Soft self-assembly of Weyl materials for light and sound

Soft materials can self-assemble into highly structured phases which replicate at the mesoscopic scale the symmetry of atomic crystals. As such, they offer an unparalleled platform to design mesostructured materials for light and sound. Here, we present a bottom-up approach based on self-assembly to engineer three-dimensional photonic and phononic crystals with topologically protected Weyl points. In addition to angular and frequency selectivity of their bulk optical response, Weyl materials are endowed with topological surface states, which allows for the existence of one-way channels even in the presence of time-reversal invariance. Using a combination of group-theoretical methods and numerical simulations, we identify the general symmetry constraints that a self-assembled structure has to satisfy in order to host Weyl points, and describe how to achieve such constraints using a symmetry-driven pipeline for self-assembled material design and discovery. We illustrate our general approach using block copolymer self-assembly as a model system.Comment: published version, SI are available as ancillary files, code and data are available on Zenodo at https://doi.org/10.5281/zenodo.1182581, PNAS (2018

Initial Adherence to Autotitrating Positive Airway Pressure Therapy: Influence of Upper Airway Narrowing

ObjectivesThere is still debate concerning the reason for the high initial failure rate of positive airway pressure (PAP) treatment. The objective of this study is to investigate the factors of the initial adherence to PAP, with an emphasis on the role of upper airway narrowing.MethodsThe patients were divided into two groups according to the continuation of therapy within the first three months of treatment. The demographic and polysomnographic findings, the minimal nasal cross sectional area (MCA), the degree of palatine tonsilar hypertrophy (PTH) and the modified Mallampati grade of the oropharynx inlet (Orophx) were compared between the study groups.ResultsAmong 36 patients, 23 continued the auto-adjusting positive airway pressure (APAP) therapy (the adherent group) and 13 discontinued APAP within three months (the non-adherent group). The apnea-hypopnea index (AHI) was significantly higher in the adherent group than in the non-adherent group (P<0.001). The AHI distributions of the two groups are extremely different. Thirteen of the 23 patients in the adherent group had an AHI of more than 60/hr, while none of the patients in the non-adherent group had an AHI of more than 60/hr. In the patients with an AHI from 15 to 60/hr, the MCA at the wide side of the nasal cavity and the sum of the MCAs of both sides were significantly larger in the adherent group than those values in the non-adherent group (P=0.004). The PTH and the Orophx were not significantly different between the two groups.ConclusionAHI is a definite significant factor of adherence to APAP therapy. The dimension of the nasal cavity has an influence on initial APAP adherence in the patients who have a not too high level of AHI

A Relationship between the Obstructive Sleep Apnea Syndrome and the Erythrocyte Sedimentation Rate

ObjectivesThe erythrocyte sedimentation rate (ESR) is a marker for inflammation, and it has been identified as a risk factor for atherothrombotic cardiovascular disease. The aim of this study was to determine the relationship between the plasma ESR level and nocturnal oxygen desaturation or other polysomnographic variables and to examine the role of obesity in patients with obstructive sleep apnea syndrome (OSAS).MethodsThis retrospective study included 72 patients with a diagnosis of OSAS who underwent overnight polysomnography and routine blood tests between July and December of 2005. We compared the plasma ESR level with the sum of all the polysomnographic variables and divided the patient group into obese and non-obese patients.ResultsThe mean ESR level was 8.45 mm/hr. There was a significant difference in the ESR level between genders (P<0.001). A significant correlation was found between the percentage of time spent at a SpO2 below 90% and the ESR level in the obese group (BMI ≥25, N=43, P=0.012). In addition, the ESR levels had a positive correlation with age in the obese group (P=0.002). However, there was no significant correlation with the percentage of time spent at a SpO2 below 90% in the whole group of patients and in the non-obese group (BMI <25, N=29). The ESR level showed no correlation with the other polysomnographic variables.ConclusionThe duration of deoxygenation in obese patients with OSAS may be associated with the ESR level which is an independent predictor of cardiovascular disease

Tension-induced twist of twist-spun carbon nanotube yarns and its effect on their torsional behavior

Abstract Twist-spun carbon nanotube (CNT) yarns exhibit a large and reversible rotational behavior under specific boundary conditions. In situ polarized Raman spectroscopy revealed that a tension-induced twist provides reversibility to this rotation. The orientation changes of individual CNTs were followed when twist-spun CNT yarns were untwisted and subsequently retwisted. Twist-spun CNT yarn, when untwisted and subsequently retwisted under the one-ended tethered boundary condition, showed irreversible orientation changes of the individual CNTs due to snarls formed during the untwisting operation, which resulted in macroscopic irreversible rotational behavior of the CNT yarns. In contrast, the orientation changes of the individual CNTs in twist-spun CNT yarn, when operated under the two-ended tethered boundary condition, were hysteretically reversible due to a tension-induced twist, which has not been reported previously. Indeed, the tension-induced twist was observed by following the orientation change of individual CNTs in elongated CNT yarns, which simulated the deformational behavior of the CNT yarn rotated under the two-ended tethered boundary condition

High Purity Single Wall Carbon Nanotube by Oxygen-Containing Functional Group of Ferrocene-Derived Catalyst Precursor by Floating Catalyst Chemical Vapor Deposition

