Biofouling affects the redox kinetics of outer and inner sphere probes on carbon surfaces drastically differently - implications to biosensing.

Biofouling imposes a significant threat for sensing probes used in vivo. Antifouling strategies commonly utilize a protective layer on top of the electrode but this may compromise performance of the electrode. Here, we investigated the effect of surface topography and chemistry on fouling without additional protective layers. We have utilized two different carbon materials; tetrahedral amorphous carbon (ta-C) and SU-8 based pyrolytic carbon (PyC) in their typical smooth thin film structure as well as with a nanopillar topography templated from black silicon. The near edge X-ray absorption fine structure (NEXAFS) spectrum revealed striking differences in chemical functionalities of the surfaces. PyC contained equal amounts of ketone, hydroxyl and ether/epoxide groups, while ta-C contained significant amounts of carbonyl groups. Overall, oxygen functionalities were significantly increased on nanograss surfaces compared to the flat counterparts. Neither bovine serum albumin (BSA) or fetal bovine serum (FBS) fouling caused major effects on electron transfer kinetics of outer sphere redox (OSR) probe Ru(NH3)63+ on any of the materials. In contrast, negatively charged OSR probe IrCl62- kinetics were clearly affected by fouling, possibly due to the electrostatic repulsion between redox species and the anionically-charged proteins adsorbed on the electrode and/or stronger interaction of the proteins and positively charged surface. The OSR probe kinetics were less affected by fouling on PyC, probably due to conformational changes of proteins on the surface. Dopamine (DA) was tested as an inner sphere redox (ISR) probe and as expected, the kinetics were heavily dependent on the material; PyC had very fast electron transfer kinetics, while ta-C had sluggish kinetics. DA electron transfer kinetics were heavily affected on all surfaces by fouling (ΔEp increase 30-451%). The effect was stronger on PyC, possibly due to the more strongly adhered protein layer limiting the access of the probe to the inner sphere.

Doping as a means to probe the potential dependence of dopamine adsorption on carbon-based surfaces: A first-principles study.

In this work, we study the adsorption characteristics of dopamine (DA), ascorbic acid (AA), and dopaminequinone (DAox) on carbonaceous electrodes. Our goal is to obtain a better understanding of the adsorption behavior of these analytes in order to promote the development of new carbon-based electrode materials for sensitive and selective detection of dopamine in vivo. Here we employ density functional theory-based simulations to reach a level of detail that cannot be achieved experimentally. To get a broader understanding of carbonaceous surfaces with different morphological characteristics, we compare three materials: graphene, diamond, and amorphous carbon (a-C). Effects of solvation on adsorption characteristics are taken into account via a continuum solvent model. Potential changes that take place during electrochemical measurements, such as cyclic voltammetry, can also alter the adsorption behavior. In this study, we have utilized doping as an indirect method to simulate these changes by shifting the work function of the electrode material. We demonstrate that sp2- and sp3-rich materials, as well as a-C, respond markedly different to doping. Also the adsorption behavior of the molecules studied here differs depending on the surface material and the change in the surface potential. In all cases, adsorption is spontaneous, but covalent bonding is not detected in vacuum. The aqueous medium has a large effect on the adsorption behavior of DAox, which reaches its highest adsorption energy on diamond when the potential is shifted to more negative values. In all cases, inclusion of the solvent enhances the charge transfer between the slab and DAox. Largest differences in adsorption energy between DA and AA are obtained on graphene. Gaining better understanding of the behavior of the different forms of carbon when used as electrode materials provides a means to rationalize the observed complex phenomena taking place at the electrodes during electrochemical oxidation/reduction of these biomolecules.

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW.

We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which X-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with GW and uses kernel ridge regression for the ML predictions. We apply the new approach to disordered materials and small molecules containing carbon, hydrogen, and oxygen and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server.

Connection between the physicochemical characteristics of amorphous carbon thin films and their electrochemical properties.

