Electrochemical studies on small electron transfer proteins using membrane electrodes

Journal of Electroanalytical Chemistry 541 (2003) 153-162

Benefits of membrane electrodes in the electrochemistry of metalloproteins: mediated catalysis of Paracoccus pantotrophus cytochrome c peroxidase by horse cytochrome c: a case study

J Biol Inorg Chem. 2008 Jun;13(5):779-87. doi: 10.1007/s00775-008-0365-8

Direct electrochemistry of the Desulfovibrio gigas aldehyde oxidoreductase

Eur. J. Biochem. 271, 1329–1338 (2004)

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Write-erase and read paper memory transistor

Applied Physics Letters, Vol.93, issue 20

Electrochemical characterization of Dps, a DNA-protecting protein

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Artefacts induced on c-type haem proteins by electrode surfaces

J Biol Inorg Chem (2011) 16:209–215 DOI 10.1007/s00775-010-0717-z

Towards printed carbon nanotube transistors on paper substrates

This thesis reports the work performed in the optimization of deposition parameters of Multi – Walled Carbon Nanotubes (MWCNT) targeting the development of a Field Effect Transistors (FET) on paper substrates. The CNTs were dispersed in a water solution with sodium dodecyl sulphate (SDS) through ultrasonication, ultrasonic bath and a centrifugation to remove the supernatant and have a homogeneous solution. Several deposition tests were performed using different types of CNTs, dis-persants, papers substrates and deposition techniques, such as spray coating and inkjet printing. The characterization of CNTs was made by Scanning Electron Microscopy (SEM) and Hall Effect. The most suitable CNT coatings able to be used as semiconductor in FETs were deposited by spray coat-ing on a paper substrate with hydrophilic nanoporous surface (FS2) at 100 ºC, 4 bar, 10 cm height, 5 second of deposition time and 90 seconds of drying between steps (4 layers of CNTs were deposited). Planar electrolyte gated FETs were produced with these layers using gold-nickel gate, source and drain electrodes. Despite the small current modulation (Ion/Ioff ratio of 1.8) one of these devices have p-type conduction with a field effect mobility of 1.07 cm2/V.s.

Control system for actuation and sensing in digital microfluidics devices

Digital Microfluidics (DMF) is a second generation technique, derived from the conventional microfluidics that instead of using continuous liquid fluxes, it uses only individual droplets driven by external electric signals. In this thesis a new DMF control/sensing system for visualization, droplet control (movement, dispensing, merging and splitting) and real time impedance measurement have been developed. The software for the proposed system was implemented in MATLAB with a graphical user interface. An Arduino was used as control board and dedicated circuits for voltage switching and contacts were designed and implemented in printed circuit boards. A high resolution camera was integrated for visualization. In our new approach, the DMF chips are driven by a dual-tone signal where the sum of two independent ac signals (one for droplet operations and the other for impedance sensing) is applied to the electrodes, and afterwards independently evaluated by a lock-in amplifier. With this new approach we were able to choose the appropriated amplitudes and frequencies for the different proposes (actuation and sensing). The measurements made were used to evaluate the real time droplet impedance enabling the knowledge of its position and velocity. This new approach opens new possibilities for impedance sensing and feedback control in DMF devices.

Digital microfluidic devices: the role of the dielectric layer

Digital microfluidics (DMF) is a field which has emerged in the last decade as a re-liable and versatile tool for sensing applications based on liquid reactions. DMF allows the discrete displacement of droplets, over an array of electrodes, by the application of voltage, and also the dispensing from a reservoir, mixing, merging and splitting fluidic operations. The main drawback of these devices is due to the need of high driving volt-ages for droplet operations. In this work, alternative dielectric layers combinations were studied aiming the reduction of these driving voltages. DMF chips were designed, pro-duced and optimized according to the theory of electrowetting-on-dielectric, adopting different combinations of parylene-C and tantalum pentoxide (Ta2O5) as dielectric ma-terials, and Teflon as hydrophobic layer. With both devices’ configurations, i.e., Parylene as single dielectric, and multilayer chips combining Parylene and Ta2O5, it was possible to perform all the fluidic opera-tions in the microliter down to hundreds of nanoliters range. Multilayer chips presented significant reduction on driving voltages for droplet op-erations in silicone oil filler medium: from 70 V (parylene only) down to 30 V (parylene/Ta2O5) for dispensing; and from 50 V (parylene only) down to 15 V (parylene/Ta2O5) for movement. Peroxidase colorimetric reactions were successfully performed as proof-of-concept, using multilayer configuration devices.

Nanostructuring silicon probes via electrodeposition: characterization of electrode coatings for acute in vivo neural recordings

Understanding how the brain works will require tools capable of measuring neuron elec-trical activity at a network scale. However, considerable progress is still necessary to reliably increase the number of neurons that are recorded and identified simultaneously with existing mi-croelectrode arrays. This project aims to evaluate how different materials can modify the effi-ciency of signal transfer from the neural tissue to the electrode. Therefore, various coating materials (gold, PEDOT, tungsten oxide and carbon nano-tubes) are characterized in terms of their underlying electrochemical processes and recording ef-ficacy. Iridium electrodes (177-706 μm2) are coated using galvanostatic deposition under different charge densities. By performing electrochemical impedance spectroscopy in phosphate buffered saline it is determined that the impedance modulus at 1 kHz depends on the coating material and decreased up to a maximum of two orders of magnitude for PEDOT (from 1 MΩ to 25 kΩ). The electrodes are furthermore characterized by cyclic voltammetry showing that charge storage capacity is im-proved by one order of magnitude reaching a maximum of 84.1 mC/cm2 for the PEDOT: gold nanoparticles composite (38 times the capacity of the pristine). Neural recording of spontaneous activity within the cortex was performed in anesthetized rodents to evaluate electrode coating performance.

Functional characterization and directed engineering of redox proteins for a rational improvement of Bioelectrochemical systems

In recent years, new methods of clean and environmentally friendly energy production have been the focus of intense research efforts. Microbial fuel cells (MFCs) are devices that utilize naturally occurring microorganisms that feed on organic matter, like waste water, while producing electrical energy. The natural habitats of bacteria thriving in microbial fuel cells are usually marine and freshwater sediments. These microorganisms are called dissimilatory metal reducing bacteria (DMRB), but in addition to metals like iron and manganese, they can use organic compounds like DMSO or TMAO, radionuclides and electrodes as terminal electron acceptors in their metabolic pathways.(...)

Digital microfluidics on paper

This thesis is one of the first reports of digital microfluidics on paper and the first in which the chip’s circuit was screen printed unto the paper. The use of the screen printing technique, being a low cost and fast method for electrodes deposition, makes the all chip processing much more aligned with the low cost choice of paper as a substrate. Functioning chips were developed that were capable of working at as low as 50 V, performing all the digital microfluidics operations: movement, dispensing, merging and splitting of the droplets. Silver ink electrodes were screen printed unto paper substrates, covered by Parylene-C (through vapor deposition) as dielectric and Teflon AF 1600 (through spin coating) as hydrophobic layer. The morphology of different paper substrates, silver inks (with different annealing conditions) and Parylene deposition conditions were studied by optical microscopy, AFM, SEM and 3D profilometry. Resolution tests for the printing process and electrical characterization of the silver electrodes were also made. As a showcase of the applications potential of these chips as a biosensing device, a colorimetric peroxidase detection test was successfully done on chip, using 200 nL to 350 nL droplets dispensed from 1 μL drops.

