Coal based carbon dots: recent advances in synthesis, properties, and applications

Carbon dots are zero-dimensional carbon nanomaterials with quantum confinement effects and edge effects, which have aroused great interests in many disciplines such as energy, chemistry, materials, and environmental applications. They can be prepared by chemical oxidation, electrochemical synthesis, hydrothermal preparation, arc discharge, microwave synthesis, template method, and many other methods. However, the raw materials' high cost, the complexity and environmental-unfriendly fabrication process limit their large-scale production and commercialization. Herein, we review the latest developments of coal-based carbon dots about selecting coal-derived energy resources (bituminous coal, anthracite, lignite, coal tar, coke, etc.) the developments of synthesis processes, surface modification, and doping of carbon dots. The coal-based carbon dots exhibit the advantages of unique fluorescence, efficient catalysis, excellent water solubility, low toxicity, inexpensive, good biocompatibility, and other advantages, which hold the potentiality for a wide range of applications such as environmental pollutants sensing, catalyst preparation, chemical analysis, energy storage, and medical imaging technology. This review aims to provide a guidance of finding abundant and cost-effective precursors, green, simple and sustainable production processes to prepare coal-based carbon dots, and make further efforts to exploit the application of carbon dots in broader fields

Grassland health assessment based on indicators monitored by UAVs: a case study at a household scale

Grassland health assessment (GHA) is a bridge of study and management of grassland ecosystem. However, there is no standardized quantitative indicators and long-term monitor methods for GHA at a large scale, which may hinder theoretical study and practical application of GHA. In this study, along with previous concept and practices (i.e., CVOR, the integrated indexes of condition, vigor, organization and resilience), we proposed an assessment system based on the indicators monitored by unmanned aerial vehicles (UAVs)-UAVCVOR, and tested the feasibility of UAVCVOR at typical household pastures on the Qinghai-Tibetan Plateau, China. Our findings show that: (1) the key indicators of GHA could be measured directly or represented by the relative counterpart indicators that monitored by UAVs, (2) there was a significantly linear relationship between CVOR estimated by field- and UAV-based data, and (3) the CVOR decreased along with the increasing grazing intensity nonlinearly, and there are similar tendencies of CVOR that estimated by the two methods. These findings suggest that UAVs is suitable for GHA efficiently and correctly, which will be useful for the protection and sustainable management of grasslands

Combining Online News Articles and Web Search to Predict the Fluctuation of Real Estate Market in Big Data Context

The real estate price is of paramount importance in both economic and social fields. It is a key indicator of the operation of real estate market and its prediction is essential in the decision-making process of both average people and official governments. Past researchers on this topic have already proposed several prediction methods including linear regression models, nonlinear regression models and machine learning models. Nevertheless, those models have generally neglected the impact of human behavior, which we believe is a significant factor of the real estate price prediction. What’s more, past studies have shown that news sentiments could improve the prediction performance of real estate price. Search engine query data were studied to reflect web users’ behavior by analyzing the frequency of words searched by online users. Researchers have already used the news sentiments and query data for prediction, respectively. But none have combined them together as an integrated model. In this paper, we propose an integrated method that throws new light on the prediction of real estate price in China by integrating these two factors into the forecasting model. In our method, we extract sentiment series from both news data and search engine query data by adding weights to original sentiment series that are produced by news data alone. Then both the weighted series and original ones are used as inputs of several well-acknowledged data mining models, including SVR, RBFNN and BPNN, to produce prediction results. To validate the integrated model, we apply it to four representative cities in China respectively, and compare the results produced by the integrated model using weighted inputs with non-integrated ones using original inputs. The results show that for every one of the four cities, the integrated model generally leads to lower prediction errors than the non-integrated ones. This not only validates the model’s accuracy and universality, but also proves the hypothesis that human searching behavior as a strong impact in typical Chinese cities’ real estate market and can enhance the prediction accuracy of real estate prices. Available at: https://aisel.aisnet.org/pajais/vol6/iss4/2

DOES WEB NEWS MEDIA HAVE OPINIONS? EVIDENCE FROM REAL ESTATE MARKET PREDICTION

In this paper, we propose a novel method for real estate price prediction using web new media sentiments by incorporating human searching behaivor on the web. By combining online daily news’ sentiments and Google search engine query data, we construct a web news content and online search behavior-based integrated model for real estate prediction. Besides these factors, real estate price time series data are also considered into the model in order to improve the forecasting performance. Furthermore, we make a comparison between the integrated model and the baseline model without search engine query data. Experimental results indicate that the integrated model outperforms the non-integrated model, which suggests that online user searching behavior is of great value in enhancing the prediction performance. These findings imply that the proposed integrated model is effective and feasible for real estate market prediction

Three-Dimensional Surface-Enhanced Raman Scattering Platform with Hotspots Built by a Nano-mower for Rapid Detection of MRSA

