Nanoscale Intelligent Imaging Based on Real-Time Analysis of Approach Curve by Scanning Electrochemical Microscopy

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.analchem.9b02361.Scanning electrochemical microscopy (SECM) enables high-resolution imaging by examining the amperometric response of an ultramicroelectrode tip near a substrate. Spatial resolution, however, is compromised for non-flat substrates, where distances from a tip far exceed the tip size to avoid artifacts caused by the tip–substrate contact. Herein, we propose a new imaging mode of SECM based on real-time analysis of approach curve to actively control nanoscale tip–substrate distances without contact. The power of this software-based method is demonstrated by imaging an insulating substrate with step edges using standard instrumentation without combination of another method for distance measurement, e.g., atomic force microscopy. An ~500 nm-diameter Pt tip approaches down to ~50 nm from upper and lower terraces of a 500 nm-height step edge, which are located by real-time theoretical fitting of experimental approach curve to ensure the lack of electrochemical reactivity. The tip approach to step edge can be terminated at <20 nm prior to the tip–substrate contact as soon as the theory deviates from the tip current, which is analyzed numerically afterward to locate the inert edge. The advantageous local adjustment of tip height and tip current at the final point of tip approach distinguishes the proposed imaging mode from other modes based on standard instrumentation. In addition, the glass sheath of Pt tip is thinned to ~150 nm to rarely contact the step edge, which is unavoidable and instantaneously detected as an abrupt change in the slope of approach curve to prevent the damage of fragile nanotip

Probing High Permeability of Nuclear Pore Complexes by Scanning Electrochemical Microscopy: Ca2+ Effects on Transport Barriers

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http:doi.org/10.1021/acs.analchem.9b00796.The nuclear pore complex (NPC) solely mediates molecular transport between the nucleus and cytoplasm of a eukaryotic cell to play important biological and biomedical roles. However, it is not well-understood chemically how this biological nanopore selectively and efficiently transports various substances, including small molecules, proteins, and RNAs by using transport barriers that are rich in highly disordered repeats of hydrophobic phenylalanine and glycine intermingled with charged amino acids. Herein, we employ scanning electrochemical microscopy to image and measure the high permeability of NPCs to small redox molecules. The effective medium theory demonstrates that the measured permeability is controlled by diffusional translocation of probe molecules through water-filled nanopores without steric or electrostatic hindrance from hydrophobic or charged regions of transport barriers, respectively. However, the permeability of NPCs is reduced by a low millimolar concentration of Ca2+, which can interact with anionic regions of transport barriers to alter their spatial distributions within the nanopore. We employ atomic force microscopy to confirm that transport barriers of NPCs are dominantly recessed (∼80%) or entangled (∼20%) at the high Ca2+ level in contrast to authentic populations of entangled (∼50%), recessed (∼25%), and “plugged” (∼25%) conformations at a physiological Ca2+ level of submicromolar. We propose a model for synchronized Ca2+ effects on the conformation and permeability of NPCs, where transport barriers are viscosified to lower permeability. Significantly, this result supports a hypothesis that the functional structure of transport barriers is maintained not only by their hydrophobic regions, but also by charged regions

Influence of Fuel Injection System and Engine-Timing Adjustments on Regulated Emissions from Four Biodiesel Fuels

The use of biofuels for transportation has grown substantially in the past decade in response to federal mandates and increased concern about the use of petroleum fuels. As biofuels become more common, it is imperative to assess their influence on mobile source emissions of regulated and hazardous pollutants. This assessment cannot be done without first obtaining a basic understanding of how biofuels affect the relationship between fuel properties, engine design, and combustion conditions. Combustion studies were conducted on biodiesel fuels from four feedstocks (palm oil, soybean oil, canola oil, and coconut oil) with two injection systems, mechanical and electronic. For the electronic system, fuel injection timing was adjusted to compensate for physical changes caused by different fuels. The emissions of nitrogen oxides (NOx) and partial combustion products were compared across both engine injection systems. The analysis showed differences in NOx emissions based on hydrocarbon chain length and degree of fuel unsaturation, with little to no NOx increase compared with ultra-low sulfur diesel fuel for most conditions. Adjusting the fuel injection timing provided some improvement in biodiesel emissions for NOx and particulate matter, particularly at lower engine loads. The results indicated that the introduction of biodiesel and biodiesel blends could have widely dissimilar effects in different types of vehicle fleets, depending on typical engine design, age, and the feedstock used for biofuel production

From Coding to Characterizing: A Study of Electrocatalysts using Scanning Electrochemical Microscopy

