Functionalization of Si(111) surfaces and the formation of mixed monolayers for the covalent attachment of molecular catalysts in photoelectrochemical devices

The functionalization of silicon surfaces with molecular catalysts for proton reduction is an important part of the development of a solar-powered, water-splitting device for solar fuel formation. The covalent attachment of these catalysts to silicon without damaging the underlying electronic properties of silicon that make it a good photocathode has proven difficult. We report the formation of mixed monolayer-functionalized surfaces that incor- porate both methyl and vinylferrocenyl or vinylbipyridyl (vbpy) moieties. The silicon was functionalized using reaction conditions analogous to those of hydrosilylation, but instead of a H-terminated Si surface, a chlorine-terminated Si precursor surface was used to produce the linked vinyl-modified functional group. The functionalized surfaces were characterized by time-resolved photoconductivity decay, X-ray photoelectron spectroscopy (XPS), electro- chemical, and photoelectrochemical measurements. The functionalized Si surfaces were well passivated, exhibited high surface coverage and few remaining reactive Si atop sites, had a very low surface recombination velocity, and displayed little initial surface oxidation. The surfaces were stable toward atmospheric and electrochemical oxidation. The surface coverage of ferrocene or bipyridine was controllably varied from 0 up to 30% of a monolayer without loss of the underlying electronic properties of the silicon. Interfacial charge transfer to the attached ferrocene group was relatively rapid, and a photovoltage of 0.4 V was generated upon illumination of functionalized n-type silicon surfaces in CH₃CN. The immobilized bipyridine ligands bound transition metal ions, and thus enabled the assembly of metal complexes on the silicon surface. XPS studies demonstrated that [Cp∗Rh(vbpy)Cl]Cl, [Cp∗Ir(vbpy)Cl]Cl, and Ru(acac)₂vbpy were assembled on the surface. For the surface prepared with iridium, x-ray absorption spectroscopy at the Ir L_III edge showed an edge energy and post-edge features virtually identical to a powder sample of [Cp∗Ir(bipy)Cl]Cl (bipy is 2,2 ́-bipyridyl). Electrochemical studies on these surfaces confirmed that the assembled complexes were electrochemically active.

New optoelectronic devices using GaAs-GaAlAs epitaxy

Three subjects related to epitaxial GaAs-GaAlAs optoelectronic devices are discussed in this thesis. They are:

1. Embedded Epitaxy

This is a technique of selective multilayer growth of GaAs- Ga_1-xAl_xAs single crystal structures through stripe openings in masking layers on GaAs substrates. This technique results in prismatic layers of GaAs and Ga_1-xAl_xAs "embedded" in each other and leads to controllable uniform structures terminated by crystal faces. The dependence of the growth habit on the orientation of the stripe openings has been studied. Room temperature embedded double heterostructure lasers have been fabricated using this technique. Threshold current densities as low as 1.5 KA/cm² have been achieved.

2. Barrier Controlled PNPN Laser Diode

It is found that the I-V characteristics of a PNPN device can be controlled by using potential barriers in the base regions. Based on this principle, GaAs-GaAlAs heterostructure PNPN laser diodes have been fabricated. GaAlAs potential barriers in the bases control not only the electrical but also the optical properties of the device. PNPN lasers with low threshold currents and high breakover voltage have been achieved. Numerical calculations of this barrier controlled structure are presented in the ranges where the total current is below the holding point and near the lasing threshold.

3. Injection Lasers on Semi-Insulating Substrates

GaAs-GaAlAs heterostructure lasers fabricated on semi-insulating substrates have been studied. Two different laser structures achieved are: (1) Crowding effect lasers, (2) Lateral injection lasers. Experimental results and the working principles underlying the operation of these lasers are presented. The gain induced guiding mechanism is used to explain the lasers' far field radiation patterns. It is found that Zn diffusion in Ga_1-xAl_xAs depends on the Al content x, and that GaAs can be used as the diffusion mask for Zn diffusion in Ga_1-xAl_xAs. Lasers having very low threshold currents and operating in a stable single mode have been achieved. Because these lasers are fabricated on semi-insulating substrates, it is possible to integrate them with other electronic devices on the same substrate. An integrated device, which consists of a crowding effect laser and a Gunn oscillator on a common semi-insulating GaAs substrate, has been achieved.

