Ultra-sensitive carbon based molecular sensors

This thesis presented the study of carbon-based materials for ultra-sensitive molecular sensing. Reduced Graphene Oxide (rGO), a 2-dimensional one-atomic layer thick carbon material, had the advantage of low-cost, aqueous and industrial-scalable production route. Using rGO as the transducer platform could potentially lower the cost of sensors down to a few dollars per chip. However, there were still limitations in rGO that prevented its widespread usage as a biosensor transducer or in electronics: its low electrical conductivity and large electrical deviations. This thesis was structured to understand and solve these problems for transducer application. The thesis could be broken down into 3 parts: The first part of the thesis presented the critical review of the background and limitations of graphene research, followed by the background and importance of biosensor developments for the detection of sweat sodium ions and circulatory Interleukin-6 proteins. The second part of the thesis tested the hypothesis that the rGO limitations could be eliminated to create a highly sensitive biosensor transducer via (A) improving rGO synthesis (B) pristine Carbon Nanotubes-rGO hybrid film and (C) growth of rGO. The mechanism of ultra-large graphene oxide synthesis and graphene oxide growth was also elucidated in this section. The third part of the thesis then presented the fabrication and test of the practical and homogenous carbon-based biosensor using the transducer synthesized earlier. The thesis showed that through proving the hypothesis correct, it enabled the synthesis of an all organic sodium ion sensor with integrated pump and an ultra-sensitive interleukin-6 bio-sensor. Both of these novel sensors were able to detect the respective molecules in their physiological ranges

Interleukin-18 enhances vascular calcification and osteogenic differentiation of vascular smooth muscle cells through TRPM7 channel activation

Objective—Vascular calcification (VC) is an important predictor of cardiovascular morbidity and mortality. Osteogenic differentiation of vascular smooth muscle cells (VSMCs) is a key mechanism of VC. Recent studies show that IL-18 (interleukin-18) favors VC while TRPM7 (transient receptor potential melastatin 7) channel upregulation inhibits VC. However, the relationship between IL-18 and TRPM7 is unclear. We questioned whether IL-18 enhances VC and osteogenic differentiation of VSMCs through TRPM7 channel activation. Approach and Results—Coronary artery calcification and serum IL-18 were measured in patients by computed tomographic scanning and enzyme-linked immunosorbent assay, respectively. Primary rat VSMCs calcification were induced by high inorganic phosphate and exposed to IL-18. VSMCs were also treated with TRPM7 antagonist 2-aminoethoxy-diphenylborate or TRPM7 small interfering RNA to block TRPM7 channel activity and expression. TRPM7 currents were recorded by patch-clamp. Human studies showed that serum IL-18 levels were positively associated with coronary artery calcium scores (r=0.91; P<0.001). In VSMCs, IL-18 significantly decreased expression of contractile markers α-smooth muscle actin, smooth muscle 22 α, and increased calcium deposition, alkaline phosphatase activity, and expression of osteogenic differentiation markers bone morphogenetic protein-2, Runx2, and osteocalcin (P<0.05). IL-18 increased TRPM7 expression through ERK1/2 signaling activation, and TRPM7 currents were augmented by IL-18 treatment. Inhibition of TRPM7 channel by 2-aminoethoxy-diphenylborate or TRPM7 small interfering RNA prevented IL-18–enhanced osteogenic differentiation and VSMCs calcification. Conclusions—These findings suggest that coronary artery calcification is associated with increased IL-18 levels. IL-18 enhances VSMCs osteogenic differentiation and subsequent VC induced by β-glycerophosphate via TRPM7 channel activation. Accordingly, IL-18 may contribute to VC in proinflammatory conditions

Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery

Time-series Vegetation Indices (VIs) are usually used for estimating grain yield. However, multi-temporal VIs may be affected by different background, illumination, and atmospheric conditions, so the absolute differences among time-series VIs may include the effects induced from external conditions in addition to vegetation changes, which will pose a negative effect on the accuracy of crop yield estimation. Therefore, in this study, the parcel-based relative vegetation index (ΔVI) and the parcel-based relative yield are proposed and further used to estimate rice yield. Hyperspectral images at key growth stages, including tillering stage, jointing stage, booting stage, heading stage, filling stage, and ripening stage, as well as rice yield, were obtained with Rikola hyperspectral imager mounted on Unmanned Aerial Vehicle (UAV) in 2017 growing season. Three types of parcel-level relative vegetation indices, including Relative Normalized Difference Vegetation Index (RNDVI), Relative Ratio Vegetation Index (RRVI), and Relative Difference Vegetation Index (RDVI) are created by using all possible two-band combinations of discrete channels from 500 to 900 nm. The optimal VI type and its band combinations at different growth stages are identified for rice yield estimation. Furthermore, the optimal combinations of different growth stages for yield estimation are determined by F-test and validated using leave-one-out cross validation (LOOCV) method. The comparison results show that, for the single-growth-stage model, RNDVI[880,712] at booting stage has the best correlation with rice yield with a R2-value of 0.75. For the multiple-growth-stage model, RNDVI[808,744] at jointing stage, RNDVI[880,712] at booting stage and RNDVI[808,744] at filling stage gain a higher R2-value of 0.83 with the mean absolute percentage error of estimated rice yield of 3%. The study demonstrates that the proposed method with parcel-level relative vegetation indices and relative yield can achieve higher yield estimation accuracy because it can make full use of the advantage that remote sensing can monitor relative changes accurately. The new method will further enrich the technology system for crop yield estimation based on remotely sensed data