Science has shown that climate change is caused by human activities that pump greenhouse gases into the atmosphere, largely through the transportation and energy sectors. As the impacts of climate change are becoming more apparent and devastating, there is a rising demand for sustainable and renewable alternatives to the energy system, such as the potential for using hydrogen as fuel. Currently, 95% of hydrogen is sourced from non-renewable energy resources, essentially offsetting the benefits of using hydrogen. The research in this dissertation aims to advance a green method of hydrogen production, water electrolysis, by increasing intrinsic activity and providing mechanistic insights of water-splitting electrocatalysts in alkaline media, and thereby increasing its economical benefits and encouraging commercial use. Another promising method for combating climate change is to capture the currently emitted carbon dioxide and recycle it by using electrocatalysts to electrochemically reduce it into carbon-based fuels. A second objective of this dissertation work furthers this aim by increasing the selectivity of CO2-reducing electrocatalysts for electrochemical CO2 reduction. Moreover, scanning electrochemical microscopy techniques are developed to assist with characterization of the previous two aims and to improve SECM characterizing capabilities. We first demonstrate a new operando multireactional (MR) mode of SECM that can distinguish between the electrochemical reduction of CO2 (CO2RR) to CO in aqueous media and the competing hydrogen evolution reaction (HER) as a function of applied potential. This study was performed on a gold electrode and the products generated were detected via a Pt tip electrode through selectively oxidizing H2 and electrochemically stripping CO on the Pt tip electrode. Via MR-SECM, we observed the optimum potential window to maximize CO2 reduction on a Au electrode in aqueous media, where CO2RR is too low at potentials less negative than -0.9 V vs. Ag/AgCl and CO2RR becomes dominated by HER at potentials more negative than -1.1 V vs. Ag/AgCl. MR-SECM is able to detect this phenomenon that is not possible to detect via traditional CV measurements and is a significant improvement over traditional product detection via bulk analysis. Furthering the CO2RR work, we demonstrate a new synthesis technique of in situ reduction of In2O3 nanoparticles to form an In0-In2O3 composite that changes the selectivity from the typical bulk indium product, formate, to CO. By electrochemically reducing this In2O3 nanocatalyst in Ar-saturated electrolytes prior to CO2 exposure, the meta-stable oxide layer is removed and allows the CO2 to react with In0 instead of In2O3. We found that this change in In0-In2O3 composite material changes the selectivity of CO2RR to nearly 100% CO. This tunability is attributed to the change of environment that makes the first electron-transfer step to form the surface-adsorbed intermediates highly reversible. The substrate generation/tip collection (SG-TC) mode of SECM was used to investigate the potential dependent product detection of the catalyst, allowing us to electrochemically collect any CO and/or H2 that is produced on the surface. SG-TC SECM revealed that the redox feature that was observed before the high onset of current did not generate any CO or H2 and that the In2O3 electrocatalyst started to generate products just after -1.0 V vs. Ag/AgCl, a relatively low overpotential. This technique allows for in situ collection of CO as it is produced on the catalytic surface during the voltammetry experiment, resulting in accurate potential dependent measurements of CO production. The next part of this dissertation focuses on the OER and HER for the electrolysis of water in alkaline. The OER is a major bottleneck for water electrolysis because the four electron/four proton transfer limits the overall efficiency. NiFe oxide electrocatalysts, specifically ratios of Ni0.8:Fe0.2, have emerged as alkaline OER catalysts that rival the high activity of precious metal electrocatalysts. Herein, we demonstrate a new microwave-assisted route to synthesize nanoamorphous Ni0.8:Fe0.2 electrocatalysts with higher activity than the crystalline-derived Ni0.8:Fe0.2 structure, reducing the OER overpotential by ∼100 mV. While the crystalline-derived Ni0.8:Fe0.2 catalyst was synthesized at high temperatures (525 ◦C) and electrochemical conditioning, the nanoamorphous Ni0.8:Fe0.2 catalyst was formed through a 2-minute microwave-heating step. The surface interrogation (SI) mode of SECM was used to probe the kinetics of the active sites on each surface, revealing that the crystalline Ni0.8:Fe0.2 had two types of sites, a “slow” site with a rate constant of 0.05 s−1 and a “fast” site with a rate constant of 1.3 s−1, while the nanoamorphous Ni0.8:Fe0.2 structure had only “fast” sites with a rate constant of 1.9 s−1. These results show that homogeneous dispersion of metals in a bimetallic catalyst is essential to maximizing the number of “fast” active sites, while high temperature synthesis is likely to result in segregation of the metals. Furthermore, the imaging and approach curve modes of SECM were used to investigate the edge sites of the crystalline-derived Ni0.8:Fe0.2 for the OER. These experimental results, coupled with theoretical COMSOL simulations, demonstrate that edge sites of crystalline Ni0.8:Fe0.2 electrocatalysts have a higher OER performance than the surface sites. The other focus of water electrolysis is the HER, where we utilized SI-SECM to examine the surface-adsorbed species that form on Pt(poly) during H2 evolution in alkaline media. Coupling the experimental results with a COMSOL-based kinetic model allowed for us to distinguish between surface-adsorbed underpotential hydrogen (H(UPD)), overpotential hydrogen (H(OPD)), and oxygenated species that form under different applied potentials. We observed that surface-adsorbed oxygenated species form at all potentials investigated and are the strongest adsorbed species. This study also showed that while H(UPD) formed at potentials less negative than the onset of H2 evolution as expected, it is also present at potentials where H2 evolution occurs, suggesting they occupy different active sites than H(OPD) on Pt(poly). At full H2-producing potentials, the oxygenated species and H(UPD) accounted for ∼26% of total sites used, showing that Pt(poly) may not be able to fully utilize all the active sites (∼970 sites nm−2) due to these species forming on the surface during reaction conditions. Furthermore, this technique allowed for further insights into the effect of adsorbed surface coverage on the traditional Tafel analysis, explaining the shift in Tafel slope from 40 mV dec−1 to 120 mV dec−1. This study demonstrates the capability of using SI-SECM to experimentally measure the quantity and strength of different surface-adsorbed intermediates that form during HER in alkaline, providing insights as to why the HER kinetics are more sluggish in alkaline media compared to acidic media. The final dissertation objective was to improve the imaging mode of SECM. Traditional constant height imaging has limitations on rough electrocatalysts, as the tip electrode may crash to drift outside the necessary feedback regime. We demonstrate a new imaging mode of SECM based on real-time analysis of the approach curve to actively control nanoscale tip-substrate distances without contact. To obtain the image, a ∼500 nm diameter Pt tip electrode is approached to ∼50 nm from the surface across several points of an insulating substrate. Fitting each experimental curve with theoretical equations allows for accurate topography measurements and provides the groundwork for determining the reactivity of the substrate simultaneously