Polilaktida oinarri duten 3D zelula-euskarri (scaffold) polimeriko biodegradagarrien diseinu, fabrikazio eta propietateak

[EU]Hiru dimentsioko inprimaketa etorkizun handiko teknologia bezala azaltzen zaigu gaur egun. Esate baterako, biomedikuntza arloan aukera berritzaileak ekar ditzake, baina baita hezkuntza, heziketa eta ikerketa munduetan ere. Teknologia berri honen abantailarik nagusiena prototipatze azkarrean datza, eta honi esker, mikro- eta makro- egitura definituak dituzten objektuak diseinatu eta fabrikatu daitezke modu lehiakorrean. Lan honen helburua 3D inprimagailu baten bitartez inprimaturiko polimero biobateragarri eta biodegradagarrietan oinarrituriko ereduen garapen eta fabrikazioan datza. Hala ere, lehenik eta behin, lehengaiak bai fisikoki eta bai termikoki karakterizatu behar dira, ondoren, 3D inprimagailuaren parametroen arteko erlazioa ezarri, eta azkenik, produktu finalaren egitura propietateak eta kalitatea aztertu. Aipaturiko lana aurrera eramateko erabili den materiala polilaktida (PLA) izan da, zeinen erabilera oso zabaldua dagoen medikuntza arloan inplante (torloju, iltze, plaka eta abar) moduan eta ehun ingeniaritzaren munduan.

Neocarzinostatin chromophore: structural, mechanistic, and synthetic studies

Neocarzinostatin chromophore 1 is the active component of the antitumor antibiotic neocarzinostatin (NCS). The chromophore reacts with thiols to form a highly strained cumulene-enyne species which rapidly rearranges to a biradical intermediate which can abstract hydrogen atoms from DNA, leading to strand cleavage. DNA damage is the proposed source of biological activity for NCS. The structure of the methyl thioglycolate monoadduct 2 of NCS chromophore, including the absolute stereochemistry, was determined by NMR studies. The presence of the cumulene-enyne intermediate and the rearrangement to a biradical were supported by data from low temperature NMR investigations. Also included are synthetic approaches to NCS chromophore model compounds based on intramolecular addition of an acetylide to an aldehyde.

As transformações no ensino na saúde: um estudo sobre a extensão universitária e sua relação com a formação do profissional de saúde para o SUS