16S Next-generation sequencing and quantitative PCR reveal the distribution of potential pathogens in the Liaohe Estuary

The existence of potentially pathogenic bacteria seriously threatens aquatic animals and human health. Estuaries are closely related to human activities, and the detection of pathogens is important for aquaculture and public health. However, monitoring only indicator microorganisms and pathogens is not enough to accurately and comprehensively estimate water pollution. Here, the diversity of potentially pathogenic bacteria in water samples from the Liaohe estuary was profiled using 16S next-generation sequencing (16S NGS) and quantitative polymerase chain reaction (qPCR) analysis. The results showed that the dominant genera of environmental pathogens were Pseudomonas, Vibrio, Mycobacterium, Acinetobacter, Exiguobacterium, Sphingomonas, and Legionella, and the abundance of enteric pathogens was significantly less than the environmental pathogens, mainly, Citrobacter, Enterococcus, Escherichia-Shigella, Enterobacter, Bacteroides. The qPCR results showed that the 16S rRNA genes of Vibrio were the most abundant, with concentrations between 7.06 and 9.48 lg copies/L, followed by oaa gene, fliC gene, trh gene, and uidA gene, and the temperature and salinity were the main factors affecting its abundance. Variance partitioning analysis (VPA) analysis of spatial factors on the potential pathogen’s distribution (19.6% vs 5.3%) was greater than environmental factors. In addition, the co-occurrence analysis of potential pathogens in the estuary revealed significant co-occurrence among the opportunistic pathogens Testosteronemonas, Brevimonas vesicularis, and Pseudomonas putida. Our findings provide an essential reference for monitoring and occurrence of potentially pathogenic bacteria in estuaries

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Evaluating the performance of PC-ANN for the estimation of rice nitrogen concentration from canopy hyperspectral reflectance

In this study, a wide range of leaf nitrogen concentration levels was established in field-grown rice with the application of three fertilizer levels. Hyperspectral reflectance data of the rice canopy through rice whole growth stages were acquired over the 350 nm to 2500 nm range. Comparisons of prediction power of two statistical methods (linear regression technique (LR) and artificial neural network (ANN)), for rice N estimation (nitrogen concentration, mg nitrogen g(-1) leaf dry weight) were performed using two different input variables (nitrogen sensitive hyperspectral reflectance and principal component scores). The results indicted very good agreement between the observed and the predicted N with all model methods, which was especially true for the PC-ANN model (artificial neural network based on principal component scores), with an RMSE 0.347 and REP 13.14%. Compared to the LR algorithm, the ANN increased accuracy by lowering the RMSE by 17.6% and 25.8% for models based on spectral reflectance and PCs, respectively

A review on electronic bio-sensing approaches based on non-antibody recognition elements

In this review, recent advances in the development of electronic detection methodologies based on non-antibody recognition elements such as functional liposomes, aptamers and synthetic peptides are discussed. Particularly, we highlight the progress of field effect transistor (FET) sensing platforms where possible as the number of publications on FET-based platforms has increased rapidly. Biosensors involving antibody-antigen interactions have been widely applied in diagnostics and healthcare in virtue of their superior selectivity and sensitivity, which can be attributed to their high binding affinity and extraordinary specificity, respectively. However, antibodies typically suffer from fragile and complicated functional structures, large molecular size and sophisticated preparation approaches (resource-intensive and time-consuming), resulting in limitations such as short shelf-life, insufficient stability and poor reproducibility. Recently, bio-sensing approaches based on synthetic elements have been intensively explored. In contrast to existing reports, this review provides a comprehensive overview of recent advances in the development of biosensors utilizing synthetic recognition elements and a detailed comparison of their assay performances. Therefore, this review would serve as a good summary of the efforts for the development of electronic bio-sensing approaches involving synthetic recognition elements

The extended growth of graphene oxide flakes using ethanol CVD

We report the extended growth of Graphene Oxide (GO) flakes using atmospheric pressure ethanol Chemical Vapor Deposition (CVD). GO was used to catalyze the deposition of carbon on substrate in the ethanol CVD with Ar and H2 as carrier gases. Raman, SEM, XPS 10 and AFM characterized the growth to be reduced GO (RGO) of <5 layers. This new grown RGO possesses lower defect density with larger and increased distribution of sp2 domains than chemically-reduced RGO. Furthermore this method without optimization reduces relative standard deviation of electrical conductivity between chips, from 80.5% to 16.5%, enabling RGO to be used in practical electronic devices