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Journal ArticleDOI

Ultrasensitive Detection of Dopamine, IL-6 and SARS-CoV-2 Proteins on Crumpled Graphene FET Biosensor

TL;DR: In this article, the effect of the crumpling ratio on electrical double layer (EDL) formation and bandgap opening on the graphene was studied and a small and electroneutral molecule dopamine was captured by an aptamer and its conformation change induced electrical signal changes.
Abstract: Universal platforms for biomolecular analysis using label-free sensing modalities can address important diagnostic challenges. Electrical field effect-sensors are an important class of devices that can enable point-of-care sensing by probing the charge in the biological entities. Use of crumpled graphene for this application is especially promising. It is previously reported that the limit of detection (LoD) on electrical field effect-based sensors using DNA molecules on the crumpled graphene FET (field-effect transistor) platform. Here, the crumpled graphene FET-based biosensing of important biomarkers including small molecules and proteins is reported. The performance of devices is systematically evaluated and optimized by studying the effect of the crumpling ratio on electrical double layer (EDL) formation and bandgap opening on the graphene. It is also shown that a small and electroneutral molecule dopamine can be captured by an aptamer and its conformation change induced electrical signal changes. Three kinds of proteins were captured with specific antibodies including interleukin-6 (IL-6) and two viral proteins. All tested biomarkers are detectable with the highest sensitivity reported on the electrical platform. Significantly, two COVID-19 related proteins, nucleocapsid (N-) and spike (S-) proteins antigens are successfully detected with extremely low LoDs. This electrical antigen tests can contribute to the challenge of rapid, point-of-care diagnostics.
Citations
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
TL;DR: A comprehensive review of 2D-materials-based field effect transistors (2D FET) sensors can be found in this paper , where the authors highlight the challenges of their commercialization and discuss corresponding solution techniques.
Abstract: The evolutionary success in information technology has been sustained by the rapid growth of sensor technology. Recently, advances in sensor technology have promoted the ambitious requirement to build intelligent systems that can be controlled by external stimuli along with independent operation, adaptivity, and low energy expenditure. Among various sensing techniques, field-effect transistors (FETs) with channels made of two-dimensional (2D) materials attract increasing attention for advantages such as label-free detection, fast response, easy operation, and capability of integration. With atomic thickness, 2D materials restrict the carrier flow within the material surface and expose it directly to the external environment, leading to efficient signal acquisition and conversion. This review summarizes the latest advances of 2D-materials-based FET (2D FET) sensors in a comprehensive manner that contains the material, operating principles, fabrication technologies, proof-of-concept applications, and prototypes. First, a brief description of the background and fundamentals is provided. The subsequent contents summarize physical, chemical, and biological 2D FET sensors and their applications. Then, we highlight the challenges of their commercialization and discuss corresponding solution techniques. The following section presents a systematic survey of recent progress in developing commercial prototypes. Lastly, we summarize the long-standing efforts and prospective future development of 2D FET-based sensing systems toward commercialization.

46 citations

Journal ArticleDOI
TL;DR: A soft implantable aptamer-graphene microtransistor probe for real-time monitoring of neurochemical release with the capability of capturing the dopamine release dynamics evoked by pharmacological stimulation is reported, suggesting the potential applications in basic neuroscience studies and studying neurological disease-related processes.
Abstract: The real-time monitoring of neurochemical release in vivo plays a critical role in understanding the biochemical process of the complex nervous system. Current technologies for such applications, including microdialysis and fast-scan cyclic voltammetry, suffer from limited spatiotemporal resolution or poor selectivity. Here, we report a soft implantable aptamer-graphene microtransistor probe for real-time monitoring of neurochemical release. As a demonstration, we show the monitoring of dopamine with nearly cellular-scale spatial resolution, high selectivity (dopamine sensor >19-fold over norepinephrine), and picomolar sensitivity, simultaneously. Systematic benchtop evaluations, ex vivo experiments, and in vivo studies in mice models highlight the key features and demonstrate the capability of capturing the dopamine release dynamics evoked by pharmacological stimulation, suggesting the potential applications in basic neuroscience studies and studying neurological disease-related processes. The developed system can be easily adapted for monitoring other neurochemicals and drugs by simply replacing the aptamers functionalized on the graphene microtransistors.

13 citations

Journal ArticleDOI
TL;DR: In this paper , a review of the application of carbon nanomaterials in the diagnosis of infectious diseases and tumors is presented, emphasizing the importance of their unique properties in infectious disease and tumor diagnosis and the challenges and opportunities that exist for future clinical applications.
Abstract: Infectious diseases (such as Corona Virus Disease 2019) and tumors pose a tremendous challenge to global public health. Early diagnosis of infectious diseases and tumors can lead to effective control and early intervention of the patient's condition. Over the past few decades, carbon nanomaterials (CNs) have attracted widespread attention in different scientific disciplines. In the field of biomedicine, carbon nanotubes, graphene, carbon quantum dots and fullerenes have the ability of improving the accuracy of the diagnosis by the improvement of the diagnostic approaches. Therefore, this review highlights their applications in the diagnosis of infectious diseases and tumors over the past five years. Recent advances in the field of biosensing, bioimaging, and nucleic acid amplification by such CNs are introduced and discussed, emphasizing the importance of their unique properties in infectious disease and tumor diagnosis and the challenges and opportunities that exist for future clinical applications. Although the application of CNs in the diagnosis of several diseases is still at a beginning stage, biosensors, bioimaging technologies and nucleic acid amplification technologies built on CNs represent a new generation of promising diagnostic tools that further support their potential application in infectious disease and tumor diagnosis.