HENRIQUES, Regina Lúcia Monteiro.2011. 182 f. Tese (Doutorado em Saúde Coletiva) Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, 2011. Este estudo trata dos processos de mudança na formação em saúde, especialmente das questões político estruturais da articulação do ensino e da extensão para a construção de estratégias de transformação dos cursos da área da saúde. Tem por objetivos analisar as perspectiva dos sujeitos envolvidos na transformação curricular no que diz respeito à articulação entre práticas de ensino, extensão universitária e saúde, a constituição de novos cenários de prática e sua relação com a extensão universitária, assim como examinar os sentidos atribuídos pelos sujeitos aos processos políticos internos e externos às instituições de ensino. Parte-se da compreensão de que há em andamento um grande movimento de lutas pela mudança na formação em saúde e de algumas das ideias que têm sido discutidas e difundidas na defesa da reorientação da formação em saúde baseada em princípios e diretrizes consoantes com a política de saúde e com a defesa de direitos de cidadania. Baseia-se em concepções de espaço cotidiano das instituições de formação superior em saúde e dos serviços de saúde e outros cenários de prática. Reflete sobre a responsabilidade política das instituições formadoras e da extensão universitária como lugar de tornar públicas as posições e valores defendidos para as práticas do cuidado e da relação social e política da universidade. Para a operacionalização do estudo, foram escolhidas duas instituições universitárias que se apresentaram e que foram selecionadas no programa do Pro-Saúde. Foram realizadas entrevistas com roteiro semi-estruturado com protagonistas de processos de mudança na formação nestas instituições, em três profissões da área da saúde, enfermagem, medicina e odontologia. Fez-se analise hermenêutica a partir dos sentidos que foram atribuídos a estas experiências concretas pelos atores que as empreenderam. Definiu-se três categorias de análise, a dimensão das políticas governamentais de incentivo a mudanças na formação em saúde e os efeitos das mesmas para as instituições de ensino; a ampliação de cenários de prática, as estratégias de aproximação entre os mundos do ensino e do trabalho e se significaram novas práticas de cuidado; relação existente entre os processos de transformação curricular e a extensão universitária. A extensão vem adquirindo protagonismo na construção de dispositivos de enfrentamento e superação das dificuldades e resistências nos processos de transformação curricular, além de criar com relativa liberdade novas possibilidades espaciais e conceituais para o cuidado em saúde, porém pelos próprios sentidos que assume desde a invisibilidade até a redenção da universidade sua baixa institucionalidade e reflexão impede que sua potencialidade seja tomada como aliada nestes processos de modo mais sistemático e impactante.

Acceleration Sensing, Feedback Cooling, and Nonlinear Dynamics with Nanoscale Cavity-Optomechanical Devices

Light has long been used for the precise measurement of moving bodies, but the burgeoning field of optomechanics is concerned with the interaction of light and matter in a regime where the typically weak radiation pressure force of light is able to push back on the moving object. This field began with the realization in the late 1960's that the momentum imparted by a recoiling photon on a mirror would place fundamental limits on the smallest measurable displacement of that mirror. This coupling between the frequency of light and the motion of a mechanical object does much more than simply add noise, however. It has been used to cool objects to their quantum ground state, demonstrate electromagnetically-induced-transparency, and modify the damping and spring constant of the resonator. Amazingly, these radiation pressure effects have now been demonstrated in systems ranging 18 orders of magnitude in mass (kg to fg).

In this work we will focus on three diverse experiments in three different optomechanical devices which span the fields of inertial sensors, closed-loop feedback, and nonlinear dynamics. The mechanical elements presented cover 6 orders of magnitude in mass (ng to fg), but they all employ nano-scale photonic crystals to trap light and resonantly enhance the light-matter interaction. In the first experiment we take advantage of the sub-femtometer displacement resolution of our photonic crystals to demonstrate a sensitive chip-scale optical accelerometer with a kHz-frequency mechanical resonator. This sensor has a noise density of approximately 10 micro-g/rt-Hz over a useable bandwidth of approximately 20 kHz and we demonstrate at least 50 dB of linear dynamic sensor range. We also discuss methods to further improve performance of this device by a factor of 10.

In the second experiment, we used a closed-loop measurement and feedback system to damp and cool a room-temperature MHz-frequency mechanical oscillator from a phonon occupation of 6.5 million down to just 66. At the time of the experiment, this represented a world-record result for the laser cooling of a macroscopic mechanical element without the aid of cryogenic pre-cooling. Furthermore, this closed-loop damping yields a high-resolution force sensor with a practical bandwidth of 200 kHZ and the method has applications to other optomechanical sensors.

The final experiment contains results from a GHz-frequency mechanical resonator in a regime where the nonlinearity of the radiation-pressure interaction dominates the system dynamics. In this device we show self-oscillations of the mechanical element that are driven by multi-photon-phonon scattering. Control of the system allows us to initialize the mechanical oscillator into a stable high-amplitude attractor which would otherwise be inaccessible. To provide context, we begin this work by first presenting an intuitive overview of optomechanical systems and then providing an extended discussion of the principles underlying the design and fabrication of our optomechanical devices.

Expanding the Toolkit for Synthetic Biology: Frameworks for Native-like Non-natural Gene Circuits

Synthetic biology combines biological parts from different sources in order to engineer non-native, functional systems. While there is a lot of potential for synthetic biology to revolutionize processes, such as the production of pharmaceuticals, engineering synthetic systems has been challenging. It is oftentimes necessary to explore a large design space to balance the levels of interacting components in the circuit. There are also times where it is desirable to incorporate enzymes that have non-biological functions into a synthetic circuit. Tuning the levels of different components, however, is often restricted to a fixed operating point, and this makes synthetic systems sensitive to changes in the environment. Natural systems are able to respond dynamically to a changing environment by obtaining information relevant to the function of the circuit. This work addresses these problems by establishing frameworks and mechanisms that allow synthetic circuits to communicate with the environment, maintain fixed ratios between components, and potentially add new parts that are outside the realm of current biological function. These frameworks provide a way for synthetic circuits to behave more like natural circuits by enabling a dynamic response, and provide a systematic and rational way to search design space to an experimentally tractable size where likely solutions exist. We hope that the contributions described below will aid in allowing synthetic biology to realize its potential.

Optimal Data Distributions in Machine Learning

In the first part of the thesis we explore three fundamental questions that arise naturally when we conceive a machine learning scenario where the training and test distributions can differ. Contrary to conventional wisdom, we show that in fact mismatched training and test distribution can yield better out-of-sample performance. This optimal performance can be obtained by training with the dual distribution. This optimal training distribution depends on the test distribution set by the problem, but not on the target function that we want to learn. We show how to obtain this distribution in both discrete and continuous input spaces, as well as how to approximate it in a practical scenario. Benefits of using this distribution are exemplified in both synthetic and real data sets.

In order to apply the dual distribution in the supervised learning scenario where the training data set is fixed, it is necessary to use weights to make the sample appear as if it came from the dual distribution. We explore the negative effect that weighting a sample can have. The theoretical decomposition of the use of weights regarding its effect on the out-of-sample error is easy to understand but not actionable in practice, as the quantities involved cannot be computed. Hence, we propose the Targeted Weighting algorithm that determines if, for a given set of weights, the out-of-sample performance will improve or not in a practical setting. This is necessary as the setting assumes there are no labeled points distributed according to the test distribution, only unlabeled samples.

Finally, we propose a new class of matching algorithms that can be used to match the training set to a desired distribution, such as the dual distribution (or the test distribution). These algorithms can be applied to very large datasets, and we show how they lead to improved performance in a large real dataset such as the Netflix dataset. Their computational complexity is the main reason for their advantage over previous algorithms proposed in the covariate shift literature.

In the second part of the thesis we apply Machine Learning to the problem of behavior recognition. We develop a specific behavior classifier to study fly aggression, and we develop a system that allows analyzing behavior in videos of animals, with minimal supervision. The system, which we call CUBA (Caltech Unsupervised Behavior Analysis), allows detecting movemes, actions, and stories from time series describing the position of animals in videos. The method summarizes the data, as well as it provides biologists with a mathematical tool to test new hypotheses. Other benefits of CUBA include finding classifiers for specific behaviors without the need for annotation, as well as providing means to discriminate groups of animals, for example, according to their genetic line.

Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement

Over the last century, the silicon revolution has enabled us to build faster, smaller and more sophisticated computers. Today, these computers control phones, cars, satellites, assembly lines, and other electromechanical devices. Just as electrical wiring controls electromechanical devices, living organisms employ "chemical wiring" to make decisions about their environment and control physical processes. Currently, the big difference between these two substrates is that while we have the abstractions, design principles, verification and fabrication techniques in place for programming with silicon, we have no comparable understanding or expertise for programming chemistry.

In this thesis we take a small step towards the goal of learning how to systematically engineer prescribed non-equilibrium dynamical behaviors in chemical systems. We use the formalism of chemical reaction networks (CRNs), combined with mass-action kinetics, as our programming language for specifying dynamical behaviors. Leveraging the tools of nucleic acid nanotechnology (introduced in Chapter 1), we employ synthetic DNA molecules as our molecular architecture and toehold-mediated DNA strand displacement as our reaction primitive.

Abstraction, modular design and systematic fabrication can work only with well-understood and quantitatively characterized tools. Therefore, we embark on a detailed study of the "device physics" of DNA strand displacement (Chapter 2). We present a unified view of strand displacement biophysics and kinetics by studying the process at multiple levels of detail, using an intuitive model of a random walk on a 1-dimensional energy landscape, a secondary structure kinetics model with single base-pair steps, and a coarse-grained molecular model that incorporates three-dimensional geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Our findings are consistent with previously measured or inferred rates for hybridization, fraying, and branch migration, and provide a biophysical explanation of strand displacement kinetics. Our work paves the way for accurate modeling of strand displacement cascades, which would facilitate the simulation and construction of more complex molecular systems.

In Chapters 3 and 4, we identify and overcome the crucial experimental challenges involved in using our general DNA-based technology for engineering dynamical behaviors in the test tube. In this process, we identify important design rules that inform our choice of molecular motifs and our algorithms for designing and verifying DNA sequences for our molecular implementation. We also develop flexible molecular strategies for "tuning" our reaction rates and stoichiometries in order to compensate for unavoidable non-idealities in the molecular implementation, such as imperfectly synthesized molecules and spurious "leak" pathways that compete with desired pathways.

We successfully implement three distinct autocatalytic reactions, which we then combine into a de novo chemical oscillator. Unlike biological networks, which use sophisticated evolved molecules (like proteins) to realize such behavior, our test tube realization is the first to demonstrate that Watson-Crick base pairing interactions alone suffice for oscillatory dynamics. Since our design pipeline is general and applicable to any CRN, our experimental demonstration of a de novo chemical oscillator could enable the systematic construction of CRNs with other dynamic behaviors.

DNA-Mediated Charge Transport Devices for Protein Detection

Detection of biologically relevant targets, including small molecules, proteins, DNA, and RNA, is vital for fundamental research as well as clinical diagnostics. Sensors with biological elements provide a natural foundation for such devices because of the inherent recognition capabilities of biomolecules. Electrochemical DNA platforms are simple, sensitive, and do not require complex target labeling or expensive instrumentation. Sensitivity and specificity are added to DNA electrochemical platforms when the physical properties of DNA are harnessed. The inherent structure of DNA, with its stacked core of aromatic bases, enables DNA to act as a wire via DNA-mediated charge transport (DNA CT). DNA CT is not only robust over long molecular distances of at least 34 nm, but is also especially sensitive to anything that perturbs proper base stacking, including DNA mismatches, lesions, or DNA-binding proteins that distort the π-stack. Electrochemical sensors based on DNA CT have previously been used for single-nucleotide polymorphism detection, hybridization assays, and DNA-binding protein detection. Here, improvements to (i) the structure of DNA monolayers and (ii) the signal amplification with DNA CT platforms for improved sensitivity and detection are described.

First, improvements to the control over DNA monolayer formation are reported through the incorporation of copper-free click chemistry into DNA monolayer assembly. As opposed to conventional film formation involving the self-assembly of thiolated DNA, copper-free click chemistry enables DNA to be tethered to a pre-formed mixed alkylthiol monolayer. The total amount of DNA in the final film is directly related to the amount of azide in the underlying alkylthiol monolayer. DNA monolayers formed with this technique are significantly more homogeneous and lower density, with a larger amount of individual helices exposed to the analyte solution. With these improved monolayers, significantly more sensitive detection of the transcription factor TATA binding protein (TBP) is achieved.

