Electrocatalysis for selective oxidation of biomass-derived compounds

Producing chemicals from biomass feedstocks is a potential route for reducing our dependence on unsustainable petroleum resources. However, unlike petrochemicals, biomass-derived chemicals are highly oxygenated, and there is a need to develop new processes to efficiently upgrade them to useful chemicals and products. One approach is to perform the desired transformations using electrochemistry. Electrochemical reactions are usually conducted at mild temperatures, which is advantageous for the conversion of thermally-unstable bioderived compounds. Additionally, electroanalytical methods can reveal mechanistic information about the complex processes occurring at electrode/electrolyte interfaces. Electrochemical cells are uniquely capable of integrating sustainable chemical production and renewable energy conversion. Alcohol-fed fuel cells can convert chemical energy to electricity, and may also generate value-added chemical products. Electrolytic cells can utilize renewable energy directly by employing sunlight-absorbing semiconductor photoelectrodes, or indirectly by using renewable electricity to drive non-spontaneous reactions. Electrochemistry may play key roles in our future chemical and energy landscapes, yet fundamental and applied research is needed to develop these technologies. In this work, the electrochemical oxidation of biomass-derived compounds to valuable chemicals was explored. Specifically, this research focused on overcoming challenges related to catalyst activity and product selectivity for the electrocatalytic oxidation of bioderived polyols and polyfunctional molecules. Selective oxidation of vicinal diols under electrochemical conditions was studied using 1,2-propanediol (PDO) as a model compound. Potential-dependent pathways and selectivity trends were elucidated for PDO oxidation with carbon-supported Pt and Au nanoparticle electrocatalysts in anion-exchange membrane fuel cells. Electrochemical conversion of a polyfunctional substrate, namely 5-hydroxymethylfurfural (HMF), was studied in electrolytic flow cells. HMF was selectively oxidized to 2,5-furandicarboxylic acid (FDCA), an important precursor for biobased polymers, using carbon-supported palladium and gold bimetallic nanoparticle electrocatalysts. Tuning anode potential and catalyst composition were critical for achieving high selectivity to FDCA. Efficient photoelectrochemical oxidation of HMF to FDCA was demonstrated using a homogeneous electrocatalyst, 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO), together with a heterostructured photoanode. Modifying bismuth vanadate semiconductor films with electrodeposited cobalt phosphate simultaneously enhanced TEMPO oxidation photocurrent and suppressed the undesired oxygen evolution reaction

Electrochemical conversion of biomass-derived furanics for production of renewable chemicals and fuels

Replacing fossil-based fuels and chemicals with biobased alternatives can help alleviate our heavy dependence on petroleum sources, reduce the global carbon footprint, and strengthen our energy security. Electrocatalytic conversion of biomass-derived platform molecules is an emerging route for sustainable fuel and chemical production, with the advantages of eliminating harmful reagents, being tunable, and potentially being driven by renewable electricity. However, the widespread application of organic electrocatalysis is hindered by limitations such as low catalytic activity, product selectivity and energy efficiency. The goals of this work were to explore the electrochemical conversion of biobased furanics and develop more efficient electrocatalysts and processes for fuel and chemical production. The electrochemical reduction of furfural was investigated on metal electrodes in acidic aqueous electrolytes. Two mechanisms, namely electrocatalytic hydrogenation and direct electroreduction, were distinguished through a combination of voltammetry, bulk electrolysis, thiol-electrode modifications, and kinetic isotope effect studies. Better understanding of the underlying mechanisms and pathways enabled the manipulation of product selectivity. By rationally tuning applied potential, electrolyte pH, and bulk furfural concentration, the selective and efficient formation of a biofuel additive (i.e. 2-methylfuran) or a precursor for polymer and resin synthesis (i.e. furfuryl alcohol) was achieved. Pairing 5-(hydroxymethyl)furfural (HMF) reduction and oxidation half-reactions in a single electrochemical cell enabled efficient HMF conversion to biobased monomers. Electrocatalytic hydrogenation of HMF to 2,5-bis(hydroxymethyl)furan (BHMF) was achieved under mild conditions using Ag/C as the cathode catalyst. The competition between Ag-catalyzed HMF hydrogenation to BHMF and undesired HMF hydrodimerization and hydrogen evolution reactions was sensitive to cathode potential. Accordingly, precise control of the cathode potential was critical for achieving high BHMF selectivity and efficiency. In contrast, the selectivity of HMF oxidation facilitated by a homogeneous electrocatalyst, 4-acetamido-TEMPO (ACT, TEMPO = 2,2,6,6‐tetramethylpiperidine‐1‐oxyl), together with an inexpensive carbon felt electrode was not dependent on anode potential. Thus, it was feasible to conduct HMF hydrogenation to BHMF and oxidation to 2,5-furandicarboxylic acid (FDCA) in a single cathode-potential-controlled cell, achieving remarkable overall electron efficiency

A Neurocomputational Model of the Functional Role of Dopamine in Stimulus-Response Task Learning and Performance

