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Silica nanoparticles (nano-silica) were used as synergistic agents with ammonium polyphosphate (APP) and pentaerythritol (PER) to enhance flame retardancy of polypropylene (PP) in this research. The composites were prepared using a melt-mixing method. The influences of nano-silica on the fire performance of composites were thoroughly discussed, which promotes understanding of nano-silica on the flame-retardant performance of polypropylene composite. Scanning electron microscope (SEM) and energy-dispersive spectrometer (EDS) results indicated that the nano-silica with a diameter of about 95 ± 3.9 nm were dispersed favorably in the composite matrix, which might elevate its synergistic effect with intumescent flame retardant and improve the flame retardancy of polypropylene composite. The synergistic effects between nano-silica and intumescent flame retardant on PP composites were studied using the limiting oxygen index (LOI), UL-94 test, and cone calorimeter test (CCT). The total amount of flame retardant was maintained at 30%. When the dosage of nano-silica was 1 wt.%, the LOI value of PP/IFR/Si1.0 composite reached 27.3% and its UL-94 classification reached V-1. Based on the parameters of the CCT, the introduction of nano-silica induced composites with depressed heat release rate (HRR) and peak heat release rate (PHRR). The PHRR of PP/IFR/Si0.5 was only 295.8 kW/m2, which was 17% lower than that of PP/IFR. Moreover, the time to PHRR of PP/IFR/Si0.5 was delayed to 396 s, which was about 36 s later than that without nano-silica. EDS was used to quantitatively analyze the distribution of silica in charred residue. The EDS results indicated that the silica tended to accumulate on the surface during the fire. The surface accumulation characteristic of silica endows it with the enhanced flame-retardant properties of polypropylene composite at a very small dosage (as low as 1 wt.%).

Self-limiting assembly of particles represents the state-of-the-art controllability in nanomanufacturing processes where the assembly stops at a designated stage, providing a desirable platform for applications requiring delicate thickness control such as optics, electronics, and catalytic systems. Most successes in self-limiting assembly are limited to self-assembled monolayers (SAMs) of small molecules on inorganic, chemically homogeneous rigid substrates (e.g., Au and SiO

As the largest carbon emitter in the world, China is facing increasing challenge to reduce CO2 emissions. Given this issue, exploring the influencing factors is of great significance for scientific low-carbon emission policymaking. Although previous literature has explored the effects of urbanization on CO2 emissions, the impact of the space of flow on urban carbon emissions have been less explored. Due to the increasing connection between cities, its impact on urban carbon emissions cannot be ignored. Thus, this paper takes the space of flows into account as an aspect of urbanization to supplement the existing literature and empirically examines the multiple effects of urbanization on CO2 emissions in the Pearl River Delta (PRD) urban agglomeration. By using a STIRPAT model, statistical data, and web crawler data, we examined impacts of different types of urbanization on CO2 emissions. Our empirical results show that: (1) Within the PRD urban agglomeration, urban linkage intensity is strongly connected to urban socioeconomic growth, establishing a geographical structure with Guangzhou and Shenzhen as the double core. (2) Our results show that urbanization exerts two opposite effects on CO2 emissions: positively connects carbon emissions with population urbanization, integrated urban linkage flow, and energy intensity, whereas economic urbanization and social urbanization are shown to be negatively correlated. However, spatial urbanization has no significant positive effect on urban CO2 emissions. (3) It is worth noting that urban linkage flows are the second most important factor affecting urban carbon emissions after economic urbanization. Our study could formulate effective planning suggestions for future CO2 emission reduction paths and development modes in the PRD.

Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D$^2$NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables robust and noise-resilient Multi-task Learning in D$^2$NNs. Our experimental results demonstrate significant improvements in versatility and hardware efficiency, and also demonstrate the robustness of proposed multi-task D$^2$NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task., Comment: 23 pages including references and Supplementary Information. To appear in Nature Scientific Reports

Recent studies have demonstrated that in visual working memory (VWM), only items in an active state can guide attention. Further evidence has revealed that items with higher perceptual salience or items prioritised by a valid retro-cue in VWM tend to be in an active state. However, it is unclear which factor (perceptual salience or retro-cues) is more important for influencing the item state in VWM or whether the factors can act concurrently. Experiment 1 examined the role of perceptual salience by asking participants to hold two features with relatively different perceptual salience (colour vs. shape) in VWM while completing a visual search task. Guidance effects were found when either colour or both colour and shape in VWM matched one of the search distractors but not when shape matched. This demonstrated that the more salient feature in VWM can actively guide attention, while the less salient feature cannot. However, when shape in VWM was cued to be more relevant (prioritised) in Experiment 2, we found guidance effects in both colour-match and shape-match conditions. That is, both more salient but non-cued colour and less salient but cued shape could be active in VWM, such that attentional selection was affected by the matching colour or shape in the visual search task. This suggests that bottom-up perceptual salience and top-down retro-cues can jointly determine the active state in VWM.