10 citations

Journal ArticleDOI
TL;DR: A viable electrochemical approach for the detection of dopamine (DA) in uric acid (UA) utilizing a silver nanoparticle-doped 2-aminodiphenylamine (AgNPs-2ADPA) electrode was invented as mentioned in this paper .
Abstract: A viable electrochemical approach for the detection of dopamine (DA) in uric acid (UA) utilizing a silver nanoparticle-doped 2-aminodiphenylamine (AgNPs-2ADPA) electrode was invented. The electrochemical performance of DA showed that the incorporated electrode displayed outstanding electrocatalytic performance to the electrochemical oxidation of DA. In our study, the AgNPs-2ADPA exhibits remarkable catalytic activity, retaining high current value and resilience when employed as a working electrode component for electrocatalytic detection of DA. We have also utilized the bare and polymeric-2ADPA in DA detection for a comparison study. This method offers a facile route with extraordinary sensitivity, selectivity, and strength for the voltammetric detection of DA, even in the presence of UA and ascorbic acid (AA) as interferents, that can be employed for pharmaceutical and biological specimens.

9 citations

References
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Journal ArticleDOI
TL;DR: A simple derivation of a simple GGA is presented, in which all parameters (other than those in LSD) are fundamental constants, and only general features of the detailed construction underlying the Perdew-Wang 1991 (PW91) GGA are invoked.
Abstract: Generalized gradient approximations (GGA’s) for the exchange-correlation energy improve upon the local spin density (LSD) description of atoms, molecules, and solids. We present a simple derivation of a simple GGA, in which all parameters (other than those in LSD) are fundamental constants. Only general features of the detailed construction underlying the Perdew-Wang 1991 (PW91) GGA are invoked. Improvements over PW91 include an accurate description of the linear response of the uniform electron gas, correct behavior under uniform scaling, and a smoother potential. [S0031-9007(96)01479-2] PACS numbers: 71.15.Mb, 71.45.Gm Kohn-Sham density functional theory [1,2] is widely used for self-consistent-field electronic structure calculations of the ground-state properties of atoms, molecules, and solids. In this theory, only the exchange-correlation energy EXC › EX 1 EC as a functional of the electron spin densities n"srd and n#srd must be approximated. The most popular functionals have a form appropriate for slowly varying densities: the local spin density (LSD) approximation Z d 3 rn e unif

146,533 citations

Journal ArticleDOI
TL;DR: An efficient scheme for calculating the Kohn-Sham ground state of metallic systems using pseudopotentials and a plane-wave basis set is presented and the application of Pulay's DIIS method to the iterative diagonalization of large matrices will be discussed.
Abstract: We present an efficient scheme for calculating the Kohn-Sham ground state of metallic systems using pseudopotentials and a plane-wave basis set. In the first part the application of Pulay's DIIS method (direct inversion in the iterative subspace) to the iterative diagonalization of large matrices will be discussed. Our approach is stable, reliable, and minimizes the number of order ${\mathit{N}}_{\mathrm{atoms}}^{3}$ operations. In the second part, we will discuss an efficient mixing scheme also based on Pulay's scheme. A special ``metric'' and a special ``preconditioning'' optimized for a plane-wave basis set will be introduced. Scaling of the method will be discussed in detail for non-self-consistent and self-consistent calculations. It will be shown that the number of iterations required to obtain a specific precision is almost independent of the system size. Altogether an order ${\mathit{N}}_{\mathrm{atoms}}^{2}$ scaling is found for systems containing up to 1000 electrons. If we take into account that the number of k points can be decreased linearly with the system size, the overall scaling can approach ${\mathit{N}}_{\mathrm{atoms}}$. We have implemented these algorithms within a powerful package called VASP (Vienna ab initio simulation package). The program and the techniques have been used successfully for a large number of different systems (liquid and amorphous semiconductors, liquid simple and transition metals, metallic and semiconducting surfaces, phonons in simple metals, transition metals, and semiconductors) and turned out to be very reliable. \textcopyright{} 1996 The American Physical Society.

81,985 citations

Journal ArticleDOI
TL;DR: VMD is a molecular graphics program designed for the display and analysis of molecular assemblies, in particular biopolymers such as proteins and nucleic acids, which can simultaneously display any number of structures using a wide variety of rendering styles and coloring methods.

46,130 citations

Journal ArticleDOI
TL;DR: In this article, three parallel algorithms for classical molecular dynamics are presented, which can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors.

32,670 citations

01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations