Hybrid carbon based nanomaterials for electrochemical detection of biomolecules

Laurila, Tomi; Sainio, Sami; Miguel, A.

doi:10.1016/j.pmatsci.2017.04.012

Cited by 157 publications

(137 citation statements)

References 326 publications

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“…One such relatively inert carbon framework is tetrahedral amorphous carbon thin lms, or ta-C. Due to the high fraction of >60% sp 3 -bonded carbon, 11 pure ta-C is a very hard, ultrasmooth, 12 protective diamond-like carbon (DLC) coating, which is chemically reasonably inert, has a wide water window, is biocompatible, and is resistant against biofouling. [13][14][15] Furthermore, the physicochemical properties of ta-C thin lms can be tuned by doping it with metals or gases, 16,17 as well as adjusting the sp 3 /sp 2 ratio. 18,19 With pure ta-C, the overall kinetics of outer sphere redox probe electron transfer can be tuned by adjusting the thickness of the lm i.e.…”

Section: 5mentioning

confidence: 99%

Characterization and electrochemical properties of iron-doped tetrahedral amorphous carbon (ta-C) thin films

2018

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a Iron-doped tetrahedral amorphous carbon thin films (Fe/ta-C) were deposited with varying iron content using a pulsed filtered cathodic vacuum arc system (p-FCVA). The aim of this study was to understand effects of iron on both the physical and electrochemical properties of the otherwise inert sp 3 -rich ta-C matrix. As indicated by X-ray photoelectron spectroscopy (XPS), even $0.4 at% surface iron had a profound electrochemical impact on both the potential window of ta-C in H 2 SO 4 and KOH, as well as pseudocapacitance. It also substantially enhanced the electron transport and re-enabled facile outer sphere redox reaction kinetics in comparison to un-doped ta-C, as measured with electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) using outer-sphere probes Ru(NH 3 ) 6 , IrCl 6 , and FcMeOH. These increases in surface iron loading were linked to increased surface oxygen content and iron oxides. Unlike few other metals, an iron content even up to 10 at% was not found to result in the formation of sp 2 -rich amorphous carbon films as investigated by Raman spectroscopy. Atomic force microscopy (AFM) and transmission electron microscopy (TEM) investigations found all films to be amorphous and ultrasmooth with R q values always in the range of 0.1-0.2 nm. As even very small amounts of Fe were shown to dominate the electrochemistry of ta-C, implications of this study are very useful e.g. in carbon nanostructure synthesis, where irregular traces of iron can be readily incorporated into the final structures.

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Section: 5mentioning

confidence: 99%

Characterization and electrochemical properties of iron-doped tetrahedral amorphous carbon (ta-C) thin films

2018

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“…[146] The surface chemistry of a-C is complex, due in part to the disordered nature of the material but also due to the many different types of O-, H-, and N-containing functional groups that can become attached to the surface, either during the manufacturing process or when the materials are put in contact with an electrolyte. [146] The surface chemistry of a-C is complex, due in part to the disordered nature of the material but also due to the many different types of O-, H-, and N-containing functional groups that can become attached to the surface, either during the manufacturing process or when the materials are put in contact with an electrolyte.…”

Section: Carbon Nanomaterialsmentioning

confidence: 99%

“…This structural diversity is reflected in many known and hypothetical crystalline structures, [34,143] metastable over an unusually wide stability window, [144] and in a plethora of disordered phases with very diverse properties. [145,146] Between the ideally ordered structures of diamond and graphite on the one hand, and fully disordered amorphous structures on the other hand (Figure 5a), there are partly ordered, "porous" and "hard" carbon materials, [147][148][149][150] a few examples of which are shown in Figure 5b. These materials are of utmost importance for practical applications, for example, for energy storage and conversion.…”

Section: Carbon Nanomaterialsmentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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“…Carbon materials, particularly those based on nanocarbon, are the focus of researchers in recent years. Their unique properties such as the ability to tolerate harsh chemical conditions, high strength, excellent resistance, good electrical conductivity and thermal conduction make them suitable for various chemical and biological applications.…”

Section: Introductionmentioning

confidence: 99%

“…The progress of electrochemical sensors for neurotransmitter (NT) sensing is vital target in electrochemistry. Carbon materials have played a significant role in this regard . Besides carbon, other electrochemical mediators, self‐assembled monolayer, conducting polymers, layered double hydroxides, are also used in the development of probes for neurotransmitter detection, but carbon materials have been proven very effective for this task.…”

Section: Introductionmentioning

confidence: 99%

Carbon‐Nanotube‐Based Materials for Electrochemical Sensing of the Neurotransmitter Dopamine

2018

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The development of new tools with outstanding performance for monitoring neurotransmitters is a challenging task in neuroscience. Electrochemical probes have attracted tremendous attention for the identification and quantification of neurotransmitters, owing to their demonstrated potential for quick response, real‐time measurements, elevated selectivity and sensitivity. In this Review, we have focused on the recent developments of carbon nanotube (CNT)‐based electrochemical sensors for dopamine detection. The use of CNTs is beneficial for this task, because of their high tensile strength, flexibility, good electrical conductivity, and low thermal expansion coefficient. Therefore, electrochemical sensing approaches with CNTs have opened the door for researchers working towards neurobiomedical applications.

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Hybrid carbon based nanomaterials for electrochemical detection of biomolecules

Cited by 157 publications

References 326 publications

Characterization and electrochemical properties of iron-doped tetrahedral amorphous carbon (ta-C) thin films

Characterization and electrochemical properties of iron-doped tetrahedral amorphous carbon (ta-C) thin films

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Carbon‐Nanotube‐Based Materials for Electrochemical Sensing of the Neurotransmitter Dopamine

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