Mg–Al hydrotalcites were prepared by the forward, reverse and simultaneous titration coprecipitation methods, respectively. The effects of coprecipitation method on Mg–Al hydrotalcite properties were investigated. The resulting catalysts were evaluated for their performance in the synthesis of diethylene glycol di-(methyl carbonate). The results showed that there were no other crystalline phases, but a Mg–Al hydrotalcite structure in any of the samples and HTS prepared by the simultaneous titration method had the best crystallinity. Only the Mg–Al hydrotalcites prepared by the forward and simultaneous titration coprecipitation methods had a similar Mg/Al ratio as with the salt solution. The coprecipitation method affected the pore structure, specific surface area and basicity distribution of Mg–Al hydrotalcite and thus affected its catalytic performance. HTS, which had a relatively small pore size, moderate specific surface area and considerable basicity, displayed superior catalytic performance (91.6% yield) under optimized conditions. The catalyst could be easily separated and reused. Its activity did not decrease apparently, and the yield of target product maintained around 90% after five circles.

We have presented a simple method for situ fabrication of photoluminescent lanthanide metallogels. A series of lanthanide metallogels could be rapidly formed through mixing the aqueous solution of Tb3+ or Eu3+ and organic solution of synthesized aromatic carboxylic ligands (DCn, n = 14, 16,18) at room temperature. The gelation tests revealed that the ligand with longer alkyl chain (DC18) showed stronger gelling abilities, indicating that the structure of non-coordinated groups in ligand molecules could affect self-assembly processes and, hence, the microstructures and properties of lanthanide metallogels. FT-IR measurements demonstrated that the driving force of formation of the metallogels was the coordination interaction between DCn and Tb3+ or Eu3+. XRD analysis revealed that DC14 metallogels took a mixture of hexagonal and tetragonal packing modes. All obtained metallogels have shown typical luminescent emissions of the lanthanide complex. In particular, DC18/Tb(NO3)3 metallogel in DMF/H2O (v:v = 1:1) has shown excellent self-supporting, self-healing, film-forming, and rheological properties.

We demonstrate a reconfigurable diffractive optical neural network based on cost-effective spatial light modulators. We explored various diffraction calculation methods to match trainable models with experimental results. This lays the foundation for fast system deployment.

Glycol dicarbonate is successfully synthesized via transesterification of glycol with dimethyl carbonate. A catalyst screening is performed by studying the effect of acidity and alkalinity on the catalytic performance. The results indicate that compared with acidic catalysts, high conversions and yields are obtained with basic catalysts. Ca(OH)2, with medium-strength alkalinity, exhibits excellent catalytic activity with glycol dicarbonate yields of 95%-99% when using a fractionation reaction system.

Carbon intensity (CI) is a valuable indicator of the balances struck by the world's governments between economic growth and environmental issues. This study investigates spatiotemporal variations in the CI levels related to energy consumption, as well as the spatial heterogeneity of its driving forces, in 208 countries globally during 2000–2018. To do this, we obtained data from the International Energy Agency (IEA) and the World Bank, employing methods of exploratory spatial data analysis (ESDA) and standard deviation ellipse (SDE) in order to analyze CI's spatiotemporal variations. We also performed a geographically weighted regression (GWR) analysis to determine the spatial heterogeneity of CI and the strength of its influencing factors. Our results reveal that: (1) Carbon emissions from energy consumption increased, while CI decreased globally, with the CI of most countries and regions declining significantly. (2) Global CI evidenced a heterogeneous spatial distribution, with higher-value areas concentrated in Asia and lower-value areas in Africa and Western Europe; obvious spatial agglomeration was also presented, especially with respect to High-High and Low-Low agglomerations, and the gravity center point moved from the northeast to the southwest. (3) The 8 influencing factors investigated in this study all had effective explanatory power in relation to CI globally. These factors showed significant spatial heterogeneity, and energy structure was the only factor to have a fully positive influence on CI, while foreign direct investment, foreign trade openness, industrial structure, total population, and energy intensity, mainly exerted a positive influence, and the urbanization rate and GDP per capita exerted a negative influence. By clarifying the spatiotemporal variations characteristics of global CI and the spatial heterogeneity of its influencing factors, this study provides a targeted reference for reducing CI and promoting sustainable development, globally.

We present a graphene-based optoelectronic artificial intelligence accelerator using spatial light modulators and photodetectors, design a method of performing accurate calculations using imperfect components, and show the system capability of accelerating a few representative algorithms.

Learning performance analysis is such a research field that draws much attention from researchers though it has just been emerged in recent years. On the one hand, analyzing learning behaviors can help learners to choose their learning methods and allocate their study time in a more appropriate way. On the other hand, learning analysis can provide valuable feedbacks for teachers and administrators to improve teaching efficiency and quality. This paper studies and analyzes more than 640,000 learning data from the MOOC platform edX. A tree-based model along with an information gain measure is applied to identify the usefulness of data features. A back-propagation neural network model is further adopted to train data and achieve a prediction model of learning performance. In addition, a genetic algorithm calculates learning score conditions and return feedbacks as suggestions to learners. Experiment results demonstrate the effectiveness of the utilization of the methods in the predication of online learning performance.