Experimental and modeling studies on reverse electrodialysis for sustainable power generation

A potentially renewable and sustainable source of energy is the chemical energy associated with solvation of salts. Mixing of two aqueous streams with different saline concentrations is spontaneous and releases energy. The global theoretically obtainable power from salinity gradient energy due to World’s rivers discharge into the oceans has been estimated to be within the range of 1.4-2.6 TW. Reverse electrodialysis (RED) is one of the emerging, membrane-based, technologies for harvesting the salinity gradient energy. A common RED stack is composed by alternately-arranged cation- and anion-exchange membranes, stacked between two electrodes. The compartments between the membranes are alternately fed with concentrated (e.g., sea water) and dilute (e.g., river water) saline solutions. Migration of the respective counter-ions through the membranes leads to ionic current between the electrodes, where an appropriate redox pair converts the chemical salinity gradient energy into electrical energy. Given the importance of the need for new sources of energy for power generation, the present study aims at better understanding and solving current challenges, associated with the RED stack design, fluid dynamics, ionic mass transfer and long-term RED stack performance with natural saline solutions as feedwaters. Chronopotentiometry was used to determinate diffusion boundary layer (DBL) thickness from diffusion relaxation data and the flow entrance effects on mass transfer were found to avail a power generation increase in RED stacks. Increasing the linear flow velocity also leads to a decrease of DBL thickness but on the cost of a higher pressure drop. Pressure drop inside RED stacks was successfully simulated by the developed mathematical model, in which contribution of several pressure drops, that until now have not been considered, was included. The effect of each pressure drop on the RED stack performance was identified and rationalized and guidelines for planning and/or optimization of RED stacks were derived. The design of new profiled membranes, with a chevron corrugation structure, was proposed using computational fluid dynamics (CFD) modeling. The performance of the suggested corrugation geometry was compared with the already existing ones, as well as with the use of conductive and non-conductive spacers. According to the estimations, use of chevron structures grants the highest net power density values, at the best compromise between the mass transfer coefficient and the pressure drop values. Finally, long-term experiments with natural waters were performed, during which fouling was experienced. For the first time, 2D fluorescence spectroscopy was used to monitor RED stack performance, with a dedicated focus on following fouling on ion-exchange membrane surfaces. To extract relevant information from fluorescence spectra, parallel factor analysis (PARAFAC) was performed. Moreover, the information obtained was then used to predict net power density, stack electric resistance and pressure drop by multivariate statistical models based on projection to latent structures (PLS) modeling. The use in such models of 2D fluorescence data, containing hidden, but extractable by PARAFAC, information about fouling on membrane surfaces, considerably improved the models fitting to the experimental data.

Engineering of metal oxide nanoparticles for application in electrochemical devices

The growing demand for materials and devices with new functionalities led to the increased inter-est in the field of nanomaterials and nanotechnologies. Nanoparticles, not only present a reduced size as well as high reactivity, which allows the development of electronic and electrochemical devices with exclusive properties, when compared with thin films. This dissertation aims to explore the development of several nanostructured metal oxides by sol-vothermal synthesis and its application in different electrochemical devices. Within this broad theme, this study has a specific number of objectives: a) research of the influence of the synthesis parameters to the structure and morphology of the nanoparticles; b) improvement of the perfor-mance of the electrochromic devices with the application of the nanoparticles as electrode; c) application of the nanoparticles as probes to sensing devices; and d) production of solution-pro-cessed transistors with a nanostructured metal oxide semiconductor. Regarding the results, several conclusions can be exposed. Solvothermal synthesis shows to be a very versatile method to control the growth and morphology of the nanoparticles. The electrochromic device performance is influenced by the different structures and morphologies of WO3 nanoparticles, mainly due to the surface area and conductivity of the materials. The dep-osition of the electrochromic layer by inkjet printing allows the patterning of the electrodes without wasting material and without any additional steps. Nanostructured WO3 probes were produced by electrodeposition and drop casting and applied as pH sensor and biosensor, respectively. The good performance and sensitivity of the devices is explained by the high number of electrochemical reactions occurring at the surface of the na-noparticles. GIZO nanoparticles were deposited by spin coating and used in electrolyte-gated transistors, which promotes a good interface between the semiconductor and the dielectric. The produced transistors work at low potential and with improved ON-OFF current ratio, up to 6 orders of mag-nitude. To summarize, the low temperatures used in the production of the devices are compatible with flexible substrates and additionally, the low cost of the techniques involved can be adapted for disposable devices.