The fully bottom-up and scalable synthesis of complex micro/ nanoscale materials and functional devices requires masking methods to define key features and direct the deposition of various coatings and films.Here, we demonstrate selective coaxial lithography via etching of surfaces (SCALES), an enabling bottom-up process to add polymer masks to micro/ nanoscale objects. SCALES is a three-step process, including (1) bottom-up synthesis of compositionally modulated structures, (2) surface-initiated polymerization of a conformal mask, and (3) selective removal of the mask only from regions whose underlying surface is susceptible to an etchant. We demonstrate the key features of and characterize the SCALES process with a series of model Si/Ge systems: Si and Ge wafers, Si and Ge nanowires, and Si/Ge heterostructure nanowires.
The thermodynamic activity of a reacting species, rather than the concentration of that species, generally determines the rate of a kinetically-limited reaction. In this work we demonstrate the need for the explicit accounting of reacting species’ thermodynamic activities in solution, especially when conducting electrochemical kinetic tests. In hydrogen evolution in an alkaline acetonitrile-water blended electrolyte as well as previously-reported oxygen-atom transfer reactions (cyclooctene epoxidation and cyclohexanone lactonization), we demonstrate that accounting for species thermodynamic activity causes water-dependence measurements to yield different mechanistic interpretations than data which treats concentration as a proxy for activity. We hypothesize many ways in which water contributes to the reaction rate beyond direct participation in the reaction, offer initial molecular interpretations of the water activity-concentration relationship in the blended electrolyte, and discuss implications of these findings for better understanding solvent effects.
Redox flow batteries (RFBs) are hindered by complex failure modes, particularly crossover through the membrane, resulting in capacity fade and reduced cycling efficiencies. Redox‐active oligomers (RAOs) have recently been proposed for mitigating this phenomenon while maintaining sufficient transport properties; however, to date, few studies have quantified how the chemical and electrochemical properties of RAOs influence their performance in redox flow cells. Here, we demonstrate that oligomeric derivatives of 2,2,6,6‐tetramethylpiperidine 1‐oxyl (TEMPO) exhibit lower diffusivities than the monomeric species but retain facile charge transfer characteristics. The size‐dependent variations in mass transport rates directly translate to differences in flow cell polarization and symmetric cycling performance. Post‐mortem analyses reveal that oligomerization does not meaningfully alter decay processes as evinced by similar capacity fade across all species. Broadly, these findings corroborate and extend upon previously developed relationships between molecular size, electrochemical properties, and flow cell performance.
One of the main challenges in autonomous racing is to design algorithms for motion planning at high speed, and across complex racing courses. End-to-end trajectory synthesis has been previously proposed where the trajectory for the ego vehicle is computed based on camera images from the racecar. This is done in a supervised learning setting using behavioral cloning techniques. In this paper, we address the limitations of behavioral cloning methods for trajectory synthesis by introducing Differential Bayesian Filtering (DBF), which uses probabilistic Bézier curves as a basis for inferring optimal autonomous racing trajectories based on Bayesian inference. We introduce a trajectory sampling mechanism and combine it with a filtering process which is able to push the car to its physical driving limits. The performance of DBF is evaluated on the DeepRacing Formula One simulation environment and compared with several other trajectory synthesis approaches as well as human driving performance. DBF achieves the fastest lap time, and the fastest speed, by pushing the racecar closer to its limits of control while always remaining inside track bounds.
Imprecise and incomplete specification of system configurations threatens safety, security, functionality, and other critical system properties and uselessly enlarges the configuration spaces to be searched by configuration engineers and auto-tuners. To address these problems, this paper introduces interpreted formalisms based on real-world types for configurations. Configuration values are lifted to values of real-world types, which we formalize as subset types in Coq. Values of these types are dependent pairs whose components are values of underlying Coq types and proofs of additional properties about them. Real-world types both extend and further constrain machine-level configurations, enabling richer, proof-based checking of their consistency with real-world constraints. Tactic-based proof scripts are written once to automate the construction of proofs, if proofs exist, for configuration fields and whole configurations. Failures to prove reveal real-world type errors. Evaluation is based on a case study of combinatorial optimization of Hadoop performance by meta-heuristic search over Hadoop configurations spaces.
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