An Efficient Synthesis and Photoelectric Properties of Green Carbon Quantum Dots with High Fluorescent Quantum Yield

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)To greatly improve the production quality and efficiency of carbon quantum dots (CQDs), and provide a new approach for the large-scale production of high-quality CQDs, green carbon quantum dots (g-CQDs) with high product yield (PY) and high fluorescent quantum yield (QY) were synthesized by an efficient one-step solvothermal method with 2,7-dihydroxynaphthalene as the carbon source and ethylenediamine as the nitrogen dopant in this study. The PY and QY of g-CQDs were optimised by adjusting reaction parameters such as an amount of added ethylenediamine, reaction temperature, and reaction duration. The results showed that the maximum PY and QY values of g-CQDs were achieved, which were 70.90% and 62.98%, respectively when the amount of added ethylenediamine, reaction temperature, and reaction duration were 4 mL, 180 °C, and 12 h, respectively. With the optimised QY value of g-CQDs, white light emitting diodes (white LEDs) were prepared by combining g-CQDs and blue chip. The colour rendering index of white LEDs reached 87, and the correlated colour temperature was 2520 K, which belongs to the warm white light area and is suitable for indoor lighting. These results indicate that g-CQDs have potential and wide application prospects in the field of white LEDs.Peer reviewedFinal Published versio

One-step hydrothermal synthesis of fluorescence carbon quantum dots with high product yield and quantum yield

A one-step hydrothermal synthesis of nitrogen and silicon co-doped fluorescence carbon quantum dots (N,Si-CQDs), from citric acid monohydrate and silane coupling agent KH-792 with a high product yield (PY) of 52.56% and high quantum yield (QY) of 97.32%, was developed. This greatly improves both the PY and QY of CQDs and provides a new approach for a large-scale production of high-quality CQDs. Furthermore, N,Si-CQDs were employed as phosphors without dispersants to fabricate white light-emitting diodes (WLEDs) with the color coordinates at (0.29, 0.32). It is suggested that N,Si-CQDs have great potential as promising fluorescent materials to be applied in WLEDs.Peer reviewe

TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods.Comment: MICCAI 202

Volatility of mixed atmospheric humic-like substances and ammonium sulfate particles

The volatility of organic aerosols remains poorly understood due to the complexity of speciation and multiphase processes. In this study, we extracted humic-like substances (HULIS) from four atmospheric aerosol samples collected at the SORPES station in Nanjing, eastern China, and investigated the volatility behavior of particles at different sizes using a Volatility Tandem Differential Mobility Analyzer (VTDMA). In spite of the large differences in particle mass concentrations, the extracted HULIS from the four samples all revealed very high-oxidation states (O : C > 0.95), indicating secondary formation as the major source of HULIS in Yangtze River Delta (YRD). An overall low volatility was identified for the extracted HULIS, with the volume fraction remaining (VFR) higher than 55% for all the regenerated HULIS particles at the temperature of 280 degrees C. A kinetic mass transfer model was applied to the thermodenuder (TD) data to interpret the observed evaporation pattern of HULIS, and to derive the mass fractions of semi-volatile (SVOC), low-volatility (LVOC) and extremely low-volatility components (ELVOC). The results showed that LVOC and ELVOC dominated (more than 80 %) the total volume of HULIS. Atomizing processes led to a size-dependent evaporation of regenerated HULIS particles, and resulted in more ELVOC in smaller particles. In order to understand the role of interaction between inorganic salts and atmospheric organic mixtures in the volatility of an organic aerosol, the evaporation of mixed samples of ammonium sulfate (AS) and HULIS was measured. The results showed a significant but nonlinear influence of ammonium sulfate on the volatility of HULIS. The estimated fraction of ELVOC in the organic part of the largest particles (145 nm) increased from 26 %, in pure HULIS samples, to 93% in 1 : 3 (mass ratio of HULIS : AS) mixed samples, to 45% in 2 : 2 mixed samples, and to 70% in 3 : 1 mixed samples, suggesting that the interaction with ammonium sulfate tends to decrease the volatility of atmospheric organic compounds. Our results demonstrate that HULIS are important low-volatility, or even extremely low-volatility, compounds in the organic-aerosol phase. As important formation pathways of atmospheric HULIS, multiphase processes, including oxidation, oligomerization, polymerization and interaction with inorganic salts, are indicated to be important sources of low-volatility and extremely low-volatility species of organic aerosols.Peer reviewe

High-Level PM2.5/PM10 Exposure Is Associated With Alterations in the Human Pharyngeal Microbiota Composition

