1-PS was employed for detecting the bacterial oxygen consumption of Escherichia coli (E-coli) to monitor the metabolism of bacteria. The results show that Fe3O4@Os1-PS provide high biocompatibility and non-toxicity. Polystyrene magnetic nanoparticles therefore present significant potential for application in biological oxygen sensing.

Paralinguistic singing attribute recognition using supervised machine learning for describing the classical tenor solo singing voice in vocal pedagogy.

Humans can recognize someone's identity through their voice and describe the timbral phenomena of voices. Likewise, the singing voice also has timbral phenomena. In vocal pedagogy, vocal teachers listen and then describe the timbral phenomena of their student's singing voice. In this study, in order to enable machines to describe the singing voice from the vocal pedagogy point of view, we perform a task called paralinguistic singing attribute recognition. To achieve this goal, we first construct and publish an open source dataset named Singing Voice Quality and Technique Database (SVQTD) for supervised learning. All the audio clips in SVQTD are downloaded from YouTube and processed by music source separation and silence detection. For annotation, seven paralinguistic singing attributes commonly used in vocal pedagogy are adopted as the labels. Furthermore, to explore the different supervised machine learning algorithm for classifying each paralinguistic singing attribute, we adopt three main frameworks, namely openSMILE features with support vector machine (SF-SVM), end-to-end deep learning (E2EDL), and deep embedding with support vector machine (DE-SVM). Our methods are based on existing frameworks commonly employed in other paralinguistic speech attribute recognition tasks. In SF-SVM, we separately use the feature set of the INTERSPEECH 2009 Challenge and that of the INTERSPEECH 2016 Challenge as the SVM classifier's input. In E2EDL, the end-to-end framework separately utilizes the ResNet and transformer encoder as feature extractors. In particular, to handle two-dimensional spectrogram input for a transformer, we adopt a sliced multi-head self-attention (SMSA) mechanism. In the DE-SVM, we use the representation extracted from the E2EDL model as the input of the SVM classifier. Experimental results on SVQTD show no absolute winner between E2EDL and the DE-SVM, which means that the back-end SVM classifier with the representation learned by E2E as input does not necessarily improve the performance. However, the DE-SVM that utilizes the ResNet as the feature extractor achieves the best average UAR, with an average 16% improvement over that of the SF-SVM with INTERSPEECH's hand-crafted feature set.

Association of long-term inhalation of caffeine-sodium benzoate with coronary heart disease in men.

OBJECTIVE: A possible correlation between caffeine and coronary heart disease (CHD) is controversial. The objective of this study was to explore the effect of long-term inhalation of caffeine-sodium benzoate (CSB) on the development of CHD in men, the severity of coronary artery lesions and the possible contributing effects of smoking. MATERIALS: A retrospective analysis was performed on 2,001 consecutive men who underwent selective coronary angiography. These men were assigned to a CSB inhalation group (CSB; 1 - 6 times/d, 274 - 1,644 mg/d, > 10 years; n = 326) or a non-inhalation group (non-CSB; n = 1,675). METHODS: The two groups were compared for the prevalence, onset age, and risk factors of CHD. The men were also stratified as CSB-only, smoking-only, combined CSB+ smoking, and the control (non-CSB+non-smoking). The prevalence, onset age, risk factors of CHD, and severity of coronary artery lesions and major adverse cardiovascular events (MACE) were compared among these groups. RESULTS: The prevalence of CHD in the CSB group was higher compared with the non-CSB group (91.72 vs. 86.09%, p < 0.01). In the CSB+smoking group, the percentages of men with CHD (93.11%) or > 70% stenosis of the coronary artery lesion (64.92%) were significantly higher than that of the smoking-only group (88.19 and 54.29%, respectively) or control (83.20 and 52.90%), while the percentage with stenosis involving the anterior descending branch was lower (62.30 vs. 72.29% and 74.17%, p < 0.01). CONCLUSION: Men who inhaled CSB long-term had a higher rate of CHD compared with those who did not take CSB. The combination of CSB inhalation and smoking appears to increase synergistically the risk and severity of CHD.

Easily Synthesized Polyaniline@Cellulose Nanowhiskers Better Tune Network Structures in Ag-Based Adhesives: Examining the Improvements in Conductivity, Stability, and Flexibility.

It is essential to develop a novel and versatile strategy for constructing electrically conductive adhesives (ECAs) that have superior conductivity and high mechanical properties. In this work, easily synthesized polyaniline@cellulose (PANI@CNs) nanowhiskers with a high aspect ratio and excellent solubility in 1,4-dioxane were prepared and added to conventional Ag-containing adhesives. A small amount of PANI@CNs can dramatically tune the structure of the ECAs' conductive network and significantly improve the conductivity of the ECAs. Good solubility of PANI@CNs in solvents brings excellent dispersion in the polymer matrix. Thus, a three-dimensional (3D) conducting network formed with dispersed PANI@CNs and Ag flakes can enhance the conductivity of ECAs. The conductivity of the ECAs (with 1.5 wt% PANI@CNs and 55 wt% Ag flakes) showed three orders of magnitude higher than that of the ECAs filled with 55 wt% Ag flakes and 65 wt% Ag flakes. Meanwhile, the integration of PANI@CNs with Ag flakes in polymer matrices also significantly enhanced the mechanical compliance of the resulted ECAs. The resistivity remained unchanged after rolling the PANI@CNs-containing ECAs' film into a 4 mm bending radius for over 1500 cycles. A bendable printed circuit was fabricated using the above PANI@CNs-containing ECAs, which demonstrated their future potential in the field of flexible electronics.