Connecting a material's surface chemistry with its electrocatalytic performance is one of the major questions in analytical electrochemistry. This is especially important in many sensor applications where analytes from complex media need to be measured. Unfortunately, today this connection is still largely missing except perhaps for the most simple ideal model systems. Here we present an approach that can be used to obtain insights about this missing connection and apply it to the case of carbon nanomaterials. In this paper we show that by combining advanced computational techniques augmented by machine learning methods with x-ray absorption spectroscopy (XAS) and electrochemical measurements, it is possible to obtain a deeper understanding of the correlation between local surface chemistry and electrochemical performance. As a test case we show how by computationally assessing the growth of amorphous carbon (a-C) thin films at the atomic level, we can create computational structural motifs that may in turn be used to deconvolute the XAS data from the real samples resulting in local chemical information. Then, by carrying out electrochemical measurements on the same samples from which x-ray spectra were measured and that were further characterized computationally, it is possible to gain insight into the interplay between the local surface chemistry and electrochemical performance. To demonstrate this methodology, we proceed as follows: after assessing the basic electrochemical properties of a-C films, we investigate the effect of short HNO3treatment on the sensitivity of these electrodes towards an inner sphere redox probe dopamine to gain knowledge about the influence of altered surface chemistry to observed electrochemical performance. These results pave the way towards a more general assessment of electrocatalysis in different systems and provide the first steps towards data driven tailoring of electrode surfaces to gain optimal performance in a given application.

X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene.

In this work, we demonstrate how to identify and characterize the atomic structure of pristine and functionalized graphene materials from a combination of computational simulation of X-ray spectra, on the one hand, and computer-aided interpretation of experimental spectra, on the other. Despite the enormous scientific and industrial interest, the precise structure of these 2D materials remains under debate. As we show in this study, a wide range of model structures from pristine to heavily oxidized graphene can be studied and understood with the same approach. We move systematically from pristine to highly oxidized and defective computational models, and we compare the simulation results with experimental data. Comparison with experiments is valuable also the other way around; this method allows us to verify that the simulated models are close to the real samples, which in turn makes simulated structures amenable to several computational experiments. Our results provide ab initio semiquantitative information and a new platform for extended insight into the structure and chemical composition of graphene-based materials.

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning.

Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary to draw representative statistics. With the emergence and popularization of machine learning (ML) approaches in materials science, systematic (and accurate) studies of the surface chemistry of disordered materials are now coming within reach. In this paper, we show how the reactivity of amorphous carbon (a-C) surfaces can be systematically quantified and understood by a combination of ML interatomic potentials, ML clustering techniques, and density functional theory calculations. This methodology allows us to process large amounts of atomic data to classify carbon atomic motifs on the basis of their geometry and quantify their reactivity toward hydrogen- and oxygen-containing functionalities. For instance, we identify subdivisions of sp and sp2 motifs with markedly different reactivities. We therefore draw a comprehensive, both qualitative and quantitative, picture of the surface chemistry of a-C and its reactivity toward -H, -O, -OH, and -COOH. While this paper focuses on a-C surfaces, the presented methodology opens up a new systematic and general way to study the surface chemistry of amorphous and disordered materials.

SU-8 based pyrolytic carbon for the electrochemical detection of dopamine.

Here we investigated the electrochemical properties and dopamine (DA) detection capability of SU-8 photoresist based pyrolytic carbon (PyC) as well as its biocompatibility with neural cells. This approach is compatible with microfabrication techniques which is crucial for device development. X-ray photoelectron spectroscopy shows that PyC consists 98.5% of carbon, while oxygen plasma treatment (PyC-O2) increases the amount of oxygen up to 27.1%. PyC showed nearly reversible (ΔEp 63 mV) electron transfer kinetics towards outer sphere redox probe (Ru(NH3)6 2+/3+), while the reaction on PyC-O2 was quasi-reversible (ΔEp > 75 mV). DA showed both diffusion and adsorption-defined reaction kinetics with fast electron transfer with the ΔEp values of 50 mV and 30 mV, for PyC and PyC-O2, respectively. The strong interaction between the hydroxyl groups on the surface and DA, as confirmed by simulations, facilitates the redox reactions of DA. DA showed a linear response in the measured physiologically relevant range (50 nM-1 µM) and sensitivities were 1.2 A M-1 cm-2 for PyC and 2.7 A M-1 cm-2 for PyC-O2. Plasma oxidation (PyC-O2) improved cell adhesion even more than poly-l-lysine (PLL) coating on PyC, but best adhesion was achieved on PLL coated PyC-O2. Glial cells, neuroblastoma cells and neural stem cells all showed similar behavior.