Previous studies showed that high concentration of particulate matter (PM) 2.5 and PM10 carried a large number of bacterial and archaeal species, including pathogens and opportunistic pathogens. In this study, pharyngeal swabs from 83 subjects working in an open air farmer’s market were sampled before and after exposure to smog with PM2.5 and PM10 levels up to 200 and 300 μg/m3, respectively. Their microbiota were investigated using high-throughput sequencing targeting the V3–V4 regions of the 16S rRNA gene. The genus level phylotypes was increased from 649 to 767 in the post-smog pharyngeal microbiota, of which 142 were new and detected only in the post-smog microbiota. The 142 new genera were traced to sources such as soil, marine, feces, sewage sludge, freshwater, hot springs, and saline lakes. The abundance of the genera Streptococcus, Haemophilus, Moraxella, and Staphylococcus increased in the post-smog pharyngeal microbiota. All six alpha diversity indices and principal component analysis showed that the taxonomic composition of the post-smog pharyngeal microbiota was significantly different to that of the pre-smog pharyngeal microbiota. Redundancy analysis showed that the influences of PM2.5/PM10 exposure and smoking on the taxonomic composition of the pharyngeal microbiota were statistically significant (p < 0.001). Two days of exposure to high concentrations of PM2.5/PM10 changed the pharyngeal microbiota profiles, which may lead to an increase in respiratory diseases. Wearing masks could reduce the effect of high-level PM2.5/PM10 exposure on the pharyngeal microbiota

Experimental and Numerical Investigation of One-Dimensional Electroosmotic Consolidation Based on Segment Inheritance Strategy

The attenuation of effective potential is a significant factor leading to the low postdrainage efficiency in the electroosmotic consolidation and drainage methods. In this study, the influence of the attenuation of effective potential on the electroosmotic consolidation process and the problem of solving the governing equations were investigated. A 102.5-hr one-dimensional electroosmotic consolidation test was performed to monitor the changes in the properties of the soil interior and the soil–electrode interface, and the variation curve of the effective potential in the test model was measured. The time-dependent nature of the potential distribution due to the attenuation of effective potential contradicts the assumption of introducing intermediate variables. To address this key issue, the variation curve of the effective potential was linearly segmented, ensuring the validity of introducing intermediate variables within each local state segment. Based on the continuity between state segments, the initial conditions of the governing equations in different state segments were updated, thereby extending the differential iteration within local state segments to the entire electroosmotic time domain. A finite-difference program for this method was developed using the Python language. Calculations and analyses based on the measured potential data were performed, revealing that a decrease in potential leads to a reduction in the effectiveness of electroosmotic drainage and consolidation in terms of the instantaneous distribution of pore pressure and the overall average degree of consolidation. This method can reflect the influence of the attenuation of effective potential on the pore water pressure during the electroosmotic consolidation process. The research findings of this paper can provide theoretical and numerical support for the improvement and engineering application of the electroosmotic consolidation and drainage method

Comparison of multiple models for remote sensing of carbon exchange using MODIS data in conifer-dominated forests

Boreal forests in the northern hemisphere provide important sinks for storing carbon dioxide (CO₂). However, the size and distribution of these sinks remain uncertain. In particular, many remote-sensing models show a strong bias in the simulation of carbon fluxes for evergreen needleleaf forest. The objective of this study is to improve these predictive models for accurately quantifying temporal changes in the net ecosystem exchange (NEE) of conifer-dominated forest solely based on satellite remote sensing, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime land-surface temperature (LST), night-time LST′, enhanced vegetation index (EVI), land–surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). Considering that the component fluxes, gross primary production (GPP), and ecosystem respiration (Re), are strongly influenced by vegetation phenology, seasonality information was extracted from timeseries MODIS EVI data based on non-linear least-squares fits of asymmetric Gaussian model functions with a software package for analysing the time-series of satellite sensor data (TIMESAT). The results indicated that models directly incorporating phenological information failed to improve their performance for temperate deciduous forest. Instead, three methods to retrieve the component fluxes – GPP and Re – including direct estimates, models incorporating the phenological information, and models developed based on the threshold value (LST 273 K), were explored respectively. All methods improved NEE estimates markedly and models developed based on the threshold value performed best, and provided a future framework for accurate remote sensing of NEE in evergreen forest

RESEARCH ON BOLT CONNECTION LOOSE MECHANISM UNDER DYNAMIC TENSION COMPRESSION AND SHEAR LOAD

Aiming at the loosening of bolt connection in the rotating mechanism under dynamic tension compression and shear load,the failure mechanism and its influencing factors are studied by using virtual prototyping technology and finite element analysis method. First,based on the virtual prototype technology,the load of failure bolt connection structure is extracted;Secondly,the finite element method is used to analyze the influence factors of bolt number,preload,thickness of the connecting piece and spring gasket. The evaluation index system of digital analysis is set up to guide the design of the measures for improving the anti loosening. In this paper,the design and research on the bolt of a servo device rotating mechanism is taken as an example. The method of digital design and analysis is carried out,which solves the problem of bolt connection loose. The conclusion shows that the improved design is consistent with the practical engineering,and bolt connection structure is free from loosening and failure