Methicillin-resistant Staphylococcus aureus (MRSA) has become one of the greatest threats to human health due to its strong drug resistance, wide distribution range, and high infection rate. Rapid identification of MRSA strains is very important for accurate diagnosis and early treatment of MRSA infections. Here, we introduced an Exo III-assisted nanomotor mower to build 3D hotspots for rapid detection of MRSA by surface-enhanced Raman scattering (SERS). As the bacteria bound to the aptamer, two trigger chains were released from the double-stranded structure, and the nano-mowers were activated by opening a hairpin probe on gold nanoparticles (AuNPs). With the continued cleavage of Exo III and cyclic release of the trigger chain, multiple hairpin DNAs on the AuNPs were cleaved to increase the motor power. The resulting nano-mower continued slicing protective DNA from larger AuNPs, exposing the AuNPs. Without the protection of DNA, Mg2+ in the buffer induced spontaneous aggregation of the AuNPs, and a large number of hotspots were formed for SERS measurements. Under optimal conditions, MRSA can be detected within 40 min, and the concentration of MRSA showed a good linear relationship with the SERS intensity at 1342 cm–1, with a limit of detection as low as 1 CFU/mL and a wide linear range (100 to 107 CFU/mL). This strategy creates a rapid bacterial detection method that performs well on actual samples utilizing portable Raman spectroscopy instruments, with potential applications in food detection, water detection, clinical treatment, and diagnosis

Ultrasensitive Simultaneous Detection of Multiplex Disease-Related Nucleic Acids Using Double-Enhanced Surface-Enhanced Raman Scattering Nanosensors

Developing ultrasensitive probes holds great significance for simultaneous detection of multiplexed cancer-associated nucleic acids. Bimetallic nanoparticles containing silver may be exploited as nanoprobes for disease detection, which can produce stable and strong surface-enhanced Raman scattering (SERS) signals. However, it remains extremely challenging that such SERS nanoprobes are directly synthesized. Herein gold–silver nanosnowmen, grown via a DNA-mediated approach and attached to thiol-containing Raman dyes, are successfully synthesized. Stable SERS-enhanced gold substrates are also prepared and used as the enriching containers, where the capture DNAs are tethered to sense the target genes jointly enhanced by the SERS nanoprobes in a sandwich hybridization assay. This means detection of the target gene can obtain a limit of detection close to 0.839 fM. Such double-enhanced SERS nanosensors are further employed to simultaneously detect the three types of prostate carcinoma-related genes with high sensitivity and specificity, which meanwhile exhibit robust capacity of resisting disturbance in practical samples. Simultaneous and multiplexed detection of cancer-related genes may provide further biomedical applications with new opportunity

Three-Dimensional and Time-Ordered Surface-Enhanced Raman Scattering Hotspot Matrix

The “fixed” or “flexible” design of plasmonic hotspots is a frontier area of research in the field of surface-enhanced Raman scattering (SERS). Most reported SERS hotspots have been shown to exist in zero-dimensional point-like, one-dimensional linear, or two-dimensional planar geometries. Here, we demonstrate a novel three-dimensional (3D) hotspot matrix that can hold hotspots between every two adjacent particles in 3D space, simply achieved by evaporating a droplet of citrate-Ag sols on a fluorosilylated silicon wafer. In situ synchrotron-radiation small-angle X-ray scattering (SR-SAXS), combined with dark-field microscopy and in situ micro-UV, was employed to explore the evolution of the 3D geometry and plasmonic properties of Ag nanoparticles in a single droplet. In such a droplet, there is a distinct 3D geometry with minimal polydispersity of particle size and maximal uniformity of interparticle distance, significantly different from the dry state. According to theoretical simulations, the liquid adhesive force promotes a closely packed assembly of particles, and the interparticle distance is not fixed but can be balanced in a small range by the interplay of the van der Waals attraction and electrostatic repulsion experienced by a particle. The “trapping well” for immobilizing particles in 3D space can result in a large number of hotspots in a 3D geometry. Both theoretical and experimental results demonstrate that the 3D hotspots are predictable and time-ordered in the absence of any sample manipulation. Use of the matrix not only produces giant Raman enhancement at least 2 orders of magnitude larger than that of dried substrates, but also provides the structural basis for trapping molecules. Even a single molecule of resonant dye can generate a large SERS signal. With a portable Raman spectrometer, the detection capability is also greatly improved for various analytes with different natures, including pesticides and drugs. This 3D hotspot matrix overcomes the long-standing limitations of SERS for the ultrasensitive characterization of various substrates and analytes and promises to transform SERS into a practical analytical technique

Three-Dimensional and Time-Ordered Surface-Enhanced Raman Scattering Hotspot Matrix

