Self-Healable and Recyclable Biomass-Derived Polyurethane Networks through Carbon Dioxide Immobilization

Due to growing environmental issues, research on carbon dioxide (CO2) use is widely conducted and efforts are being made to produce useful materials from biomass-derived resources. However, polymer materials developed by a combined strategy (i.e., both CO2-immobilized and biomass-derived) are rare. In this study, we synthesized biomass-derived poly(carbonate-co-urethane) (PCU) networks using CO2-immobilized furan carbonate diols (FCDs) via an ecofriendly method. The synthesis of FCDs was performed by directly introducing CO2 into a biomass-derived 2,5-bis(hydroxymethyl)furan. Using mechanochemical synthesis (ball-milling), the PCU networks were effortlessly prepared from FCDs, erythritol, and diisocyanate, which were then hot-pressed into films. The thermal and thermomechanical properties of the PCU networks were thoroughly characterized by thermogravimetric analysis, differential scanning calorimetry, dynamic (thermal) mechanical analysis, and using a rheometer. The self-healing and recyclable properties of the PCU films were successfully demonstrated using dynamic covalent bonds. Interestingly, transcarbamoylation (urethane exchange) occurred preferentially as opposed to transcarbonation (carbonate exchange). We believe our approach presents an efficient means for producing sustainable polyurethane copolymers using biomass-derived and CO2-immobilized diols

An Urban Autodriving Algorithm Based on a Sensor-Weighted Integration Field with Deep Learning

This paper proposes two algorithms for adaptive driving in urban environments: The first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms utilize three kinds of sensors, namely vision, Light Detection and Range (LiDAR), and GPS sensors, and decide critical motions for autonomous vehicle, such as steering angles and vehicle speed. SSCNN, which is used for lane recognition, has 2.7 times faster processing speed than the existing spatial CNN method. Additionally, the dataset for SSCNN was constructed by considering both normal and abnormal driving in 7 classes. Thus, lanes can be recognized by extending lanes for special characteristics in urban settings, in which the lanes can be obscured or erased, or the vehicle can drive in any direction. SWIF generates a two-dimensional matrix, in which elements are weighted by integrating both the object data from LiDAR and waypoints from GPS based on detected lanes. These weights are the integers, indicating the degree of safety. Based on the field formed by SWIF, the safe trajectories for two vehicles’ motions, steering angles, and vehicle speed are generated by applying the cost field. Additionally, to flexibly follow the desired steering angle and vehicle speed, the Proportional-Integral-Differential (PID) control is moderated by an integral anti-windup scheme. Consequently, as the dataset considers characteristics of the urban environment, SSCNN is able to be adopted for lane recognition on urban roads. The SWIF algorithm is also useful for flexible driving owing to the high efficiency of its sensor integration, including having a resolution of 2 cm per pixel and speed of 24 fps. Thus, a vehicle can be successfully maneuvered with minimized steering angle change, without lane or route departure, and without obstacle collision in the presence of diverse disturbances in urban road conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.TRU

An Urban Autodriving Algorithm Based on a Sensor-Weighted Integration Field with Deep Learning

This paper proposes two algorithms for adaptive driving in urban environments: the first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms utilize three kinds of sensors, namely vision, Light Detection and Range (LiDAR), and GPS sensors, and decide critical motions for autonomous vehicle, such as steering angles and vehicle speed. SSCNN, which is used for lane recognition, has 2.7 times faster processing speed than the existing spatial CNN method. Additionally, the dataset for SSCNN was constructed by considering both normal and abnormal driving in 7 classes. Thus, lanes can be recognized by extending lanes for special characteristics in urban settings, in which the lanes can be obscured or erased, or the vehicle can drive in any direction. SWIF generates a two-dimensional matrix, in which elements are weighted by integrating both the object data from LiDAR and waypoints from GPS based on detected lanes. These weights are the integers, indicating the degree of safety. Based on the field formed by SWIF, the safe trajectories for two vehicles’ motions, steering angles, and vehicle speed are generated by applying the cost field. Additionally, to flexibly follow the desired steering angle and vehicle speed, the Proportional-Integral-Differential (PID) control is moderated by an integral anti-windup scheme. Consequently, as the dataset considers characteristics of the urban environment, SSCNN is able to be adopted for lane recognition on urban roads. The SWIF algorithm is also useful for flexible driving owing to the high efficiency of its sensor integration, including having a resolution of 2 cm per pixel and speed of 24 fps. Thus, a vehicle can be successfully maneuvered with minimized steering angle change, without lane or route departure, and without obstacle collision in the presence of diverse disturbances in urban road conditions