Using low-density DNA monolayers, two-electrode DNA arrays were designed and fabricated to enable the placement of multiple DNA sequences onto a single underlying electrode. To pattern DNA onto the primary electrode surface of these arrays, a copper precatalyst for click chemistry was electrochemically activated at the secondary electrode. The location of the secondary electrode relative to the primary electrode enabled the patterning of up to four sequences of DNA onto a single electrode surface. As opposed to conventional electrochemical readout from the primary, DNA-modified electrode, a secondary microelectrode, coupled with electrocatalytic signal amplification, enables more sensitive detection with spatial resolution on the DNA array electrode surface. Using this two-electrode platform, arrays have been formed that facilitate differentiation between well-matched and mismatched sequences, detection of transcription factors, and sequence-selective DNA hybridization, all with the incorporation of internal controls.

For effective clinical detection, the two working electrode platform was multiplexed to contain two complementary arrays, each with fifteen electrodes. This platform, coupled with low density DNA monolayers and electrocatalysis with readout from a secondary electrode, enabled even more sensitive detection from especially small volumes (4 μL per well). This multiplexed platform has enabled the simultaneous detection of two transcription factors, TBP and CopG, with surface dissociation constants comparable to their solution dissociation constants.

With the sensitivity and selectivity obtained from the multiplexed, two working electrode array, an electrochemical signal-on assay for activity of the human methyltransferase DNMT1 was incorporated. DNMT1 is the most abundant human methyltransferase, and its aberrant methylation has been linked to the development of cancer. However, current methods to monitor methyltransferase activity are either ineffective with crude samples or are impractical to develop for clinical applications due to a reliance on radioactivity. Electrochemical detection of methyltransferase activity, in contrast, circumvents these issues. The signal-on detection assay translates methylation events into electrochemical signals via a methylation-specific restriction enzyme. Using the two working electrode platform combined with this assay, DNMT1 activity from tumor and healthy adjacent tissue lysate were evaluated. Our electrochemical measurements revealed significant differences in methyltransferase activity between tumor tissue and healthy adjacent tissue.

As differential activity was observed between colorectal tumor tissue and healthy adjacent tissue, ten tumor sets were subsequently analyzed for DNMT1 activity both electrochemically and by tritium incorporation. These results were compared to expression levels of DNMT1, measured by qPCR, and total DNMT1 protein content, measured by Western blot. The only trend detected was that hyperactivity was observed in the tumor samples as compared to the healthy adjacent tissue when measured electrochemically. These advances in DNA CT-based platforms have propelled this class of sensors from the purely academic realm into the realm of clinically relevant detection.

Development of Microfluidic Devices with the Use of Nanotechnology to Aid in the Analysis of Biological Systems Including Membrane Protein Separation, Single Cell Analysis, and Genetic Markers

Computation technology has dramatically changed the world around us; you can hardly find an area where cell phones have not saturated the market, yet there is a significant lack of breakthroughs in the development to integrate the computer with biological environments. This is largely the result of the incompatibility of the materials used in both environments; biological environments and experiments tend to need aqueous environments. To help aid in these development chemists, engineers, physicists and biologists have begun to develop microfluidics to help bridge this divide. Unfortunately, the microfluidic devices required large external support equipment to run the device. This thesis presents a series of several microfluidic methods that can help integrate engineering and biology by exploiting nanotechnology to help push the field of microfluidics back to its intended purpose, small integrated biological and electrical devices. I demonstrate this goal by developing different methods and devices to (1) separate membrane bound proteins with the use of microfluidics, (2) use optical technology to make fiber optic cables into protein sensors, (3) generate new fluidic devices using semiconductor material to manipulate single cells, and (4) develop a new genetic microfluidic based diagnostic assay that works with current PCR methodology to provide faster and cheaper results. All of these methods and systems can be used as components to build a self-contained biomedical device.

Visual pattern recognition network: Its training algorithm and its optoelectronic architecture

A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition. (C) 1996 Optical Society of America