Poly(3-hydroxybutyrate) and Poly(3-hydroxybutyrate-co-3-hydroxyvalerate): Structure, Property, and Fiber

Poly(3-hydroxybutyrate) [P(3HB)] and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) [P(3HB-co-3HV)] are produced by various microorganisms as an intracellular carbon and energy reserve from agricultural feedstocks such as sugars and plant oils under unbalanced growth conditions. P(3HB) and P(3HB-co-3HV) have attracted the attention of academia and industry because of its biodegradability, biocompatibility, thermoplasticity, and plastic-like properties. This review first introduced the isodimorphism, spherulites, and molecular interaction of P(3HB) and P(3HB-co-3HV). In addition, the effects of 3HV content on the melting temperature and crystallization rate were discussed. Then the drawbacks of P(3HB) and P(3HB-co-3HV) including brittleness, narrow melt processing window, low crystallization rate, slow biodegradation rate in body, and so on were summarized. At last, the preparation, structure, and properties of P(3HB) and P(3HB-co-3HV) fiber were introduced

Effects of EGR rates on combustion and emission characteristics in a diesel engine with n-butanol/PODE3-4/diesel blends

An experimental investigation is conducted on the influence of EGR (Exhaust Gas Recirculation) rates (0–40%) on the combustion and emission characteristics of n-butanol/diesel/PODE3-4 blends at low-temperature combustion mode in diesel engine. The results show that at identical EGR rate, compared to D100 (diesel fuel), the peak values both of the mean cylinder pressure and the heat release rate of BD20 (20% butanol and 80% diesel in volume) are increased, ignition delay is extended, and the brake thermal efficiency is enhanced. Concerning BD20 blended with PODE3-4, the ignition delay is shortened, while both the brake thermal efficiency and the combustion efficiency increase. At the EGR rate below 30%, as the EGR rate grows, the effects on emission of soot, CO and HC are not significant, while the emission of NOx is sharply reduced; when the EGR rate is above 30%, as it grows, the emissions of soot, CO, and HC drastically rise. As EGR rate grows, the total particulate matter (PM) number concentrations of four fuels firstly decline and then rise, the total PM mass concentrations keep stable firstly and then rise drastically. As the proportion of added PODE3-4 in BD20 grows, the particle geometric mean diameters further decrease

Upcycling of PET oligomers from chemical recycling processes to PHA by microbial co-cultivation

Polyethylene terephthalate (PET) is the most widely consumed polyester plastic and can be recycled by many chemical processes, of which glycolysis is most cost-effective and commercially viable. However, PET glycolysis produces oligomers due to incomplete depolymerization, which are undesirable by-products and require proper disposal. In this study, the PET oligomers from chemical recycling processes were completely bio-depolymerized into monomers and then used for the biosynthesis of biodegradable plastics polyhydroxyalkanoates (PHA) by cocultivation of two engineered microorganisms Escherichia coli BL21 (DE3)-LCCICCG and Pseudomonas putida KT2440-ΔRDt-ΔZP46C-M. E. coli BL21 (DE3)-LCCICCG was used to secrete the PET hydrolase LCCICCG into the medium to directly depolymerize PET oligomers. P. putida KT2440-ΔRDt-ΔZP46C-M that mastered the metabolism of aromatic compounds was engineered to accelerate the hydrolysis of intermediate products mono-2- (hydroxyethyl) terephthalate (MHET) by expressing IsMHETase, and biosynthesize PHA using ultimate products terephthalate and ethylene glycol depolymerized from the PET oligomers. The population ratios of the two microorganisms during the co-cultivation were characterized by fluorescent reporter system, and revealed the collaboration of the two microorganisms to bio-depolymerize and bioconversion of PET oligomers in a single process. This study provides a biological strategy for the upcycling of PET oligomers and promotes the plastic circular economy

KPNet: Towards Minimal Face Detector

The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors. In this work, we find that the appearance feature of a generic face is discriminative enough for a tiny and shallow neural network to verify from the background. And the essential barriers behind us are 1) the vague definition of the face bounding box and 2) tricky design of anchor-boxes or receptive field. Unlike most top-down methods for joint face detection and alignment, the proposed KPNet detects small facial keypoints instead of the whole face by in a bottom-up manner. It first predicts the facial landmarks from a low-resolution image via the well-designed fine-grained scale approximation and scale adaptive soft-argmax operator. Finally, the precise face bounding boxes, no matter how we define it, can be inferred from the keypoints. Without any complex head architecture or meticulous network designing, the KPNet achieves state-of-the-art accuracy on generic face detection and alignment benchmarks with only $\sim1M$ parameters, which runs at 1000fps on GPU and is easy to perform real-time on most modern front-end chips.Comment: AAAI 202

On the impact of the digital economy on urban resilience based on a spatial Durbin model

Based on panel data from 31 provinces in China between 2011 and 2020, we empirically studied the impact of the digital economy on urban resilience using fixed-effects models, threshold-effects models and spatial Durbin models. Our research findings indicate that (1) the development of the digital economy has a significant positive impact on the enhancement of urban resilience; (2) the promotional effect of the digital economy on urban resilience varies significantly across different regions; (3) the promotional effect of the digital economy on urban resilience exhibits a typical double-threshold characteristic due to the different levels of development in digital financial inclusion and (4) the digital economy has a positive spillover effect on the urban resilience of surrounding areas. Therefore, we should actively promote the development of the digital economy and digital financial inclusion, making the digital economy a new driving force for promoting urban resilience

The impact of population agglomeration on ecological resilience: Evidence from China

Due to climate change and human activities, ecological and environmental issues have become increasingly prominent and it is crucial to deeply study the coordinated development between human activities and the ecological environment. Combining panel data from 31 provinces in China spanning from 2011 to 2020, we employed a fixed-effects model, a threshold regression model, and a spatial Durbin model to empirically examine the intricate impacts of population agglomeration on ecological resilience. Our findings indicate that population agglomeration can have an impact on ecological resilience and this impact depends on the combined effects of agglomeration and crowding effects. Also, the impact of population agglomeration on ecological resilience exhibits typical dual-threshold traits due to differences in population size. Furthermore, population agglomeration not only directly impacts the ecological resilience of the local area, but also indirectly affects the ecological resilience of surrounding areas. In conclusion, we have found that population agglomeration does not absolutely impede the development of ecological resilience. On the contrary, to a certain extent, reasonable population agglomeration can even facilitate the progress of ecological resilience