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.

Simultaneous Intelligent Imaging of Nanoscale Reactivity and Topography by Scanning Electrochemical Microscopy.

Scanning electrochemical microscopy (SECM) enables reactivity and topography imaging of single nanostructures in the electrolyte solution. The in situ reactivity and topography, however, are convoluted in the real-time image, thus requiring another imaging method for subsequent deconvolution. Herein, we develop an intelligent mode of nanoscale SECM to simultaneously obtain separate reactivity and topography images of non-flat substrates with reactive and inert regions. Specifically, an ∼0.5 µm-diameter Pt tip approaches a substrate with an ∼0.15 µm-height active Au band adjacent to an ∼0.4 µm-wide slope of the inactive glass surface followed by a flat inactive glass region. The amperometric tip current versus tip-substrate distance is measured to observe feedback effects including redox-mediated electron tunneling from the substrate. The intelligent SECM software automatically terminates the tip approach depending on the local reactivity and topography of the substrate under the tip. The resultant short tip-substrate distances allow for non-contact and high-resolution imaging in contrast to other imaging modes based on approach curves. The numerical post-analysis of each approach curve locates the substrate under the tip for quantitative topography imaging and determines the tip current at a constant distance for topography-independent reactivity imaging. The nanoscale grooves are revealed by intelligent topography SECM imaging as compared to scanning electron microscopy and atomic force microscopy without reactivity information and as unnoticed by constant-height SECM imaging owing to the convolution of topography with reactivity. Additionally, intelligent reactivity imaging traces abrupt changes in the constant-distance tip current across the Au/glass boundary, which prevents constant-current SECM imaging.

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

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 nonflat 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 the 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 an experimental approach curve to ensure the lack of electrochemical reactivity. The tip approach to the 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 the 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 damage of the fragile nanotip.

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

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.