Thesis (Ph.D.) - Indiana University, Psychology, 2009The neuromodulatory neurotransmitter dopamine (DA) plays a complex, but central role in the learning and performance of stimulus-response (S-R) behaviors. Studies have implicated DA's role in reward-driven learning and also its role in setting the overall level of vigor or frequency of response. Here, a neurocomputational model is developed which models DA's influence on a set of brain regions believed to be involved in the learning and execution of S-R tasks, including frontal cortex, basal ganglia, and cingulate cortex. An `actor' component of the model is trained, using `babble' (random behavior selection) and `critic' (rewarding and punishing) components of the model, to perform acceptance/rejection responses upon presentation of color stimuli in the context of recently presented auditory tones. The model behaves like an autonomous organism learning (and relearning) through `trial-and-error'. The focus of the study, the impact of hypo- and hyper-normal DA activity on this model, is investigated by three different dopaminergic pathways--two striatal and one prefrontal cortical--being manipulated independently during the learning and performance of the color response task. Hypo-DA conditions, analogous to Parkinsonism, cause slowing and reduction of frequency of learned responses, and, at extremes, degrade the learning (either initial or reversal) of the task. Hyper-DA conditions, analogous to psychostimulant effects, cause more rapid response times, but also can lead to perseveration of incorrect learning of response on the task. The presence of these effects often depends on which DA-ergic pathway is manipulated, however, which has implications for interpretation of the pharmacological experimental data. The proposed model embodies an integrative theory of dopamine function which suggests that the base rate of DA cell activity encodes the overall `activity-oriented motivation' of the organism, with hunger and/or expectation of reward driving both response vigor and tendency to generate an explorative `babble' response. This more `tonic' feature of DA functionality coexists naturally with the more extensively-studied `phasic' reward-learning features. The model may provide better insights on the role of DA system dysfunction in the cognitive and motivational symptoms of disorders such as Parkinsonism, psychostimulant abuse, ADHD, OCD, and schizophrenia, accounting for deficits in both learning and performance of tasks

Infrared testing of electronic components Final report, 5 Apr. 1965 - 5 Jun. 1966

Infrared radiation nondestructive test technique for electrical/electronic equipmen

Reasons given by Iowa women for attending homemaking classes for adults

Educators might find clues to help them interest more or different women in enrolling in homemaking classes for adults from the important reasons for attendance given by women attending classes. From 1,358 women in such classes in Iowa in 1949-51, this kind of information was obtained by asking them to answer a questionnaire during an adult class meeting. Each woman rated the relative importance of each of 43 possible reasons for her own attendance by checking “much,” “ some” or “none.” These responses were studied reason by reason in their relationship to such factors as education, age group, occupation of husband, number and ages of children in the family, subject of study in the classes and size of town in which classes were held. As a result, it is possible to tell whether certain reasons seemed more important to homemakers with certain characteristics than to those with other characteristics; for example, to those with less than eighth grade education than to those with college degrees.https://lib.dr.iastate.edu/specialreports/1007/thumbnail.jp

Cognition and the Statistics of Natural Signals

This paper illustiates how the statistical structure of natural signals may help understand cognitive phenomena. We focus on a regularity found in audio visual speech perception. Experiments by Massaro and colleagues consistently show that optic and acoustic speech signals have separable influences on perception. From a Bayesian point of view this regularity reflects a perceptual system that treats optic and acoustic speech as if they were conditionally independent signals. In this paper we perform a statistical analysis of a database of audiovisual speech to check whether optic and acoustic speech signals are indeed conditionally independent. If so, the regularities found by Massaro and colleagues could be seen as an optimal processing strategy of the perceptual system. We analyze a small database of audio visual speech using hidden Markov models, the most successful models in automatic speech recognition. The results suggest that acoustic and optic speech signals are indeed conditionally independent and that therefore, the separability found by Massaro and colleagues may be explained in terms of optimal perceptual processing: Independent processing of optic and acoustic speech results in no significant loss of information

A Parallel Genetic Algorithm For Tuning Neural Networks

One challenge in using artificial neural networks is how to determine appropriate parameters for network structure and learning. Often parameters such as learning rate or number of hidden units are set arbitrarily or with a general intuition as to what would be most effective. The goal of this project is to use a genetic algorithm to tune a population of neural networks to determine the best structure and parameters. This paper considers a genetic algorithm to tune the number of hidden units, learning rate, momentum, and number of examples viewed per weight update. Experiments and results are discussed for two domains with distinct properties, demonstrating the importance of careful tuning of network parameters and structure for best performance

Experimental evolution of the Caenorhabditis elegans sex determination pathway

Sex determination is a critical developmental decision with major ecological and evolutionary consequences, yet a large variety of sex determination mechanisms exist and we have a poor understanding of how they evolve. Theoretical and empirical work suggest that compensatory adaptations to mutations in genes involved in sex determination may play a role in the evolution of these pathways. Here, we directly address this problem using experimental evolution in Caenorhabditis elegans lines fixed for a pair of mutations in two key sex-determining genes that jointly render sex determination temperature-sensitive and cause intersexual (but still weakly to moderately fertile) phenotypes at intermediate temperatures. After 50 generations, evolved lines clearly recovered toward wild-type phenotypes. However, changes in transcript levels of key sex-determining genes in evolved lines cannot explain their partially (or in some cases, nearly completely) rescued phenotypes, implying that wild-type phenotypes can be restored independently of the transcriptional effects of these mutations. Our findings highlight the microevolutionary flexibility of sex determination pathways and suggest that compensatory adaptation to mutations can elicit novel and unpredictable evolutionary trajectories in these pathways, mirroring the phylogenetic diversity, and macroevolutionary dynamics of sex determination mechanisms

Biologist in a Box

Final report and team photo for Project 20 of ME450, Fall 2010 semester.In order to better study the relationship between the chewing rate of mammals and their body mass, Professor Geoffrey Gerstner, Ph.D, of the University of Michigan School of Dentistry tasked us with designing and building an apparatus that would film free roaming mammals chewing and simultaneously record their body mass.Geoffrey Gerstner (Biologic and Materials Sciences, U of M)http://deepblue.lib.umich.edu/bitstream/2027.42/86247/1/ME450 Fall2010 Final Report - Project 20 - Biologist in a Box.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/86247/2/ME450 Fall2010 Team Photo - Project 20 - Biologist in a Box.jp