La croissance de polymères bidimensionnels a été étudiée sous ultra-haut vide sur des surfaces métalliques par microscopie à effet tunnel (STM). La première étude concerne la croissance de réseaux nanoporeux covalents bidimensionnels obtenus par réaction de déshydratation des molécules d'acide 1.4 diboronique (BDBA). Les meilleurs réseaux sont obtenus pour les dépôts à flux élevés sur des substrats d'Argent chauffés à 150°C. La deuxième étude concerne la réaction chimique entre les molécules de 1,2,4,5 tétracyanobenzène (TCNB) et les atomes de Fer. La formation de liaison covalente entre les molécules et les atomes de Fer est thermiquement activée par des recuits à 200°C et permet la formation d'octacyanophtalocyanine de Fer. Ce travail ouvre de nouvelles perspectives pour la fabrication de matériaux 2D originaux obtenus par des réactions chimiques de surface. / The growth of two dimensional polymers has been studied under ultra-high vacuum on metallic surfaces by scanning tunnelling microscopy (STM). The first study relates on the growth of two dimensional covalent nanoporous networks obtained after dehydratation reaction of the 1,4 diboronic acid benzene molecule (BDBA). The best networks have been obtained with high molecular flux on silver metallic substrates maintained at 150°C during deposition. The second study relates on chemical reaction between the 1, 2, 4, 5 tetracyanobenzene molecule (TCNB) and iron atoms. The reaction takes place after an annealing at 200°C. In that case some iron octacyanophthalocyanine have been successfully synthesised at surfaces. This work opens new perspectives for the fabrication of 2D original materials by chemical reactions on surfaces.

The behavior of the Deoxyribonucleic Acid (DNA) molecule can be exploited to perform useful computation. It can also be ”programmed” using the language of Chemical Reaction Networks (CRNs). One specialized CRN construct is the Surface Chemical Reaction Network (SCRN). The SCRN construct can implement asynchronous cellular automata, which can in turn be used to implement digital logic circuits. SCRN based digital logic circuits are thought to have several advantages over regular CRN circuits. One of these proposed advantages is their scalability. This thesis investigates the scalability of SCRN based adder circuits, how does an increase in the number of bits affect the time required for the circuit to produce a correct result? Additionally, how is the throughput of the circuit affected when multiple additions are performed in a pipelined fashion? These questions are studied through experiments where the execution of optimized SCRN adder circuits is simulated. Due to the stochastic nature of SCRNs each such execution is all but guaranteed to be unique, requiring the simulation of the circuits to be repeated until a sufficiently large statistical sample has been collected. The results show these samples to follow a Gaussian distribution, regardless of the number of bits or the number of pipelined operations. The experiments show the simulated latency of the studied SCRN adder circuits to scale linearly with the number of input bits. The results also show that the throughput can be greatly improved through the pipelining of multiple operations. However, the results are inconclusive as to the maximum possible throughput of SCRN adder circuits. A conclusion of the project is that SCRN digital logic circuit design could conceivably benefit from the implementation of specialized components beyond the standard logic gates. / DNA-molekylen kan utnyttjas för att genomföra användbara beräkningar. Den kan också ”programmeras” via abstraktionen kemiska reaktionsnätverk. Ytbundna Kemiska Reaktionsnätverk (YKR) är i sin tur en vidare specialisering av sådana reaktionsnätverk. Ett YKR kan implementera en asynkrona cellulära automat, som i sin tur kan implementera kretsar för digital logik. Kretsar för digital logik byggda med YKR anses ha flera fördelar gentemot motsvarande kretsar byggda från vanliga kemiska reaktionsnätverk. En av dessa tilltänkta fördelar ligger i deras skalbarhet. Detta examensarbete undersöker skalbarheten hos YKR-baserade additions-kretsar, hur påverkar ett ökat antal bitar tiden som krävs för att kretsen ska producera ett korrekt resultat? Vidare, hur påverkas genomströmningen när flera operationer matas in direkt och genomför efter varandra i en pipeline? Dessa frågor studeras genom experiment där körningar av optimerande YKR-baserade additionskretsar simuleras. På grund av de stokastiska egenskaperna hos YKR är varje sådan körning i princip garanterad att vara unik, vilket kräver upprepade simuleringar av varje krets tills ett tillräckligt stort statistiskt urval har insamlats. Dessa resultat visar sig följa en normalfördelningskurva, oavsett antalet bitar eller antalet operationer som matats in i en pipeline. Experimenten visar att den simulerade latensen skalar linjärt med antalet indata-bitar för de studerade additionskretsarna. Resultaten visar även att genomströmningen förbättras avsevärt när flera operationer körs direkt efter varandra i en pipeline. Resultaten är dock ofullständiga när det gäller uppmätandet av additionskretsarna högsta möjliga genomströmning. En slutsats av projektet är att YKR-baserade kretsar för digital logik möjligen skulle kunna gagnas av implementerandet av specialiserade komponenter utöver de vanliga logikgrindarna.

Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signalto-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently iv proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the v planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research.