Deconvoluting Kinetic Rate Constants of Catalytic Substrates from Scanning Electrochemical Approach Curves with Artificial Neural Networks

Extracting information from experimental measurements in the chemical sciences typically requires curve fitting, deconvolution, and/or solving the governing partial differential equations via numerical (e.g., finite element analysis) or analytical methods. However, using numerical or analytical methods for high-throughput data analysis typically requires significant postprocessing efforts. Here, we show that deep learning artificial neural networks can be a very effective tool for extracting information from experimental data. As an example, reactivity and topography information from scanning electrochemical microscopy (SECM) approach curves are highly convoluted. This study utilized multilayer perceptrons and convolutional neural networks trained on simulated SECM data to extract kinetic rate constants of catalytic substrates. Our key findings were that multilayer perceptron models performed very well when the experimental data were close to the ideal conditions with which the model was trained. However, convolutional neural networks, which analyze images as opposed to direct data, were able to accurately predict the kinetic rate constant of Fe-doped nickel (oxy)hydroxide catalyst at different applied potentials even though the experimental approach curves were not ideal. Due to the speed at which machine learning models can analyze data, we believe this study shows that artificial neural networks could become powerful tools in high-throughput data analysis

Insights into the Active Electrocatalytic Areas of Layered Double Hydroxide and Amorphous Nickel–Iron Oxide Oxygen Evolution Electrocatalysts

Layered double hydroxide (LDH) and amorphous nickel–iron (oxy)­hydroxides (Ni_1–<i>x</i>Fe_<i>x</i>OOH) are emerging catalysts for the electrochemical oxygen evolution reaction (OER). It is still unresolved if the layered two-dimensional (2D) structure allows for active catalytic sites to exist below the traditional electrode/electrolyte interface. Herein, we utilized the surface interrogation mode of scanning electrochemical microscopy (SI-SECM) to directly measure active site densities <i>in situ</i>. We determined that Ni_0.8­Fe_0.2OOH LDH showed a 10-fold increase in the active site density compared to rock salt Ni_0.8:Fe_0.2 oxide, giving direct evidence that water and hydroxide in the interlayer are able to create stable Ni^IV/Fe^IV active species at layers below the electrode/electrolyte interface. This result suggests that electrolyte permeability of the 2D LDH structure is a major contributor for its increased catalytic activity. Amorphous Ni_0.8:Fe_0.2 oxide also exhibits an anomalously high active site density and higher activity than Ni_0.8Fe_0.2OOH LDH