N
Nathan S. Lewis
Researcher at California Institute of Technology
Publications - 730
Citations - 72550
Nathan S. Lewis is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Semiconductor & Silicon. The author has an hindex of 112, co-authored 720 publications receiving 64808 citations. Previous affiliations of Nathan S. Lewis include Lawrence Berkeley National Laboratory & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
Response versus Chain Length of Alkanethiol-Capped Au Nanoparticle Chemiresistive Chemical Vapor Sensors
Edgardo García-Berríos,Ting Gao,Marc D. Woodka,Stephen Maldonado,Bruce S. Brunschwig,Mark W. Ellsworth,Nathan S. Lewis +6 more
TL;DR: In this article, a homologous series of straight chain alkanethiols (containing 4−11 carbons in length) were used as chemiresistive organic vapor sensors.
Journal ArticleDOI
Measurement of the Dependence of Interfacial Charge-Transfer Rate Constants on the Reorganization Energy of Redox Species at n-ZnO/H2O Interfaces
TL;DR: Results show that interfacial electron-transfer rate constants at semiconductor electrodes are in good agreement with the predictions of a Marcus-type model of interfacial electrons-transfer reactions.
Journal ArticleDOI
Vapor Sensing Characteristics of Nanoelectromechanical Chemical Sensors Functionalized Using Surface-Initiated Polymerization
TL;DR: Surface-initiated polymerization can provide a straightforward, reproducible method for large-scale functionalization of nanosensors.
Journal ArticleDOI
Current Density versus Potential Characteristics of Dye-Sensitized Nanostructured Semiconductor Photoelectrodes. 2. Simulations
TL;DR: In this paper, the impact of changes in various parameters on the steady-state current density−potential (J−E) characteristics of dye-sensitized nanostructured semiconductor photoelectrodes has been evaluated through a series of simulations.
Patent
Method of resolving analytes in a fluid
TL;DR: In this article, a statistical metric based on the magnitude and standard deviations along linear projections of clustered array response data is utilized to facilitate an evaluation of the performance of detector arrays in various vapor classification tasks.