Complete mitochondrial genome of two shorebirds (Charadriiformes: Scolopacidae), great knot (Calidris tenuirostris) and bar-tailed godwit (Limosa lapponica)

The mitochondrial genome of Calidris tenuirostris and Limosa lapponica were described using the whole mitochondrial genome obtained from Illumina Next-Generation Sequencing (NGS) technology. Total length of the mitogenome of C. tenuirostris was 16,732bp with slight A+T bias (55.3%). Genome size of L. lapponica was 16,773bp long and A+T biased (56.3%). Both gemones consisting of 2 rRNAs, 13 protein-coding genes, 22 tRNA genes and 1 non-coding regions. This is the first report of complete mitogenomes of these two shorebird species, (C. tenuirostris and of L. lapponica). We observed paraphyletic relationship among the species in the Family Scolopacidae. Also our result showed analogous patterns with the previous studies on the parallel relationships of shorebird species. This study provides basic genetic information for help in understanding phylogenetic relationships . within the Charadriiformes

Torsionally Responsive Tropone-Fused Conjugated Polymers

Torsionally responsive molecular systems can change their electronic properties according to the dihedral angles and can be utilized as sensory materials. We have designed and synthesized novel tropone-fused conjugated polymers <b>PBTr</b>, <b>PBTr-T</b>, and <b>PBTr-Tz</b> that showed interesting dihedral-angle-dependent variations in UV–vis absorptions. Tropone-fused thiophene derivatives were prepared from one-step condensation of thiophene-3,4-dialdehyde and aliphatic ketones via a modular, facile, and high-yielding method. Subsequent halogenation and Stille cross-coupling polymerization with a bis­(stannyl)­benzo­dithiophene resulted in a tropone-fused conjugated polymer <b>PBTr</b>. We were also able to prepare thiophene- and thiazole-bridged polymers, <b>PBTr-T</b> and <b>PBTr-Tz</b>, respectively, using similar synthetic methods. Electronic absorptions of the newly synthesized <b>PBTrs</b> were measured in solutions and in films states. Substantial red-shifts occurred in the case of thiophene-bridged <b>PBTr-T</b>, whereas almost no shift was observed for thiazole-bridged <b>PBTr-Tz</b>. We attributed this to the substantial change in the torsional angle between the tropone-fused thiophene moiety and thiophene, which was further supported by density functional theory (DFT) calculations. Similar spectral changes of UV–vis absorptions were observed when a poor solvent (methanol) was introduced to a chloroform solution of <b>PBTr-T</b>. Reverse torsional angle variations were realized with initially planar <b>PBTr-Tz</b> by introducing steric hindrance through protonation on the thiazole rings. We believe that torsionally responsive tropone-fused conjugated polymers are promising as novel platforms for sensory applications

Selective dispersion of single-walled carbon nanotubes by binaphthyl-based conjugated polymers: Integrated experimental and simulation approach

Hybrids of ??-conjugated polymers and single-walled carbon nanotubes (SWNTs) are an intriguing class of materials owing to their interesting electric and optoelectronic properties. Herein, we synthesized three types of 1,1???-binaphthyl-incorporated conjugated polymers with thiophene bridges. It was found that the molecular structure of the ??-conjugated polymers affected the selective dispersion of individual SWNT species: the hexyl-substituted PBHT preferred (8,7) SWNT while polymers with no alkyl groups on the thiophenes (PBT and PB2T) preferred (8,6) species. Molecular dynamics (MD) simulations also revealed that the polymers were able to wrap around SWNTs and showed selective interactions with the SWNT species.clos