Single wall carbon nanotubes (SWCNTs) were synthesized using oxygen-containing ferrocene derived catalysts. The mechanism of synthesizing carbon nanotubes was clarified by the catalyst’s exothermic or endothermic decomposition processes. By monitoring the decomposition process of ferrocene-derived catalyst precursors with and without sulfur, we found that the types of oxygen function groups closely influence catalyst formation and nanotube growth. The ferrocene-derived catalyst precursors have a different oxygen containing groups, which are hydroxyl (–OH, ferrocenenemethanol) and carbonyl (C=O, acetylferrocene, and 1,1′-diacetylferrocene). The sulfur chemical state (S 2p) on synthesized catalyst particles using acetylferrocene and 1,1′-diacetylferrocene has more sulfate (SO42−) than others, and there also is a carbon state (C-S-C). The catalyst particle using ferrocenemethanol predominant formed metal–sulfur bonds (such as S2− and Sn2−). The hydroxyl group (–OH) of ferrocenemethanol enhanced the etching effect to remove amorphous carbon and prevented oxidation on the catalyst particle surfaces; however, the carbonyl group (C=O) of acetylferrocene reacted with the catalyst particles to cause partial oxidation and carbon dissociation on the surface of the catalyst particles. The partial oxidation and carbon contamination on catalyst particles controlled the activity of the catalyst. The DFT study revealed that the ferrocene-derived catalyst precursor was dissociated according to following process: the functional groups (such as CH3CO and COH) => first Cp ligands => second Cp ligands. The pyrolysis and release of Fe ions were delayed by the functional groups of ferrocene-derived precursors compared to ferrocene. The thermal-decomposition temperature of the catalyst precursor was high, the decomposition time was be delayed, affecting the formation of catalyst particles and thus making smaller catalyst particles. The size and composition of catalyst particles not only affect the nucleation of CNTs, but also affect physical properties. Therefore, the IG/ID ratio of the CNTs changed from 74 to 18 for acetylferrocene and ferrocene, respectively. The purity also increased from 79 to 90% using ferrocene-derived precursors

Pore Size Distribution Analysis of Mesoporous TiO2 Spheres by H-1 Nuclear Magnetic Resonance (NMR) Cryoporometry

Mesoporous TiO2, spheres with various pore sizes were prepared by varying the calcination temperature in the range of 300-700 degrees C. Increasing, calcination temperature was found to increase the crystal size, decrease the surface area, and increase the pore size. The morphologies of mesoporous TiO2 spheres consist of well-defined spherical shapes of monodisperse sizes near 0.8 mu m. To determine the pore size distributions (PSDs) of these mesoporous TiO2 spheres, Ill nuclear magnetic resonance (NMR) cryoporometry and Barrett-Joyner-Halenda (BJH) analysis were conducted. NMR cryoporometry is based on the theory of the melting point depression (MPD) of probe molecule confined within a pore. which is dependent on the pore diameter. MPD was determined by analyzing the variation of the NMR spin echo intensity with temperature. From the resulting spin echo intensity versus temperature (l-T) curves, it was found that the maximum MPD of a probe molecule confined within the pores of mesoporous TiO2 decreases with increasing calcination temperature; that is, the pore size increases with increasing calcination temperature. Because mesoporous TiO2 spheres consist of aggregates of nanocrystallite TiO2 and mesopores located at intercrystallites. in increase in the calcination temperature induces an increase in the crystallite size and, thus, in the pore size because the small pores collapse and die large pores increase in size. We also confirmed by BJH analysis that the pore size of mesoporous TiO2 increases with increasing calcination temperature. This trend is hi agreement with our H-1 NMR cryoporometry results. Overall, these findings indicate that NMR cryoporometry is a very effective method for determining the PSDs of mesoporous TiO2 spheres

Automated Detection of Greenhouse Structures Using Cascade Mask R-CNN

Automated detection of the content of images remains a challenging problem in artificial intelligence. Hence, continuous manual monitoring of restricted development zones is critical to maintaining territorial integrity and national security. In this regard, local governments of the Republic of Korea conduct four periodic inspections per year to preserve national territories from illegal encroachments and unauthorized developments in restricted zones. The considerable expense makes responding to illegal developments difficult for local governments. To address this challenge, we propose a deep-learning-based Cascade Mask region-based convolutional neural network (R-CNN) algorithm designed to perform automated detection of greenhouses in aerial photographs for efficient and continuous monitoring of restricted development zones in the Republic of Korea. Our proposed model is regional-based because it was optimized for the Republic of Korea via transfer learning and hyperparameter tuning, which improved the efficiency of the automated detection of greenhouse facilities. The experimental results demonstrated that the mAP value of the proposed Cascade Mask R-CNN model was 83.6, which was 12.83 higher than baseline mask R-CNN, and 0.9 higher than Mask R-CNN with hyperparameter tuning and transfer learning considered. Similarly, the F1-score of the proposed Cascade Mask R-CNN model was 62.07, which outperformed those of the baseline mask R-CNN and the Mask R-CNN with hyperparameter tuning and transfer learning considered (i.e., the F1-score 52.33 and 59.13, respectively). The proposed improved Cascade Mask R-CNN model is expected to facilitate efficient and continuous monitoring of restricted development zones through routine screening procedures. Moreover, this work provides a baseline for developing an integrated management system for national-scale land-use planning and development infrastructure by synergizing geographical information systems, remote sensing, and deep learning models