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.

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

Validation of alternative additives for paste production

Dissertation presented to confer Master Degree in Chemical and Biochemical Engineering

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

Molecular simulation of gas adsorption equilibria in nanoporous materials

This work is divided into two distinct parts. The first part consists of the study of the metal organic framework UiO-66Zr, where the aim was to determine the force field that best describes the adsorption equilibrium properties of two different gases, methane and carbon dioxide. The other part of the work focuses on the study of the single wall carbon nanotube topology for ethane adsorption; the aim was to simplify as much as possible the solid-fluid force field model to increase the computational efficiency of the Monte Carlo simulations. The choice of both adsorbents relies on their potential use in adsorption processes, such as the capture and storage of carbon dioxide, natural gas storage, separation of components of biogas, and olefin/paraffin separations. The adsorption studies on the two porous materials were performed by molecular simulation using the grand canonical Monte Carlo (μ,V,T) method, over the temperature range of 298-343 K and pressure range 0.06-70 bar. The calibration curves of pressure and density as a function of chemical potential and temperature for the three adsorbates under study, were obtained Monte Carlo simulation in the canonical ensemble (N,V,T); polynomial fit and interpolation of the obtained data allowed to determine the pressure and gas density at any chemical potential. The adsorption equilibria of methane and carbon dioxide in UiO-66Zr were simulated and compared with the experimental data obtained by Jasmina H. Cavka et al. The results show that the best force field for both gases is a chargeless united-atom force field based on the TraPPE model. Using this validated force field it was possible to estimate the isosteric heats of adsorption and the Henry constants. In the Grand-Canonical Monte Carlo simulations of carbon nanotubes, we conclude that the fastest type of run is obtained with a force field that approximates the nanotube as a smooth cylinder; this approximation gives execution times that are 1.6 times faster than the typical atomistic runs.

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.