Lithium-ion batteries have been widely used in many fields. The state-of-health is necessary and important for battery performance evaluation and lifetime prediction. A reliable state-of-health estimation is essential to help batteries work in a safe and suitable condition. In this paper, a novel state-of-health estimation approach is proposed for lithium-ion batteries based on statistical knowledge. An improved battery model, which combines the open-circuit-voltage modeling and the Thevenin equivalent circuit model, is proposed to improve the model accuracy and study the relation between internal parameters and states of the battery. The joint extended Kalman filter-recursive-least squares algorithm is employed to estimate battery state-of-charge and identify the model parameters and open-circuit-voltage simultaneously. Then a particle swarm optimization-least square support vector regression approach is employed to give a reliable state-of-health estimation result with high accuracy and good generalization ability, where the particle swarm optimization algorithm is used to improve the algorithm ability of global optimization. In order to verify the accuracy of the proposed method, static and dynamic current profile tests are carried out on lithium iron phosphate batteries in different aging levels. The experimental results indicate that the proposed method can present suitability for state-of-health estimation with high accuracy.

The catalytic synthesis of alkyl-α,ω-diyl dimethyl dicarbonate was achieved firstly by solid base via transesterification of aliphatic diols with dimethyl carbonate under atmospheric pressure. The ...

Supplemental material, Supporting_Information for Catalytic synthesis of glycol dicarbonate from glycol and dimethyl carbonate by transesterification by Ruiyang Chen, Tong Chen, Xiaojia Hu, Youkun Fan and Gongying Wang in Journal of Chemical Research

In contrast to circuit made by rigid material, flexible electronic devices are fabricated by flexible conductive material and have potential future in field of wearable electronic devices, biomedicine and intelligent sensor. Liquid metal is a kind of metal or alloy material that can remain liquid at room temperature. In recent years, it has been widely used in the field of stretchable electronics. This research applied adhesion different of liquid metal on different materials’ surface to print liquid metal circuit of certain pattern on thermal transfer paper and trans-printed the circuit to flexible base to fabricate stretchable and flexible pressure sensor. In the research we tested the electrical property of pressure sensor and applied the results to tactile feedback research of intelligent robot. Not only does this liquid metal pressure sensor have simple structure and low in cost, but is suitable for large area and mass production. We believe this pressure sensor can be widely used in field of human-computer interaction and robot intelligent sensing.

Optical and optoelectronic approaches of performing matrix-vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into the system can further improve the performance thanks to their extraordinary properties, but the non-uniformity and variation of nanostructures in the macroscopic scale pose severe limitations for large-scale hardware deployment. Here, we report a new optoelectronic architecture consisting of spatial light modulators and photodetector arrays made from graphene to perform MVM. The ultrahigh carrier mobility of graphene, nearly-zero-power-consumption electro-optic control, and extreme parallelism suggest ultrahigh data throughput and ultralow-power consumption. Moreover, we develop a methodology of performing accurate calculations with imperfect components, laying the foundation for scalable systems. Finally, we perform a few representative ML algorithms, including singular value decomposition, support vector machine, and deep neural networks, to show the versatility and generality of our platform., 20 pages, 4 figures

Accurate identification of varieties (clones) and knowledge of their genetic relationships are essential for poplar breeding and variety management. In this study, 21 elite poplar varieties of Tacamahaca and Aigeiros in China were fingerprinted using amplified fragment length polymorphism (AFLP) markers. Four AFLP primer pairs developed generated totally 181 AFLP polymorphic fragments, and in particular, each primer pair generated fingerprint profiles specific to each of the tested varieties. The genetic relationships among the varieties were evaluated by dendrograms and multidimensional scaling (MDS). The results showed that the tested poplar can be classified into five groups, and indicated the clear separation of varieties of different sections of poplar and the primary distinction between native and exotic poplar varieties. This study indicated that tested poplar varieties could be identified by their fingerprint profiles and that genetic relationships deduced from the study were consistent with their genealogy. In addition, our results demonstrated that AFLP could be used to construct DNA fingerprints of polar clones at a large-scale level and to determine genetic relationships of poplar varieties.

SummaryUsing silver staining and the whole mount electron microscopy technique of squashed chromosomes, we studied the substructural organization and behaviour of chromatid cores in mitotic chromosomes of spermatogonia of the grasshopper Oedaleus infernalis during mitosis. It was found that the formation of mitotic chromatid cores takes place during the transition from prophase to prometaphase. Each chromosome contains two compact chromatid cores which are surrounded by a halo of dispersed argyrophilic material emanating radially from the cores. In early metaphase the chromatid core usually appears as an extended, slender network running longitudinally through the entire length of the chromatid, while in late metaphase the core frequently has a spiral appearance. In addition, our results revealed the existence of interconnections between sister chromatid cores along their entire length, as a result of which sister chromatid cores appear as a single interconnected core network in mitotic metaphase chromosomes. At this stage the core occupies a lateral position in each chromatid. However, during the transition from metaphase to anaphase, the interconnections are gradually released to allow the individualization of sister chromatid cores and the segregation of chromosomes. The core comes to occupy a central position in each segregated chromatid. These findings demonstrate the presence of an intrinsic interconnected core network within metaphase chromosomes which could be involved in the maintenance and segregation of chromosomes during mitosis.