The “fixed” or “flexible” design of plasmonic hotspots is a frontier area of research in the field of surface-enhanced Raman scattering (SERS). Most reported SERS hotspots have been shown to exist in zero-dimensional point-like, one-dimensional linear, or two-dimensional planar geometries. Here, we demonstrate a novel three-dimensional (3D) hotspot matrix that can hold hotspots between every two adjacent particles in 3D space, simply achieved by evaporating a droplet of citrate-Ag sols on a fluorosilylated silicon wafer. In situ synchrotron-radiation small-angle X-ray scattering (SR-SAXS), combined with dark-field microscopy and in situ micro-UV, was employed to explore the evolution of the 3D geometry and plasmonic properties of Ag nanoparticles in a single droplet. In such a droplet, there is a distinct 3D geometry with minimal polydispersity of particle size and maximal uniformity of interparticle distance, significantly different from the dry state. According to theoretical simulations, the liquid adhesive force promotes a closely packed assembly of particles, and the interparticle distance is not fixed but can be balanced in a small range by the interplay of the van der Waals attraction and electrostatic repulsion experienced by a particle. The “trapping well” for immobilizing particles in 3D space can result in a large number of hotspots in a 3D geometry. Both theoretical and experimental results demonstrate that the 3D hotspots are predictable and time-ordered in the absence of any sample manipulation. Use of the matrix not only produces giant Raman enhancement at least 2 orders of magnitude larger than that of dried substrates, but also provides the structural basis for trapping molecules. Even a single molecule of resonant dye can generate a large SERS signal. With a portable Raman spectrometer, the detection capability is also greatly improved for various analytes with different natures, including pesticides and drugs. This 3D hotspot matrix overcomes the long-standing limitations of SERS for the ultrasensitive characterization of various substrates and analytes and promises to transform SERS into a practical analytical technique

Three-dimensional and time-ordered surface-enhanced raman scattering hotspot matrix

The "fixed" or "flexible" design of plasmonic hotspots is a frontier area of research in the field of surface-enhanced Raman scattering (SERS). Most reported SERS hotspots have been shown to exist in zero-dimensional point-like, one-dimensional linear, or two-dimensional planar geometries. Here, we demonstrate a novel three-dimensional (3D) hotspot matrix that can hold hotspots between every two adjacent particles in 3D space, simply achieved by evaporating a droplet of citrate-Ag sols on a fluorosilylated silicon wafer. In situ synchrotron-radiation small-angle X-ray scattering (SR-SAXS), combined with dark-field microscopy and in situ micro-UV, was employed to explore the evolution of the 3D geometry and plasmonic properties of Ag nanoparticles in a single droplet. In such a droplet, there is a distinct 3D geometry with minimal polydispersity of particle size and maximal uniformity of interparticle distance, significantly different from the dry state. According to theoretical simulations, the liquid adhesive force promotes a closely packed assembly of particles, and the interparticle distance is not fixed but can be balanced in a small range by the interplay of the van der Waals attraction and electrostatic repulsion experienced by a particle. The "trapping well" for immobilizing particles in 3D space can result in a large number of hotspots in a 3D geometry. Both theoretical and experimental results demonstrate that the 3D hotspots are predictable and time-ordered in the absence of any sample manipulation. Use of the matrix not only produces giant Raman enhancement at least 2 orders of magnitude larger than that of dried substrates, but also provides the structural basis for trapping molecules. Even a single molecule of resonant dye can generate a large SERS signal. With a portable Raman spectrometer, the detection capability is also greatly improved for various analytes with different natures, including pesticides and drugs. This 3D hotspot matrix overcomes the long-standing limitations of SERS for the ultrasensitive characterization of various substrates and analytes and promises to transform SERS into a practical analytical technique. ? 2014 American Chemical Society

Three-Dimensional and Time-Ordered Surface-Enhanced Raman Scattering Hotspot Matrix

The “fixed” or “flexible” design of plasmonic hotspots is a frontier area of research in the field of surface-enhanced Raman scattering (SERS). Most reported SERS hotspots have been shown to exist in zero-dimensional point-like, one-dimensional linear, or two-dimensional planar geometries. Here, we demonstrate a novel three-dimensional (3D) hotspot matrix that can hold hotspots between every two adjacent particles in 3D space, simply achieved by evaporating a droplet of citrate-Ag sols on a fluorosilylated silicon wafer. In situ synchrotron-radiation small-angle X-ray scattering (SR-SAXS), combined with dark-field microscopy and in situ micro-UV, was employed to explore the evolution of the 3D geometry and plasmonic properties of Ag nanoparticles in a single droplet. In such a droplet, there is a distinct 3D geometry with minimal polydispersity of particle size and maximal uniformity of interparticle distance, significantly different from the dry state. According to theoretical simulations, the liquid adhesive force promotes a closely packed assembly of particles, and the interparticle distance is not fixed but can be balanced in a small range by the interplay of the van der Waals attraction and electrostatic repulsion experienced by a particle. The “trapping well” for immobilizing particles in 3D space can result in a large number of hotspots in a 3D geometry. Both theoretical and experimental results demonstrate that the 3D hotspots are predictable and time-ordered in the absence of any sample manipulation. Use of the matrix not only produces giant Raman enhancement at least 2 orders of magnitude larger than that of dried substrates, but also provides the structural basis for trapping molecules. Even a single molecule of resonant dye can generate a large SERS signal. With a portable Raman spectrometer, the detection capability is also greatly improved for various analytes with different natures, including pesticides and drugs. This 3D hotspot matrix overcomes the long-standing limitations of SERS for the ultrasensitive characterization of various substrates and analytes and promises to transform SERS into a practical analytical technique