Study on Electrolyte-gated Graphene Nanoelectronic Biosensors for Biomarker Detection

Biosensors are called upon to provide valuable benefits for human society in vital fields such as disease diagnosis, food inspection, environment monitoring, etc. Among the various biosensor architectures, the field effect transistor (FET) biosensors are promising as the next generation nanoelectronic biosensors, particularly attractive for point-of-care biomedical applications. The FET biosensors typically operate by measuring the conductance change of the semiconducting channel induced by the adsorption of the target biomolecules on it. The superior properties of graphene, including the unique electronic characteristics, facile functionalization and good biocompatibility, etc., make it an ideal building block for the FET biosensors. In this dissertation, we present studies on the electrolyte-gated graphene field effect transistor (EGGFET) biosensor and its application for the label-free detection of biomarkers. Poly(methyl methacrylate) (PMMA) residues have long been a critical challenge for the transfer of the chemical vapor deposited (CVD) graphene, which is critical to obtain reliable devices. To address this issue, we first studied the degradation of the PMMA residues upon thermal annealing using Raman spectroscopy. An electrolytic cleaning method is shown to be effective to remove these post-annealing residues, resulting in a clean, residue-free graphene surface. The performance of the EGGFET biosensor is demonstrated by the successful detection of human immunoglobulin G (IgG) using IgG-aptamer as the bioreceptor. The gate voltage with the minimum conductivity (Dirac) in the transfer curve of the EGGFET biosensor is used for the quantitative measurement of IgG concentration. In EGGFET biosensors, the graphene channels are directly exposed to the electrolytes, of which the composition, concentration and pH may vary during the testing. The response of the EGGFET biosensors is found to be susceptible to these variations which might lead to high uncertainty or even false results. We present an EGGFET immunoassay which allows well regulation over the matrix effect. The performance is demonstrated by the detection of human IgG from serum. The detection range of the EGGFET immunoassay for IgG detection is estimated to be around 2-50 nM with a coefficient of variation (CV) of less than 20%. The limit of detection (LOD) is around 0.7 nM. Different from the metal-oxide-semiconductor field effect transistors (MOSFET), the gate voltage is applied on the electrolyte and the electrical double layer (EDL) at the electrolyte-graphene interface serves as the gate dielectric in EGGFET. We studied the capacitance behavior of the electrolyte-graphene interface; the results suggest that the electrolyte-graphene interface exhibits a complex constant phase element (CPE) behavior (1 = 0 () ) with both 0 and varying as functions of the gate voltage. The EDL capacitance and the quantum capacitance are determined which allows us to extract the carrier density and mobility in graphene. This study give insight into the device physics of the EGGFET biosensor and is instructive for the design of the EGGFET biosensors on the device level

Cohomology algebra of orbit spaces of free involutions on the product of projective space and 4-sphere

Let $X$ be a finitistic space with the mod 2 cohomology of the product space of a projective space and a 4-sphere. Assume that $X$ admits a free involution. In this paper we study the mod 2 cohomology algebra of the quotient of $X$ by the action of the free involution and derive some consequences regarding the existence of $\mathbb{Z}_2$-equivariant maps between such $X$ and an $n$-sphere.Comment: Corrected typos, revisions and corrections in some results. To appear in Portugaliae Mathematic

Spoken English and New Teaching and Learning Approaches Used in Spoken English Class in China

With people’s frequent communicative activities occurred among different countries, how some new teaching approaches are employed in Spoken English teaching and learning becomes a heated problem. The traditional methods of teaching Spoken English confines learners to following some patterns to speak based on an oral textbook, which hinders learners’ improving their abilities to speak English. This article aims to solve this problem and also offer some new recognitions of this problem as well as some new approaches of teaching Spoken English in class to make an exploration a new way of improving habits of Spoken English teaching and learning

FoveaBox: Beyond Anchor-based Object Detector

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox .Comment: IEEE Transactions on Image Processing, code at: https://github.com/taokong/FoveaBo