The effects of applied stress, ranging from tensile to compressive, on the atmospheric pitting corrosion behavior of 304L stainless steel (SS304L) were analyzed through accelerated atmospheric laboratory exposures and microelectrochemical cell analysis. After exposing the lateral surface of a SS304L four-point bend specimen to artificial seawater at 50°C and 35% relative humidity for 50 d, pitting characteristics were determined using optical profilometry and scanning electron microscopy. The SS304L microstructure was analyzed using electron backscatter diffraction. Additionally, localized electrochemical measurements were performed on a similar, unexposed, SS304L four-point bend bar to determine the effects of applied stress on corrosion susceptibility. Under the applied loads and the environment tested, the observed pitting characteristics showed no correlation with the applied stress (from 250 MPa to -250 MPa). Pitting depth, surface area, roundness, and distribution were found to be independent of location on the sample or applied stress. The lack of correlation between pitting statistics and applied stress was more likely due to the aggressive exposure environment, with a sea salt loading of 4 g/m2 chloride. The pitting characteristics observed were instead governed by the available cathode current and salt distribution, which are a function of sea salt loading, as well as pre-existing underlying microstructure. In microelectrochemical cell experiments performed in Cl- environments comparable to the atmospheric exposure and in environments containing orders of magnitude lower Cl- concentrations, effects of the applied stress on corrosion susceptibility were only apparent in open-circuit potential in low Cl- concentration solutions. Cl- concentration governed the current density and transpassive dissolution potential.
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.
Different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.
Additive manufacturing of mixed potential electrochemical sensors opens the possibility to perform rapid prototyping of electrode and electrolyte materials. We report for the first time the use of this technique for the fabrication of solid-state electrochemical gas sensors of the mixed potential type and assessment of variability in the manufacturing process. La0.87Sr0.13CrO3 (LSCO) and Pt electrodes bridged with a porous yttria-stabilized zirconia (YSZ) have been deposited on YSZ substrates by direct-write extrusion of pastes and inks. The sensors are evaluated for their sensitivity to 200 ppm of NOx, C3H8, and NH3. There is a need to understand how variations in intrinsic materials parameters during manufacturing such as differences in porosity affect the gas sensing of additively manufactured sensors to guide optimization of their performance and serve as quality control techniques. Further characterizations of these devices include electrochemical impedance spectroscopy and an aqueous electrochemical assessment of the electrode surface area and diffusion through the porous layer. We find a correlation of increased sensitivity with larger gas reaction impedance, higher Pt electrode surface area, and slower diffusion.
In situ Raman microscopy was used to study polysulfide speciation in the bulk ether electrolyte during the discharge and charge of a Li-S electrochemical cell to assess the complex interplay between chemical and electrochemical reactions in solution. During discharge, long chain polysulfides and the S3- radical appear in the electrolyte at 2.4 V indicating a rapid equilibrium of the dissociation reaction to form S3-. When charging, however, an increase in the concentration of all polysulfide species was observed. This highlights the importance of the electrolyte to sulfur ratio and suggests a loss in the useful sulfur inventory from the cathode to the electrolyte.
In situ Raman microscopy was used to study polysulfide speciation in the bulk ether electrolyte during the discharge and charge of a Li-S electrochemical cell to assess the complex interplay between chemical and electrochemical reactions in solution. During discharge, long chain polysulfides and the S3− radical appear in the electrolyte at 2.4 V indicating a rapid equilibrium of the dissociation reaction to form S3−. When charging, however, an increase in the concentration of all polysulfide species was observed. This highlights the importance of the electrolyte to sulfur ratio and suggests a loss in the useful sulfur inventory from the cathode to the electrolyte.
The development of on-board sensors for emissions monitoring is necessary for continuous monitoring of the performance of catalytic systems in automobiles. We have fabricated mixed potential electrochemical gas sensing devices with Pt, La0.8Sr0.2CrO3 (LSCO), and Au/Pd alloy electrodes and a porous yttria-stabilized zirconia electrolyte. The three-electrode design takes advantage of the preferential selectivity of the Pt + Au/Pd and Pt + LSCO pairs towards different species of gases and has additional tunable selectivity achieved by applying a current bias to the latter pair. Voltages were recorded in single, binary, and ternary gas streams of NO, NO2, C3H8, and CO. We have trained artificial neural networks to examine the voltage output from sensors in biased and unbiased modes to both identify which single test gas or binary mixture of two test gases is present in a gas stream as well as extract concentration values. We are able to identify single and binary mixtures of these gases with accuracy of at least 98%. For determining concentration, the peak in the error distribution for binary mixtures was 5% and 80% of test data fell under <12% error. The sensor stability was also evaluated over the course of over 100 days and the ability to retrain ANNs with a small dataset was demonstrated.
Solid-state zirconia sensors are used in a wide variety of applications including controlling the air to fuel ratio in combustion engines and pollution monitoring. These sensors use either a layer of zirconia as a solid-state ionic electrolyte or a gasporous ceramic as a protective layer. There is a need for quantitative methods to assess the tortuosity of these porous layers and the size of the electrode area exposed which can be performed on completed sensor devices. We demonstrate using electrochemical double layer capacitance and transport studies in aqueous potassium ferri/ferro-cyanide electrolytes that these parameters can be readily measured. The technique is demonstrated on sensors procured from ESL ElectroScience as well as sensors produced in-house using additive manufacturing. The processes that we develop can be applied as quality control to ensure sample-to-sample reproducibility of the porous layer.
A major goal for sensors in the automotive exhaust emissions control and monitoring field is the development of a single sensor to monitor NOx, NH3, CO, and hydrocarbons. Mixed potential electrochemical sensors can be readily tuned to be selective towards different gas mixtures by changing the composition of their electrode or applying a voltage or current bias. We have developed a three-electrode sensor comprised of Pt, La0.8Sr0.2CrO3 (LSCO), and Au0.5Pd0.5 electrodes with a porous yttria-stabilized zirconia (YSZ) electrolyte which can detect single and binary gas mixtures of NO, C3H8, and CO. The sensitivity of the electrode pairs was investigated and it was found that Au/Pd+Pt was most sensitive to CO while LSCO+Pt is best at detecting C3H8. From the sensor responses, we have trained artificial neural networks to use the voltage responses to recover the gas concentrations and classify the gas mixtures.